Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

A Review of the Transformation of Road Transport Systems: Are We Ready for the Next Step in Artificially Intelligent Sustainable Transport?

A Review of the Transformation of Road Transport Systems: Are We Ready for the Next Step in... Review A Review of the Transformation of Road Transport Systems: Are We Ready for the Next Step in Artificially Intelligent Sustainable Transport? 1 , 1 2 Umair Hasan * , Andrew Whyte and Hamad Al Jassmi School of Civil and Mechanical Engineering, Curtin University, Perth, WA 6845, Australia; andrew.whyte@curtin.edu.au Director of Roadway, Transportation and Trac Safety Research Centre (RTTSRC), United Arab Emirates University, Al Ain, UAE; h.aljasmi@uaeu.ac.ae * Correspondence: umair.hasan@postgrad.curtin.edu.au; Tel.: +61-46-716-2000 Received: 19 November 2019; Accepted: 18 December 2019; Published: 19 December 2019 Abstract: Mobility is experiencing a revolution, as advanced communications, computers with big data capacities, ecient networks of sensors, and signals, are developing value-added applications such as intelligent spaces and autonomous vehicles. Another new technology that is both promising and might even be pervasive for faster, safer and more environmentally-friendly public transport (PT) is the development of autonomous vehicles (AVs). This study aims to understand the state of the current research on the artificially intelligent transportation system (ITS) and AVs through a critical evaluation of peer-reviewed literature. This study’s findings revealed that the majority of existing research (around 82% of studies) focused on AVs. Results show that AVs can potentially reduce more than 80% of pollutant emissions per mile if powered by alternate energy resources (e.g., natural gas, biofuel, electricity, hydrogen cells, etc.). Not only can private vehicle ownership be cut down by bringing in ridesharing but the average vehicle miles travelled (VMT) should also be reduced through improved PT. The main benefits of AV adoption were reported in the literature to be travel time, trac congestion, cost and environmental factors. Findings revealed barriers such as technological uncertainties, lack of regulation, unawareness among stakeholders and privacy and security concerns, along with the fact that lack of simulation and empirical modelling data from pilot studies limit the application. AV–PT was also found to be the most sustainable strategy in dense urban areas to shift the heavy trip load from private vehicles. Keywords: autonomous cars; urban mobility; rapid bus transport; greenhouse gas (GHG) emissions; energy conservation 1. Introduction The buzz around autonomous mobility, intelligent transportation system and smart cities has propelled the ventures of personal and public transit into new domains. In its early purview, concept of autonomous vehicles was integrated with urban bus transit in the form of the United States Department of Transportation (US DoT)’s Vehicle Assist and Automation (VAA) programme (tested with 1 m spaced permanent magnetic markers over a 3-mile route in a maintenance yard), and California’s PATH, a completely automated highway bus fleet in 2003. While the VAA tests on buses were performed in 2014 by operating an equipped bus for 6–8 h/day, completely autonomous bus transit operations have yet to be developed, as most current systems require drivers with the partially automated speed or lane controls [1]. At present, automated vehicles are categorised by the National Highway Trac Safety Administration (NHTSA) [2–4] according to their automation functionality into five levels of increasing automation (ranging from zero automation to a completely unmanned autonomous vehicle). Appl. Syst. Innov. 2020, 3, 1; doi:10.3390/asi3010001 www.mdpi.com/journal/asi Appl. Syst. Innov. 2020, 3, 1 2 of 21 Appl. Syst. Innov. 2019, 2, x FOR PEER REVIEW 2 of 20 For example, the VAA programme demonstrated a Level 2 in the US state of Oregon, that is, For example, the VAA programme demonstrated a Level 2 in the US state of Oregon, that is, precision docking and lane adherence by the steering automation, while with the manned braking precision docking and lane adherence by the steering automation, while with the manned braking and throttling of on-transit bus. Figure 1, prepared after Liu [5], covers the state of research on the and throttling of on-transit bus. Figure 1, prepared after Liu [5], covers the state of research on the autonomous transportations systems in urban areas around the world based upon the vehicular size, autonomous transportations systems in urban areas around the world based upon the vehicular size, capacity and operational features. capacity and operational features. Figure 1. Mode classification of autonomous urban transportation (based on Liu [5]). Figure 1. Mode classification of autonomous urban transportation (based on Liu [5]). On the note of autonomous buses, CityMobil2 is an innovative PT project, tested at the University On the note of autonomous buses, CityMobil2 is an innovative PT project, tested at the La Sapienza in Rome to investigate the feasibility of autonomous public transit by studying the shared University La Sapienza in Rome to investigate the feasibility of autonomous public transit by and on-demand mobility on an automated guideway transit, road user interactions and bus user studying the shared and on-demand mobility on an automated guideway transit, road user experiences, where a considerably positive outlook from the public (See Section 2.3) was observed. interactions and bus user experiences, where a considerably positive outlook from the public (See Automated guideway transit was defined by Liu [6] and Liu et al. [1] as a mode of urban transportation Section 2.3) was observed. Automated guideway transit was defined by Liu [6] and Liu et al. [1] as a where dedicated tracks are used for the operation of completely autonomous vehicles. In the case of mode of urban transportation where dedicated tracks are used for the operation of completely autonomous personal transportation, companies such as Volvo and Nissan have expressed interest autonomous vehicles. In the case of autonomous personal transportation, companies such as Volvo in commercially marketing autonomous vehicles by the year 2020 [7,8]. While according to Google and Nissan have expressed interest in commercially marketing autonomous vehicles by the year 2020 co-founder Sergey Brin, autonomous vehicles may be a reality on U.S roads within the next five years [9] [7,8]. While according to Google co-founder Sergey Brin, autonomous vehicles may be a reality on or so. Researchers such as Wang [10] and Fagnant and Kockelman [11] proposed that the operation of U.S roads within the next five years [9] or so. Researchers such as Wang [10] and Fagnant and autonomous personal cars may result in safer and less congested travel. As already evidenced by the Kockelman [11] proposed that the operation of autonomous personal cars may result in safer and less increased fuel savings, roadway capacity, accident aversion and lower emissions are now due to the congested travel. As already evidenced by the increased fuel savings, roadway capacity, accident introduction of some level of automation (e.g., assisted parking and adaptive cruise control) in newer aversion and lower emissions are now due to the introduction of some level of automation (e.g., cars. Apart from fully automated cars such as Massachusetts Institute of Technology (MIT)’s fully assisted parking and adaptive cruise control) in newer cars. Apart from fully automated cars such as autonomous Land Rover LR3 Talos [12], shared autonomous vehicles (SAVs) have also been researched Massachusetts Institute of Technology (MIT)’s fully autonomous Land Rover LR3 Talos [12], shared as an extension of the emerging e-hail or car-sharing [13]. Fagnant and Kockelman [14] simulated autonomous vehicles (SAVs) have also been researched as an extension of the emerging e-hail or car- the behaviour of these SAVs and observed that technological barriers of completely operational and sharing [13]. Fagnant and Kockelman [14] simulated the behaviour of these SAVs and observed that a ordable autonomous vehicles, as well as regulations regarding commercial automated taxi service technological barriers of completely operational and affordable autonomous vehicles, as well as licensing, are still to be drafted. regulations regarding commercial automated taxi service licensing, are still to be drafted. In the domain of on-demand mobility, Tang et al. [15] have respectively expressed the ability of an In the domain of on-demand mobility, Tang et al. [15] have respectively expressed the ability of overall ITS in improving user behaviour, air quality, travel time, productivity and safety, propelled an overall ITS in improving user behaviour, air quality, travel time, productivity and safety, by revolutions in the fields of electronics, robotics, information technology and cybernetics. Due to propelled by revolutions in the fields of electronics, robotics, information technology and cybernetics. logical and temporal constraints, accurate recognition and distinction between objects and humans on Due to logical and temporal constraints, accurate recognition and distinction between objects and a roadway are most critical for autonomous vehicles on urban roads, followed by complex manoeuvre, humans on a roadway are most critical for autonomous vehicles on urban roads, followed by complex m blocked anoeuvrroutes, e, blocked congested routes, conge trac s operations ted traffic op and erat obeying ions and ob tra ey cin rules g traf [f 16 ic], ru as les [ well 16], as as we the ll overall as the overall sustainability sustainability o of commissioning f commission AVsing from AVs public frominvestments. public investments. The purpose of the current paper is to perform a critical review of peer-reviewed research in the The purpose of the current paper is to perform a critical review of peer-reviewed research in field of vehicular automation, so as to map the status of autonomous urban transportation, and the field of vehicular automation, so as to map the status of autonomous urban transportation, and specifically to ask the question of how driverless vehicles are changing the way we think of artificially intelligent transportation systems. Appl. Syst. Innov. 2020, 3, 1 3 of 21 specifically to ask the question of how driverless vehicles are changing the way we think of artificially Appl. Syst. Innov. 2019, 2, x FOR PEER REVIEW 3 of 20 intelligent transportation systems. The question of the smart cities of the future and technological transformation of urban roadway The question of the smart cities of the future and technological transformation of urban roadway systems is also closely linked with the triple-bottom-line sustainability approach: cost, environment systems is also closely linked with the triple-bottom-line sustainability approach: cost, environment and social interactions [17]. This current study is intended to serve as a platform for a pilot study on and social interactions [17]. This current study is intended to serve as a platform for a pilot study on the exploration of artificially intelligent sustainable urban transport with the city of Abu Dhabi as a the exploration of artificially intelligent sustainable urban transport with the city of Abu Dhabi as a case study. Our keywords: autonomous cars, sustainable transport, smart cities, intelligent urban case study. Our keywords: autonomous cars, sustainable transport, smart cities, intelligent urban transportation and ITS, were entered, and the 22 primary and 79 supplementary studies returned from transportation and ITS, were entered, and the 22 primary and 79 supplementary studies returned Google Scholar and ScienceDirect were analysed, and are described in the following sections. from Google Scholar and ScienceDirect were analysed, and are described in the following sections. 2. Dimensions of Artificially Intelligent Transport Systems 2. Dimensions of Artificially Intelligent Transport Systems This section discusses the primary dimensions of artificially intelligent transport systems (Figure 2) This section discusses the primary dimensions of artificially intelligent transport systems (Figure across the existing literature, as: on-demand mobility, shared autonomous vehicles, autonomous public 2) across the existing literature, as: on-demand mobility, shared autonomous vehicles, autonomous bus transport, lane control and vehicular guidance systems. The benefits of smart technologies and public bus transport, lane control and vehicular guidance systems. The benefits of smart technologies artificially intelligent transport systems for travel time reduction and easing congestions, along with and artificially intelligent transport systems for travel time reduction and easing congestions, along incident management, are also discussed in this section. A general finding was that the majority of with incident management, are also discussed in this section. A general finding was that the majority existing studies on the performance evaluation of such systems largely focused on programming of existing studies on the performance evaluation of such systems largely focused on programming and technological aspects, public law and transport law and profit generation. Although demand and technological aspects, public law and transport law and profit generation. Although demand management was also extensively studied in recent literature, little attention is paid to the long-term management was also extensively studied in recent literature, little attention is paid to the long-term impact of implementing artificially intelligent transport systems and the resulting travel pattern impact of implementing artificially intelligent transport systems and the resulting travel pattern variations, particularly in the context of sustainability performance evaluations. variations, particularly in the context of sustainability performance evaluations. Figure 2. Dimensions of a connected artificially intelligent transport system: on-demand mobility, Figure 2. Dimensions of a connected artificially intelligent transport system: on-demand mobility, smart city-spaces, users, PT and vehicle guidance systems. smart city-spaces, users, PT and vehicle guidance systems. 2.1. On-Demand Mobility Solutions 2.1. On-Demand Mobility Solutions The advances in on-demand mobility have increased their popularity before the introduction The advances in on-demand mobility have increased their popularity before the introduction of of automation (e.g., Uber, Lyft, Hailo etc.). These programmes have been aimed at replacement of automation (e.g., Uber, Lyft, Hailo etc.). These programmes have been aimed at replacement of private automobile trips from vehicle ownership to a rental service that is run on-demand. Fagnant private automobile trips from vehicle ownership to a rental service that is run on-demand. Fagnant and Kockelman [14] have commented on the increasing popularity of these services, for example, the users in the United States double every year or two. Greenblatt and Shaheen [13], in their study reviewing automated, on-demand mobility and the associated environmental impacts, found that around eighty percent or more greenhouse gas (GHG) Appl. Syst. Innov. 2020, 3, 1 4 of 21 and Kockelman [14] have commented on the increasing popularity of these services, for example, the users in the United States double every year or two. Greenblatt and Shaheen [13], in their study reviewing automated, on-demand mobility and the Appl. Syst. Innov. 2019, 2, x FOR PEER REVIEW 4 of 20 associated environmental impacts, found that around eighty percent or more greenhouse gas (GHG) emissions and energy use reduction may be observed with the use of autonomous vehicles. They also emissions and energy use reduction may be observed with the use of autonomous vehicles. They also proposed that social benefits (lower VMTs and ownership reduction), environmental load suppression, proposed that social benefits (lower VMTs and ownership reduction), environmental load land-use and mass transit benefits may be achievable through on-demand mobility. Adding AVs suppression, land-use and mass transit benefits may be achievable through on-demand mobility. may result in further reduction as smaller and more ecient SAVs become a reality, as these vehicles Adding AVs may result in further reduction as smaller and more efficient SAVs become a reality, as may result in overcoming the ride-sharing obstacles of users travelling to a reserved spot to access these vehicles may result in overcoming the ride-sharing obstacles of users travelling to a reserved rental cars. spot to access rental cars. 2.2. Shared Autonomous Mobility Solutions 2.2. Shared Autonomous Mobility Solutions Preceded by existing, commercial, car-sharing rental services (e.g., ZipCar and Car2Go), probably Preceded by existing, commercial, car-sharing rental services (e.g., ZipCar and Car2Go), one of the most widely researched applications of autonomous vehicles as a solution to urban transit probably one of the most widely researched applications of autonomous vehicles as a solution to problems is the SAV. The concept of shared car mobility came from the relatively low ratio between urban transit problems is the SAV. The concept of shared car mobility came from the relatively low the number of private vehicles owned and the actual number of vehicles in use during a typical day. ratio between the number of private vehicles owned and the actual number of vehicles in use during a t Santos ypica et l d al. ay[.2 Sant ] suggested os et al. [2 that ] su appr ggest oximately ed that ap 10% proxi ofm vehicles ately 10% o (models f vehicl > 10 es (m years) odels ar> 1 e on 0 y road ears)on are a o repr n ro esentative ad on a reday pre,swhich entative became day, w slightly hich behigher came s(17%) lightlywhen highe newer r (17%v ) w ehicles hen n (models ewer ve hic 10 les years) (modwer els ≤ e 10 years) were considered. Buying more cars is not the mobility solution for the present trip demands considered. Buying more cars is not the mobility solution for the present trip demands in most areas, in most areas, and a transitioning towards a subscribed or pay-as-you-go vehicle in suitable areas and a transitioning towards a subscribed or pay-as-you-go vehicle in suitable areas may have more may have more economical and environmental viability as the vehicle kilometres travelled are economical and environmental viability as the vehicle kilometres travelled are reduced. Shaheen and reduced. Shaheen and Cohen [18] found that the ride-sharing users increased to be 890,000 in the U.S. Cohen [18] found that the ride-sharing users increased to be 890,000 in the U.S. by 2013 from 12,000 in by 2013 from 12,000 in 2002. A follow-up survey by Shaheen and Cohen [19] found that around 2002. A follow-up survey by Shaheen and Cohen [19] found that around 104,000 shared vehicles are 104,000 shared vehicles are currently serving 4.8 million users across 1531 cities globally, with the currently serving 4.8 million users across 1531 cities globally, with the fleet distribution in Figure 3, and fleet distribution in Figure 3, and with Mexico and Italy accounting for the highest (131:1) and second with Mexico and Italy accounting for the highest (131:1) and second highest (107:1) member-vehicle highest (107:1) member-vehicle ratios, respectively. ratios, respectively. 60% 50% 40% 30% 20% 10% 0% Europe North America Others Carsharing Membership Vehicle Deployment Figure 3. Percentile carsharing membership and vehicle deployment distribution of shared Figure 3. Percentile carsharing membership and vehicle deployment distribution of shared vehicle vehicle services. services. To the end of on-demand, autonomous mobility or aTaxis, as dubbed by Kornhauser [20] To the end of on-demand, autonomous mobility or aTaxis, as dubbed by Kornhauser [20] complementing passenger transit in urban centres, a number of research works have been commissioned. complementing passenger transit in urban centres, a number of research works have been For example, the replacement of private cars in metropolitan areas by SAV taxi services was analysed commissioned. For example, the replacement of private cars in metropolitan areas by SAV taxi by Ford [21] and Kornhauser [20], where passengers were required to travel to taxi stations, similar services was analysed by Ford [21] and Kornhauser [20], where passengers were required to travel to to the Car2Go services. Rigole [22] in the metropolitan Stockholm region, focused on developing taxi stations, similar to the Car2Go services. Rigole [22] in the metropolitan Stockholm region, focused a framework to assist in dynamically allocating SAVs. This was achieved through the use of the on developing a framework to assist in dynamically allocating SAVs. This was achieved through the use of the present roadway network depiction, private commute-demand and multi-criteria evaluation (i.e., fleet size, travelling and waiting times), along with rerouting vacant vehicles based on varying values of these variables. The life-cycle analysis was performed to evaluate the environmental burdens of both traditional (diesel and petrol) and electric cars. Their results showed that by only utilising under 10% parking spots and private vehicles, an on-demand personal SAV transportation system can provide highly Percentile distribution (carsharing growth) Appl. Syst. Innov. 2020, 3, 1 5 of 21 present roadway network depiction, private commute-demand and multi-criteria evaluation (i.e., fleet size, travelling and waiting times), along with rerouting vacant vehicles based on varying values of these variables. The life-cycle analysis was performed to evaluate the environmental burdens of both traditional (diesel and petrol) and electric cars. Their results showed that by only utilising under 10% parking spots and private vehicles, an on-demand personal SAV transportation system can provide highly ecient “door-to-door” services. However, the results of the study are exceedingly hampered by the decision of users to accept ride-sharing. On average around a 15% increase in passenger travel times and 10 min starting time window was expected along with lower trac congestion and environmental impacts, which greatly increased as ride-sharing was replaced with vacant car drives between trips. Results are compatible with the market need for a shared vehicle which gave rise to projects such as ZipCar, for example, which aims for a “simple and responsible urban living” [23], and is supported through smart cards and Android/iPhone apps for identifying vehicle location. A ordability has been cited [1] as the primary driving force for the popularity of ZipCar with millennials, and especially in densely populated urban areas, with approximately 470 cities supported by 10,000 cars or so. Therefore, the potential application of SAVs on the mobility spectrum is promising. Evaluation of the benefits of on-demand autonomous vehicles was conducted by Fagnant and Kockelman [11] and Fagnant and Kockelman [24]. Approximately 8.7% of the distances travelled by the 56,000 SAVs every day were found to be vacant, reduced to 4.5% by allowing for car-sharing. Three market penetration rates: 10%, 50% and 90%; with VMT per AV 10% higher at 90% and 20% higher at 10% market penetration were used, for representing the introduction of new technology and probably a more accepting public perception over the years. The study used the discount rate and dollar-value of passenger vehicle time based upon the cost data (17 USD/person-hr) and the 2020 trac flow patterns, of Schrank et al. [25], as the baseline. Results illustrated benefits in the form of a reduced demand for parking, fuel savings and lowered congestion levels. Shladover et al. [26] proposed that congestion may fall in freeways by approximately 60% if market penetration is assumed at 90%. The study proposed that a careful evaluation of market penetration and public perception is the key to the ecient operation of SAVs. 2.3. Autonomous Public Bus Transport Personalised rapid transport (PRT) is currently being researched as a sustainable transit solution with the use of electric autonomous single cabin vehicles in a pilot study by the Masdar Institute of Science and Technology, Abu Dhabi [27]. However, the future vision of PT in smart cities is expected to harness the potential promised by automation, as discussed earlier. The initial feedback received from the VAA field tests included lesser stress experienced by drivers and the ability of the steering system to aid the drivers in any complex manoeuvres through narrow rights-of-way. Stakeholder responses from the CityMobil2 project [28] have shown as being overwhelmingly predisposed towards autonomous vehicles, with the exception of Vantaa respondents, operating on dedicated lanes as illustrated in Figure 4 [28]. The potential for automated buses to change the fundamental nature of PT has been promising, but design sensibility and accounting for the social implications are considerably important for planners. Understanding that technological Darwinism has produced an overt emphasis over advanced mobility, projects such as Atkin’s Venturer in Bristol for testing the sensors and communication networks of primarily AV buses, ARUP’s UK Autodrive and the 8 million GBP government and consortium members has led the three years “Innovate UK” urban deliveries project in Greenwich. One of the most notable projects on driverless vehicles is the safer, cleaner and innovative transport being researched by the Transport Research Laboratory’s GATEway (Greenwich Automated Transport Environment) project, which uses 8 to 10 passengers’ capacity vehicles. In all phases of trials, public engagement and feedback were collected to further ascertain the perception of the public regarding autonomous vehicles, further discussed in the perception section of this study. Appl. Syst. Innov. 2019, 2, x FOR PEER REVIEW 5 of 20 efficient “door-to-door” services. However, the results of the study are exceedingly hampered by the decision of users to accept ride-sharing. On average around a 15% increase in passenger travel times and 10 min starting time window was expected along with lower traffic congestion and environmental impacts, which greatly increased as ride-sharing was replaced with vacant car drives between trips. Results are compatible with the market need for a shared vehicle which gave rise to projects such as ZipCar, for example, which aims for a “simple and responsible urban living” [23], and is supported through smart cards and Android/iPhone apps for identifying vehicle location. Affordability has been cited [1] as the primary driving force for the popularity of ZipCar with millennials, and especially in densely populated urban areas, with approximately 470 cities supported by 10,000 cars or so. Therefore, the potential application of SAVs on the mobility spectrum is promising. Evaluation of the benefits of on-demand autonomous vehicles was conducted by Fagnant and Kockelman [11] and Fagnant and Kockelman [24]. Approximately 8.7% of the distances travelled by the 56,000 SAVs every day were found to be vacant, reduced to 4.5% by allowing for car-sharing. Three market penetration rates: 10%, 50% and 90%; with VMT per AV 10% higher at 90% and 20% higher at 10% market penetration were used, for representing the introduction of new technology and probably a more accepting public perception over the years. The study used the discount rate and dollar-value of passenger vehicle time based upon the cost data (17 USD/person-hr) and the 2020 traffic flow patterns, of Schrank et al. [25], as the baseline. Results illustrated benefits in the form of a reduced demand for parking, fuel savings and lowered congestion levels. Shladover et al. [26] proposed that congestion may fall in freeways by approximately 60% if market penetration is assumed at 90%. The study proposed that a careful evaluation of market penetration and public perception is the key to the efficient operation of SAVs. 2.3. Autonomous Public Bus Transport Personalised rapid transport (PRT) is currently being researched as a sustainable transit solution with the use of electric autonomous single cabin vehicles in a pilot study by the Masdar Institute of Science and Technology, Abu Dhabi [27]. However, the future vision of PT in smart cities is expected to harness the potential promised by automation, as discussed earlier. The initial feedback received from the VAA field tests included lesser stress experienced by drivers and the ability of the steering system to aid the drivers in any complex manoeuvres through narrow rights-of-way. Stakeholder responses from the CityMobil2 project [28] have shown as being overwhelmingly predisposed towards autonomous vehicles, with the exception of Vantaa respondents, operating on dedicated Appl. Syst. Innov. 2020, 3, 1 6 of 21 lanes as illustrated in Figure 4 [28]. Trikala Vantaa Lausanne La Rochelle 0% 20% 40% 60% 80% 100% Stakeholder response distribution (%) On dedicated lanes Shared roads (other motorised vehicles) Shared roads (pedestrians and cyclists) Figure 4. Stakeholder response distribution towards autonomous bus mode choices. Figure 4. Stakeholder response distribution towards autonomous bus mode choices. It should be noted here that the large-scale application of AV buses has not been implemented for real-world mobility issues. Nonetheless, AV buses can be integrated with the already-used PT priority schemes [29] to tackle congestion issues. These integrated transport systems (i.e., AV buses equipped with transit prioritisation schemes) may have improved ride comfort and travel time over conventional buses, as also demonstrated in a microsimulation study by Nguyen et al. [30]. This may aid in a largescale acceptance of AV buses by the public due to time savings, low cost and high ride comfort [31]. 2.4. Lane Control, Remote Sensing and Vehicular Guidance System: Vehicle to Infrastructure Communication A common feature of paved roadways around the world is the presence of lane control features, such as painted boundaries, lane markers, road signs, etc. The application of ITS for modernising the roadways and improving lane control, dynamic message signs and remote radio-frequency identification (RFID)-type controllable sensors have been cited by researchers [32,33] as a critical feature of modern cities. Better communication between ITS roadway sensors and AVs may also be observed which can reduce average vehicle mileage, crashes and trac congestions. The big data and machine learning revolution [34] is expected to result in intelligent control, trac pattern recognition, embedded electronic markers (similar to barcodes), cognition of roadway users and adapting according to trac behaviour. According to Wang [35], agent-based controlling of transportation management on an on-demand basis may be a more cost-e ective and reliable alternative to trac control while providing flexibility. Wang [10] commented that instead of traditional reliance on trac lights, cooperative intelligent-space technology may be implemented at intersections over ad-hoc networks. Sustainable, ITS-controlled transportation systems may also account for the social and climatic variations [36,37] of automated transit gateways through a comprehensive agent-based model. At present, there are many sensor-based, vehicular guidance systems available for partially automated assistance in navigation, parking assistance, crash aversion and warnings in lane departures, such as the adaptive cruise control (ACC). Shladover et al. [26] estimated that the e ective capacities of roadway lanes may be increased by up to 80% (for a 90% market penetration of ACC), depending upon market shares. KPMG and the Center for Automotive Research [38] found that improvements in trac congestion not only depend upon automated driving, but also ecient roadway sensors providing active in/out vehicle-environment sensing. For instance, vehicle to the roadway (e.g., trac signals) and vehicle to vehicle communications to increase system performance and improve operational safety. Dey et al. [39] note that reliable, ecient and e ective wireless communication between connected vehicles (i.e., vehicles using vehicular guidance system for Level 1–2 automation) or autonomous vehicles and the appropriate infrastructure, is essential for modern transportations Appl. Syst. Innov. 2020, 3, 1 7 of 21 systems. The authors [39] also highlighted the need for a safe, sustainable and secure wireless network for a large-scale operation to tackle mobility issues for real-world applications. Future research on vehicle guidance and vehicle-to-infrastructure communication for ITS application in transportation should acknowledge the safety-related issues as also noted by Ndashimye et al. [40]. Nonetheless, public investments should be directed accordingly at exploring all dimensions of the transport system’s life-cycle, including social factors, environmental, mobility and safety features. 2.5. Trac Congestion and Travel Time Reduction So far, it has been discussed that the majority of travel time and environmental burdens to users on a roadway network come from the supported trac [41], and a transit system may considerably reduce the emissions [42]. The cost of traveller time delays has been found by Rister and Graves [43] as a significant contributor to the total cost of the operation of a roadway. Gupta et al. [44] and Litman [45] have proposed that if the demand–management strategies are not carefully developed, the miles travelled by roadway users are expected to greatly increase. Trac flow patterns are expected to fluctuate if e ective vehicle–environment communication and autonomous vehicles are to become a reality on future smart cities. Research on connected automated vehicles (CAVs), has been proposed in the 2015 Automated Vehicles Symposium [46] to heavily rely on their impact on network routing, trac control and bottleneck capacity recognition. Utilisation of adaptive cruise control and ITS-based monitoring of trac flow have been reported by Atiyeh [47] to increase trac speeds (8–13%) and fuel savings (23–39%). Similar to the agent-based model for ITS roadway systems, described earlier, another study focusing on the design of an agent-based model for SAVs was conducted by Fagnant and Kockelman [14]. They studied the environmental benefits of SAVs comparative to the traditional privately-owned vehicles by mimicking trips based on real-time travelling profiles, departure and destination times and origin and destinations in a grid-based, urban setting. The case study model, run over 100 days, was equipped with the relocation of unused vehicles (which was observed for some vehicles when the model was run), varying fleet size, and service and trip generation distribution and congestion levels. Relocation of SAVs may result in 11% higher travelling as vacant vehicles move to towards the subsequent travel, showing dependence on user concentration, but may replace 10 times of privately owned vehicles. Nonetheless, the model predicted that the block-to-block relocation strategy resulted in more benefits compared to zonal relocation. It may be more suitable for reducing trac congestion if a dynamic SAV relocation strategy is applied followed by the use of an alternate source of energy and newer vehicles in the network to reduce environmental loads. 2.6. Incident Management The conversation on autonomous driving mostly ventures towards crash aversion and incident management, with a common argument given that autonomous vehicles are not expected to tailgate, speed or drive under the influence of drugs and alcohol. The National Highway Trac Safety Administration [3] notes that approximately 90% of crashes are caused by an error on the part of drivers and also that fatigue, distraction and alcohol/drug-use is behind 40% of fatal accidents [11]. Additionally, improper incident management on busy roads and highways contributes to trac congestions that can have adverse impacts on driving behaviours, ride quality and travel time [48]. Nonetheless, Cambridge Systematics [49] noted that the cost of congestion is actually half than that of crashes, which cost approximately 277 billion USD/year. However, as noted by researchers like Farhadi et al. [50] and Campbell et al. [51], it may be dicult for the currently developed autonomous vehicles to safely tackle all crash scenarios and distinguish between objects and roadways. The immediate application of autonomous vehicles for incident management purposes can be in the form of ambulances and other emergency services, provided that public acceptance is achieved [52]. On the other hand, Tupper et al. [53] compared the benefits of the ITS incident management system (trac cameras) against savings from sustainable construction strategies: warm-mix asphalt, pavement Appl. Syst. Innov. 2020, 3, 1 8 of 21 recycling [54,55], reduction of fossil fuel reliance and using regionally-sourced materials [56,57]. Schrank and Lomax [58] have commented that urban areas prone to trac delays by incidents often utilise cameras as an ITS-based incident management strategy. Fries et al. [59] also used trac cameras for incident management, which forms the basis of Tupper et al. [53]’s study. Paramics’ trac micro-simulator with trac pattern, control and geometric information as inputs and EIO-LCA [60] were used to compare savings in fuel consumption and reduction in direct GHG emissions on a six-lane, 16.4 km interstate segment. However, the disturbance of trac flow patterns during construction was not modelled. Mobile6 was used to determine the emission rates which were later used as inputs for Paramics. The simulation was supported by incident modelling through a dedicated algorithm to randomly select incident detection time from 1–5 min historical distribution. In general, the results illustrated that over 3–4 years of operation of ITS incident management strategy, the benefits of this system surpassed those from the sustainable construction-phase strategies. The study also recommended that higher weightage should be assigned to ITS incident management in the Greenroads rating system. 2.7. Tackling Problem of Parking Maintaining equilibrium between the demand and supply of parking spots within urban regions has been critical for many transportation agencies around the city, as parking control forms an integral part of trip flow management. So far, the design of ITS-supported parking management systems has been researched, primarily focused on the close integration of entire roadway systems and parking facilities, thereby discarding the older approach of a secluded evaluation of parking-reserved areas in urban regions. A feasibility study on the use of telematics resources for the data management and logic architecture development of parking systems in the Rio de Janeiro metropolitan area of Niteroi was performed by Vianna et al. [61]. The study found that utilisation of telematics may potentially increase transport system coordination, the eciency of control routine implementation and existing parking spots’ management, which can then reduce pollution and trac congestion. The primary outcome of the study was that the seemingly technological transportation systems are faced by problems that may not be entirely technical. The main obstacles in implementing ITS parking are the defragmentation of planning agencies, lack of policies, social and political issues, control and inspection, administration of various parking types (on-, o - and commercial) and a lack of will from parking operating agencies. Moreover, the parking systems should be planned together with the transportation systems, and cities should focus on a rational assignment of PT systems, the highest-burden of trip distribution load. Another solution to the parking problem, specifically user time wasted in the so-called “cruising for parking” may be avoided by AVs [62,63]. One of the studies, cited earlier [24], has indicated that approximately 10 user-owned vehicles may be replaced by a single SAV. Moreover, SAVs may also decrease the parking cost. Currently parking in general urban regions may cost 1400–3700 USD/spot and 3300–5600 USD/spot in central business districts, as Litman [64] has reported. Shladover et al. [26] commented potentially 250 USD/AV may be saved in parking costs, depending upon sharing rates. 3. Long-Term Implications: The Question of Urban Development and Sustainability 3.1. Urbanism and Urban Sprawl A question that is not usually addressed in the AV-based research for transport systems is what automation in urban mobility will mean for urban development. Urban sprawl and the relocation of residents to cheaper neighbourhoods is a direct result of car-dependence, a behaviour that may witness further increases if AV private vehicles are readily available in the consumer market [65]. A preliminary study on the potential impact of AVs by Carrese et al. [66] was conducted in the city of Rome. The authors found that more than 70% of survey respondents showed interest in relocation to suburban areas based upon the market penetration rate of private AVs. The authors also projected through choice modelling and trac simulation that by the year 2050, around a 50% penetration rate Appl. Syst. Innov. 2020, 3, 1 9 of 21 of private AVs is expected to result in excessive urban sprawl, which in turn will result in heavy trac congestions in the suburban road network. On the other hand, PT usage may see a rapid decline after AV introduction in the private vehicle market [67] if adequate long-term planning measures are not implemented. Transit-oriented development (TOD) can be adopted to reduce the overburden on personalised vehicle transit, and encourage PT uptake if mixed-use, high-density, residential developments are clustered around ecient, fast and reliable PT [68,69]. Lu et al. [70] performed a choice modelling study for the residents of Atlanta, GA, USA. They found that the integration of SAVs with TOD can result in extending the PT service radius of TOD to 3.22 km. This study by Lu et al. [70] assumed that SAVs are expected to serve the first and last-mile need of local residents, and the survey results showed around 39.3% of the residents supported AV integration with TOD. However, Lu et al. [70] also recommended that future research should use real-word trac flow data to develop trac simulation models and investigate the application of AV-based PT system towards solving mobility issues. The AV-based intelligent transport study, currently being performed by the authors of this paper, is aimed towards addressing this recommendation. 3.2. Cost Implications Further application of strategies such as intelligent transportation spaces, automated transit gateways and AVs may result in higher savings. For example, 4000 USD/year/AV from social benefits of parking, fuel eciency, travel time and crash aversion as Fagnant and Kockelman [11] reported. Stephens et al. [71] found that the per-passenger mile cost of AVs is approximately $0.2 in the United States, provided that large-scale ride-sharing can be implemented. These results were considerably lower than the Netherlands-based findings of around $0.1 by Hazan et al. [72] for ride-sharing services using AV passenger cars. However, their study relied on average values and less detailed vehicle data, disregarding some stages of the vehicle life-cycle as well as the overhead cost. Bösch et al. [73] conducted a detailed study on the cost implications of the vehicle fleet automation for the Swedish trac. They found that the life-cycle costs of electric-powered AVs for the di erent vehicle types (shared/taxi vehicles, bus and rails) were lower than the respective types of conventional vehicles (CVs). This was mainly due to the high purchase costs being o set by cost savings in the low fuel, maintenance and insurance costs. In general, these operational level costs (per passenger-kilometre) showed that the AV-based bus, rail and shared taxi cars were 54.7%, 6.4% and 82% lower than their CV-based counterparts, respectively. The only exception was the passenger car where the AV-based car was slightly (4%) more expensive than the conventional cars due to the significantly higher share of the initial purchase cost in the vehicle life-cycle cost. However, the study did not account for the cost externalities, such as costs of growing a PT fleet, mode choice variation, user time costs and vehicle occupancy, as well as the per-unit cost of the environmental emissions. 3.3. Environmental Implications Cities around the world are constantly pursuing energy conservation and climate preservation (i.e., to reduce adverse environmental emissions). Luers et al. [74] projected that in order to avoid the drastic climatic changes, GHG emissions must be considerably reduced. While Shaheen and Lipman [75] found the transportation sector to be the primary source of emissions (14%) around the world, and as such, the sustainability problems in roadway projects should be addressed. Although, there have been various programmes, e.g., Green Highway Partnership and Greenroads [76–78], for promoting partnership and innovation in sustainable transportation planning. Recycling of pavement materials [79,80], e.g., ~99% in Japan [81], and the introduction of newer materials such as warm-mix asphalt [82] have increasingly become popular. However, as Clyne [83] commented, the impacts on the performance of pavements are still uncertain. The triple-bottom-line sustainability approach has also been researched, yet, the implementation of ITS as a tool for increasing sustainability of urban roadways is also uncertain. A life-cycle assessment Appl. Syst. Innov. 2020, 3, 1 10 of 21 study of Tupper et al. [53] on the urban South Carolina interstate found that an ITS strategy can reduce CO emissions (992 tons over 2 years) more than five times than any of the Greenroads-recommended sustainable construction stage strategies, explained earlier. The study emphasised over the importance of ITS as a sustainability tool, especially since the fuel consumption reductions from ITS were 30 times higher than all four strategies combined over eight years of repaving. They further iterated that despite the emphasis of transportation agencies on strategies such as warm-mix asphalt (WMA) [84,85], ITS can potentially provide higher savings. It should be noteworthy that the study did not account for the cost parameter of sustainability; nonetheless, these environmental savings were noted for the implementation of only one ITS strategy. Apart from the long-term implication of ITS in terms of environmental impacts, the life-cycle environmental performance of AVs in the transport system has also been investigated in the literature to a certain degree. A recent trac simulation study by Patella et al. [86] in the city of Rome (assuming 100% battery-electric AV private vehicle penetration rate and 15-year vehicle service life) projected that the GHG emissions in the 100% AV scenario can be 60% (~744 tonne CO eq.) lower than the non-AV scenario over the 15 years period. Noise emissions are also a critical concern in modern societies with an increase in trac levels. Patella et al. [87] investigated the acoustic environment of future cities in a hypothetical scenario (100% electric AV passenger cars in Rome) using trac simulation. They found due to the di erences in propulsion noises between conventional and AV passenger cars a better utilisation of road capacity by AVs (i.e., platooning) and improved trac flow in the AV scenario. It should be noted here that these studies only focused on the automation of private vehicles and disregarded mix-type trac flow situations. Similarly, the mode choice patterns of passengers, ride-sharing and population growth, etc., were not considered. Future work on estimating the environmental impact of AVs can build upon the preliminary results of these studies to develop trac simulation models utilising the real-world trac flow data for the mix-type trac containing origin–destination, occupancy, mode choice and speed–time–volume profiles of all vehicle types. This way the impact of AV on modern cities can be accurately estimated towards aiding long-term policy development by the relevant municipal agencies. 4. Limitations of Past Research and Implementation Barriers The current research identifies that multi-disciplinary problems are associated with making ITS and AVs an everyday reality. Intelligent transportation systems in general, and AVs in particular, have been viewed as a potential driver of the safer, cleaner and more ecient futuristic system. As the field is ever-changing, heavily dependence on the investment decisions of policy-makers, and its benefits and shortcomings deeply embedded in the question of on-demand mobility, uncertainty remains in its overall impact on the life-cycle of the urban roadway system. Hence, based on the agendas identified, Table 1 provided below, covers a comparative review of the studies covered in this paper to identify the important findings and limitations of existing research that can be further investigated in future research work. In general, a review of the above 22 studies shows that if energy-ecient, urban transport systems have to be implemented, factors such as the value of commuting time, knock-ons to economic productivity, stress and time implications on travellers (whilst complex to evaluate), require consideration in the planning of modern ITS and AV-based transportation. Approximately, 18% of the studies focused on ITS, while more than 82% of studies focused on AV application. Current transport literature has largely analysed AV technology for ride-sharing (32% studies), public transportation (28% studies) and personal/on-demand transport (9% studies). The benefits of ITS and AV technology in transportation systems were mainly identified in terms of travel time and congestion (46%), emissions and energy use (36%), cost factors (28%) and road capacity (9%). The major concerns and barriers were stakeholder acceptance (23% studies), and policy and technological shortcomings (18%). The limitations of existing studies that can be investigated in future research are discussed in the next section (Section 5). Appl. Syst. Innov. 2020, 3, 1 11 of 21 Table 1. A critical comparison of the methodologies, findings and limitations of 22 primary research studies covered in this paper. Researchers Methodology Findings Limitations (for Future Works) - AV and ride-sharing interaction - Prioritisation SAVs. needed for AV-PRTs. - Policy and regulations. Liu et al. [1] Analytical - AV-buses need dedicated roads. - Lane-keeping and docking. - Vehicle fatality rates may reduce. - Mode-choices e ects. - Vehicle gap reduction. - Ride-sharing Fagnant and Kockelman - High penetration rate is needed Analytical - Increase in trac due to AV use as [11] - Data privacy, hacking, security, a private vehicle. cost and regulatory concerns exist. - Mode shift e ects. - Ride-sharing will increase. - Electric power grid source. - Lower GHG and energy use. Greenblatt and Shaheen - Growth in car trac. Analytical - Trac congestion will decrease. [13] - Occupancy level. - VMT will change. - Mixed-use trac. - Dynamic ride-share (DRS). - Ride-sharing had positive results. - Life-cycle impacts. - Single SAV can replace 11 CVs. - Simulation of actual data. - SAV adds 10% in travel distance. - Di erent types of vehicles. - Most travellers waited for  5 min. - Real trac speed changes. - SAV sedans decreased energy - SAV relocation for parking. used by 12% (144 GJ) compared to Fagnant and Kockelman - Modelling of longer trips. Agent-based model light-duty vehicles. [14] - SAV occupancy levels. - GHG emissions decreased by - SAV replacement rate. 5.6%. - Alternate SAV fuel. - SO , PM and NO reduced by 2 x - Emission regulations. 19%, 6.5% and 18%, respectively, - Mode choice, destination and due to SAVs. time-of-day models. - AV technology aspects. - SAVs used < 10% of CV parking - Socio-economics. - SAVs provide high service level. - Transport system analysis. - SAVs increased travel time by - Stakeholder engagement. 30%. Rigole [22] Simulation modelling - Benefits of SAVs are dependent on - Dynamic simulation user ’s willingness to - Urbanism impacts. accept ride-sharing. - Safety and legal aspects. - DRS of SAVs reduces travel cost. - Private operators can earn 19% annual return on investment. - Trac emissions. Fagnant and Kockelman Simulation modelling - DRS critical in limiting VMTs. - Life-cycle costs. [24] - DRS reduce waiting by 4.5 min. - Service time reduced by 0.3 min. - ACC had minimal change on - Dynamic response of lane capacity. ACC systems. - Cooperative ACC can increase - Response to a strong Shladover et al. [26] Microscopic simulation lane capacity (almost double) with trac disturbance. high market penetration rate - Emergency stopping conditions. (around 100%). - Restricted trials. - Restricted AV-bus use showed - Regulations. user acceptance. - AV-buses have less likelihood of - Mode-shift and mode choices Alessandrini and Analytical speed-limit violations. - Sustainable development. Mercier-Handisyde [28] - AV-bus showed reduced fuel - Infrastructure design. consumption and emissions. - System-wide integration. Appl. Syst. Innov. 2020, 3, 1 12 of 21 Table 1. Cont. Researchers Methodology Findings Limitations (for Future Works) - AV PT bus platooning is feasible. - Delay impacts of AV PT. - Multiple trac demand. - AV-buses had 60 s wait times and Nguyen et al. [30] Microscopic simulation 44 s travel times. - Vehicle-road surface interactions. - Interpretability of results - Psychological barriers for AVs. among countries. - AV buses can reduce personal costs, congestions, waiting times - Technology awareness López-Lambas and Analytical of respondents. and emissions. Alonso [31] - Vehicle costs, infrastructure costs - Stakeholder engagement. and safety risks exist for AV bus. - Actual performance results. - ITS can be applied for trac data - Technology dependent. collection and collision warning. - Data security. Dey et al. [39] Field experiment - ITS reduce network interruption. - Changes in data network. - ITS ramp metering and speed limits improve travel time by 9%. - Impact of di erent trac - Bus delays reduced by 81.5%. management scenarios. - Trac delays reduced by 29.1%. - Open-source simulation software. Dadashzadeh and Ergun Microscopic simulation - Average speed increase by 12.7%. - Performance testing using several [48] modelling - ITS reduce bottlenecks by 2.8%. merging points. - Fuel use reduced by 78.2%. - Automatic model optimisation. - Emissions reduced by 17.3%. - Respondents expressed negative - Level of awareness. feelings for AV ambulances. - Exposure of people to AV ambulance service. - Respondents from Kazakhstan Zarkeshev and Csiszár less trusting than - Representative population Analytical [52] Hungarian respondents. surveys needed. - Readiness to ride is not influenced - Sensitivity to emergency level by gender. and severity. - ITS trac camera-generated - Trac impacts of construction. Economic input-output higher fuel savings than - Ramp metering. LCA and sustainable road construction. Tupper et al. [53] - Automation of transport. microsimulation - ITS strategy should be weighed - Social factors. modelling more for sustainable roads. - AV impact on PT travel time and services. - AVs favour urban sprawl. - Intra-zonal demand. - Only average trac data - AVs may replace PT in Meyer et al. [65] Transport modelling low-density urban areas. were modelled. - Road capacity. - Cost factors. - AVs may result in urban sprawl. - Travel time costs and fares. - AVs increase travel time (suburb - Small sample size (~200) Analytical (surveys) and to city centre) by 12%, due to considered (i.e., population Carrese et al. [66] trac simulation urban relocation. representation needed). - SAVs reduce travel time by 19%. - Mode choice impacts. - AVs may result in urban sprawl. - Ownership of AVs. - AVs and TOD integration. - SAV integration with AV PT and - Heterogeneity in preferences for transport system. TOD and AVs. Lu et al. [70] Analytical - Real trac data for - Results were dependent on time simulation modelling. savings and productivity - Cost and policy initiatives. opportunities from AV use. Appl. Syst. Innov. 2020, 3, 1 13 of 21 Table 1. Cont. Researchers Methodology Findings Limitations (for Future Works) - Demand-bundling is necessary to - AV impact on parking. maintain the competitiveness of - Mode choice impacts. PT against SAVs and private AV - Government policies. cars in dense urban areas. - Personnel costs. Bösch et al. [73] Analytical - High purchase and cleaning cost - Market price fluctuations. of SAVs is still a deterrence. - Alternate fuel use and costs. - AVs may remain in private use - Emissions results. due to low variable costs. - AVs decrease travel time and - More likely internal combustion increase average vehicle speeds. hybrid electric AVs must - AVs increase VMT on long roads. be modelled. - AVs increase GHG impacts on Trac simulation - AV-specific modelling software Patella et al. [86] construction and modelling should be used. procurement level - Mode-shift, ride-sharing & - AVs decrease environmental population growth impacts impacts at mobility level by 60%. - Central city area benefits from - Modelling of di erent types of Trac simulation lower noise emissions due to the vehicles and mixed trac in Patella et al. [87] modelling high penetration rate of AVs. actual trac fleet. - Technological, policy and user - Pilot projects to analyse the Srinivasan et al. [88] Analytical acceptance are barriers. transport impacts of AVs. - Gasoline hybrid SAVs had better - Carbon taxes. cost and travel time performance - Vehicle occupancy levels. than electric SAVs. - Cost benefit of gasoline hybrid - Trip combinations. Loeb and Kockelman SAVs depends on electric SAV - Trip load reduction. Simulation [89] price and gasoline cost. - Comparison with PT services - Electric SAV capable of and automation. zero-carbon transport if coupled - Emissions and energy use. with a renewable power grid. 4.1. Public Acceptance Manufacturers like Google currently plan to release AVs by the year 2020 [13], which may result in the safer and more ecient use of roadways. The goal of mobility innovation and automation is to transform the travel choices of people, and as such, its implications on urban PT. Automation carries many social prospects in addition to the legal precepts of licensing and security. Future travellers may have the choice of using smartphones, consuming meals and engaging in recreational activities during their private or shared transit. Even though Google has logged over 1.1 million kilometres [90] through AVs on public roads in California, high costs limit the spread of autonomous cars on the consumer market [38,91], a challenge that may best be addressed through autonomous PT if the perception of the public is duly considered, and initiatives to promote a general understanding of autonomous PT (e.g., buses)-mainly safety features are conveyed to the users. Adoption of closely travelling autonomous buses on dedicated lanes may increase the capacity of lanes [92], which may in return mean fuel savings, as less brake-accelerate manoeuvre will be needed. Preliminary data from the CityMobil2 automated bus project has revealed that there is currently a positive attitude from the general public, as Figure 5 [28] illustrates. Appl. Syst. Innov. 2019, 2, x FOR PEER REVIEW 13 of 20 the studies focused on ITS, while more than 82% of studies focused on AV application. Current transport literature has largely analysed AV technology for ride-sharing (32% studies), public transportation (28% studies) and personal/on-demand transport (9% studies). The benefits of ITS and AV technology in transportation systems were mainly identified in terms of travel time and congestion (46%), emissions and energy use (36%), cost factors (28%) and road capacity (9%). The major concerns and barriers were stakeholder acceptance (23% studies), and policy and technological shortcomings (18%). The limitations of existing studies that can be investigated in future research are discussed in the next section (Section 5). 4.1. Public Acceptance Manufacturers like Google currently plan to release AVs by the year 2020 [13], which may result in the safer and more efficient use of roadways. The goal of mobility innovation and automation is to transform the travel choices of people, and as such, its implications on urban PT. Automation carries many social prospects in addition to the legal precepts of licensing and security. Future travellers may have the choice of using smartphones, consuming meals and engaging in recreational activities during their private or shared transit. Even though Google has logged over 1.1 million kilometres [90] through AVs on public roads in California, high costs limit the spread of autonomous cars on the consumer market [38,91], a challenge that may best be addressed through autonomous PT if the perception of the public is duly considered, and initiatives to promote a general understanding of autonomous PT (e.g., buses)-mainly safety features are conveyed to the users. Adoption of closely travelling autonomous buses on dedicated lanes may increase the capacity of lanes [92], which may in return mean fuel savings, as less brake-accelerate manoeuvre will be needed. Preliminary data from the CityMobil2 automated bus project has revealed that there is currently a positive attitude Appl. Syst. Innov. 2020, 3, 1 14 of 21 from the general public, as Figure 5 [28] illustrates. La Rochelle (n = 425) Lausanne (n = 586) Vantaa (n =118) Trikala (n = 470) Coventional Buses Autonomous Buses Figure 5. Public responses on mode choices: conventional vs. autonomous buses under the same Figure 5. Public responses on mode choices: conventional vs. autonomous buses under the same route route parameters parameters 4.2. Policy and Technological Shortcomings 4.2. Policy and Technological Shortcomings Routine infrastructure planning set aside, it is often expected of the government agencies to provide a platform, encourage discussion and dialogue between stakeholders, and commission Routine infrastructure planning set aside, it is often expected of the government agencies to research projects promoting equity, safety and technological integration and development policies as provide a platform, encourage discussion and dialogue between stakeholders, and commission the resea machine-brawn rch projects promoting eq artificial intelligence uity, safety revolution and technologi engulfs cal i transportation ntegration and industry development polic . Algorithmsies as able to the mach crunchine- big brawn data and artslicker ificial int transportation elligence revol pr u omised tion engulf by autonomous s transportati vehicles on indust opens ry. Algori a newthm window s able, to crunch big data and slicker transportation promised by autonomous vehicles opens a new yet due to the substantial uncertainties associated with their long-term deployment impacts, partial window, yet due to the substantial uncertainties associated with their long-term deployment impacts, information makes policies dicult to draft. Srinivasan et al. [88], through a metropolitan case study partial information makes policies difficult to draft. Srinivasan et al. [88], through a metropolitan case from the Netherlands, found that despite the uncertainties associated with AV implementation, there study from the Netherlands, found that despite the uncertainties associated with AV implementation, is a need for encouraging systematic, approach-based pilot projects to study the long-term impacts. there is a need for encouraging systematic, approach-based pilot projects to study the long-term Researchers such as Liu et al. [1] asserted the need for developing a consensus on the transformation of the traditional transport systems through the emergence of vehicle automation technologies. Future research on the autonomous vehicle, and particularly autonomous PT as a mobility solution, should address how these alternate vehicle technologies can be adopted to reduce overburden on personalised vehicle transit and achieve city-wide sustainability goals of municipal authorities in congested and heavy trac density cities around the world. 5. Future Research Directions Although, information sharing initiatives such as Sustainability Mobility and Accessibility Research and Transformation (SMART) [93], have been aimed at carefully analysing the trade-o s between investment decisions, sustainability and innovative urban mobility. This is primarily because infrastructure investments are high budgeted and may become locked-in once the decisions have been made, and cities may be stuck with irreversible bad decisions, which predicament platforms such as SMART aim to avoid. Other programmes such as the global mobility start-up database Mobi [1] and smartphone app-powered commercial door-to-door shuttle service of Transdev also aim to provide an alternate and multi-modal solution to the urban transportation problem. Nevertheless, at present most government agencies around the world have yet to catch up to the technology of automated transit. For example, as Liu et al. [1] report, the vagueness in the Federal Transit Administration and ITS Joint Program Oce-led, on-demand mobility programme’s description of newer multi-modal mobility concepts and the absence of autonomous mobility from the U.S. Department of Transport strategic plan [94]. Due to the decreasing reliance of millennial travellers on single-occupancy vehicles and the divergence of trip load towards PT, researchers such as Liu et al. [1], Alessandrini et al. [27], Lesh [95] and Polzin [96] have commented that transportation agencies should also focus on long-term Number of survey respondents Appl. Syst. Innov. 2020, 3, 1 15 of 21 autonomous transit, SAVs, connected AVs and exclusive rights-of-way for AVs. Moreover, building upon the success of projects like the Morgan Town People mover [97], ideas like automated rapid bus transit, an automated vehicle serving > 15 travellers on dedicated lanes [5,98], may become the new mode choice for mass transit. Although at present the procurement and purchase costs of private and public AVs may be a concern [31] for practical implications, the research covered in Table 1 above shows that these high initial costs may be o set by life-cycle ownership costs, specifically fuel cost savings. Environmental impacts notwithstanding, the travel time and ride comfort factors may be the primary drivers of AV adoption for public transport and ride-sharing transit systems, and as such, should be focused in future research. It is also uncertain how the mode choice of travellers shall be a ected by the introduction of ITS and AV-based transportation systems. Similarly, the detailed impact of AV–PT, private AV cars and SAVs on the daily operation of trac fleet as well as life-cycle implications need to be empirically calculated. Scenario analyses of various AV adoption rates, AV adoption methods and fuel sources should be performed with the simultaneous engagement of stakeholders (government, users and private sectors). Additionally, the following research directions are critical for the real-world application of artificially intelligent and sustainable transport systems in line with the literature studies covered in Table 1. SAV prioritization, vehicle occupancy and sharing technique (dynamic vs. serial ride-sharing). Mode-shift and mode choices. Variation of trac density due to AV use in trac fleet. Renewable electric power grid and alternate fuel sources. Long-term trac and population growth. Use of actual trac count data and growth models for simulations and empirical calculations. High-resolution (per vehicle level) calculation of cost, energy use and pollutant emissions. The penetration rate of vehicles following new emission regulations (e.g., distribution percentages of Euro 1–6 emission standards). System-wide analysis and modelling of the transportation network. Dynamic vehicle simulation and dynamic choice-modelling. Urbanism, urban relocation and zonal developments (TODs) integration with AVs and ITS. Impacts of AV trac on the construction, maintenance and operation of road infrastructures. Impact of market fluctuations in AV prices, fuel prices and government taxes on AV adoption. Diversion of trip load towards AV-based public transport. Increasing the engagement level of public and government agencies in the decision-making process of transport systems to explore the benefits of automation and artificial intelligence. Benefits of autonomous vehicles may result in a greater contribution to the life-cycle sustainability of transportation infrastructures. As stated by Hameed and Hancock [99], unused parking spaces and sustainable transportation initiatives may reduce the GHG emissions and energy consumption needs of urban mobility. Researchers [100,101] have highlighted that the sustainability triple-bottom-line approach should be the primary concern of decision-makers. Even with the uncertainties associated with the deployment and operation of autonomous public transport, it might be apt for the decision- and policy-makers to consider AVs as part of the sustainability investigations of urban public transportation solutions. 6. Conclusions The goal of a future smart city is to have mass and goods mobility primarily supported by an entirely autonomous vehicle system. The goal behind this paper was to critically analyse the state-of-the-art of the research on AVs and ITS transport systems, specifically focused on establishing the research methodologies, main findings and limitations that can be researched in future works. Appl. Syst. Innov. 2020, 3, 1 16 of 21 A total of 22 primary and 79 supplementary researches on the topic were reviewed from peer-reviewed transport literature. The results show that it is expected that in the next thirty years or so, autonomous vehicles may become a reality on public roads. Research has shown that the benefits of AVs and ITS in terms of insurance, congestion and parking costs are in the range of $2960–3900 per year per vehicle. The research on private autonomous cars concluded with a positive outlook on the SAV mobility if electric/hybrid electric autonomous cars were to be the driving force behind the system. The situation of future transport systems is further complicated by the social, environmental, political, and more so, budgetary, constraints of transportation agencies, making sustainability the primary concern. Results of this review paper showed that automated urban mass transit may also be a promising alternative to personalised travel, where users can either subscribe or operate on a pay-as-you-go basis without the need for a driver or attendant for the service operation. The expected benefits of AV–PT are the reduction of land-use, cessation of trac congestion, low user costs, fewer time delays and environmental burdens, mobility solutions to elderly and disabled people in the form of ecient and safe transport. The integration of shared mobility solutions with automated gateways and autonomous PT systems has also been investigated in the literature to certain degrees. Trac simulation, analytical reviews and stated-preference surveys and limited filed data have been used to perform scenario analysis of various AV-based PT and ITS strategies in the existing transport literature. However, the review also found that the majority of literature lacked in concise integration of stakeholder engagement, real-world trac data, cost and emission models and detailed trac simulation modelling for life-cycle implications of ITS and AV transport. Results also show that barriers regarding the technical, technological and policy-making exist for the practical, large-scale implementation of automated transit. Fragmentation of the government agencies involved, the distance between the research and stakeholder spectrums and divided public opinion further complicates the situation. The sustainability triple-bottom-line approach may be used for determining the long-term impacts of autonomous transit. As such, this study, which acts as a platform for describing the current state and need for a life-cycle analysis method for evaluating the application of autonomous rapid PT, identifies that there may be a potential for autonomous bus rapid transit (BRT) services to increase the productivity, energy and fuel savings capacity, as well as safety, of the urban transportation systems. Author Contributions: Conceptualisation, methodology, formal analysis, and writing—original draft, U.H.; supervision, writing—review and editing, and validation, A.W.; review and editing, H.A.J. All authors reviewed the results and approved the final version of the manuscript. All authors have read and agreed to the published version of the manuscript. Funding: This research project is supported by an Australian Government Research Training Program (RTP) scholarship. Conflicts of Interest: The authors declare no conflict of interest. References 1. Liu, R.R.; Fagnant, D.J.; Zhang, W.-B. Beyond single occupancy vehicles: Automated transit and shared mobility. In Road Vehicle Automation 3, 1st ed.; Meyer, G., Beiker, S., Eds.; Springer International Publishing: Cham, Switzerland, 2016; pp. 259–275. 2. Santos, A.; McGuckin, N.; Nakamoto, H.Y.; Gray, D.; Liss, S. Summary of Travel Trends: 2009 National Household Travel Survey; Department of Transportation: Washington, DC, USA, 2011; p. 82. 3. National Highway Trac Safety Administration. Preliminary Statement of Policy Concerning Automated Vehicles; National Highway Trac Safety Administration (NHTSA): Washington, DC, USA, 2013. 4. IHS Automotive. Emerging Technologies: Autonomous Cars—Not if, but When; IHS Automotive: London, UK, 5. Liu, R.R. Automated Transit: Planning, Operation, and Applications; John Wiley & Sons: Hoboken, NJ, USA, 2016; p. 224. Appl. Syst. Innov. 2020, 3, 1 17 of 21 6. Liu, R.R. Spectrum of automated guideway transit (AGT) technology and its applications. In Handbook of Transportation Engineering, Volume II: Applications and Technologies, 2nd ed.; Kutz, M., Ed.; McGraw-Hill Education: New York, NY, USA, 2011. 7. Massey, R. Volvo Develops the ‘No Death’ Car: Vehicles Which Drive Themselves and are Totally Crashproof Could be on British Roads in Eight Years. In Daily Mail; DMG Media: London, UK, 2012. 8. Nissan Motor Company. Nissan Announces Unprecedented Autonomous Drive Benchmarks; Nissan Motor Co. Ltd.: Irvine, CA, USA, 2013. 9. Sergey Brin Hopes People Will Be Driving Google Robot Cars in “Several Years”. Available online: http://www.siliconbeat.com/2012/09/25/sergey-brin-hopes-people-will-be-driving-google-robot- cars-in-several-years (accessed on 18 December 2019). 10. Wang, F.-Y. Driving into the future with ITS. IEEE Intell. Syst. 2006, 21, 94–95. [CrossRef] 11. Fagnant, D.J.; Kockelman, K. Preparing a nation for autonomous vehicles: Opportunities, barriers and policy recommendations. Transp. Res. Part A Policy Pract. 2015, 77, 167–181. [CrossRef] 12. Leonard, J.; How, J.; Teller, S.; Berger, M.; Campbell, S.; Fiore, G.; Fletcher, L.; Frazzoli, E.; Huang, A.; Karaman, S.; et al. A perception-driven autonomous urban vehicle. J. Field Robot. 2008, 25, 727–774. [CrossRef] 13. Greenblatt, J.B.; Shaheen, S. Automated Vehicles, On-Demand Mobility, and Environmental Impacts. Curr. Sustain. Renew. Energy Rep. 2015, 2, 74–81. [CrossRef] 14. Fagnant, D.J.; Kockelman, K.M. The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios. Transp. Res. Part C Emerg. Technol. 2014, 40, 1–13. [CrossRef] 15. Tang, S.; Wang, F.-Y.; Miao, Q. ITSC 05: Current issues and research trends. IEEE Intell. Syst. 2006, 21, 96–102. [CrossRef] 16. Kuwata, Y.; Teo, J.; Fiore, G.; Karaman, S.; Frazzoli, E.; How, J.P. Real-time motion planning with applications to autonomous urban driving. IEEE Trans. Control Syst. Technol. 2009, 17, 1105–1118. [CrossRef] 17. Hasan, U.; Whyte, A.; Al Jassmi, H. Framework for Delivering an AV-based Mass Mobility Solution: Integrating Government-Consumer Actors and Life-cycle Analysis of Transportation Systems. In Proceedings of the 46th European Transport Conference, Dublin, Ireland, 10–12 October 2018. 18. Shaheen, S.; Cohen, A. North American carsharing market trends. In Innovative Mobility Carsharing Outlook: Carsharing Market Overview, Analysis, and Trends; University of Berkeley: Berkeley, CA, USA, 2013; Volume 2, pp. 1–6. 19. Shaheen, S.; Cohen, A. Worldwide carsharing growth continues. In Innovative Mobility Carsharing Outlook: Carsharing Market Overview, Analysis, and Trends; University of Berkeley: Berkeley, CA, USA, 2016; pp. 1–6. 20. Kornhauser, A. Uncongested Mobility for All: New Jersey’s Area-Wide a Taxi System; Princeton University Operations Research and Financial Engineering: Princeton, NJ, USA, 2013; p. 62. 21. Ford, H.J. Shared Autonomous Taxis: Implementing an Ecient Alternative to Automobile Dependency; Princeton University: Princeton, NJ, USA, 2012; p. 104. 22. Rigole, P.-J. Study of a Shared Autonomous Vehicles Based Mobility Solution in Stockholm. Master ’s Thesis, Royal Institute of Technology (KTH), Stockholm, Sweden, 2014; p. 41. 23. Holmes, J. Zipcar—The Promise of Automated Vehicles. In Proceedings of the 4th Automated Vehicle Symposium, Albany, NY, USA, 2 December 2015; Association for Unmanned Vehicle Systems International: Ann Arbor, MI, USA, 2015. 24. Fagnant, D.J.; Kockelman, K.M. Dynamic ride-sharing and optimal fleet sizing for a system of shared autonomous vehicles. In Proceedings of the Transportation Research Board 94th Annual Meeting, Washington, DC, USA, 11–15 January 2015. 25. Schrank, D.; Eisele, B.; Lomax, T. TTI’s 2012 Urban Mobility Report; Texas A&M Transportation Institute: Bryan, TX, USA, 2012. 26. Shladover, S.; Su, D.; Lu, X.-Y. Impacts of cooperative adaptive cruise control on freeway trac flow. Transp. Res. Rec. J. Transp. Res. Board 2012, 2324, 63–70. [CrossRef] 27. Alessandrini, A.; Campagna, A.; Delle Site, P.; Filippi, F.; Persia, L. Automated vehicles and the rethinking of mobility and cities. Transp. Res. Procedia 2015, 5, 145–160. [CrossRef] 28. Alessandrini, A.; Mercier-Handisyde, P. CityMobil 2 experience and recommendations. Retrieved Febr. 2016, 22, 2017. Appl. Syst. Innov. 2020, 3, 1 18 of 21 29. Dadashzadeh, N.; Ergun, M. Spatial bus priority schemes, implementation challenges and needs: An overview and directions for future studies. Public Transp. 2018, 10, 545–570. [CrossRef] 30. Nguyen, T.; Xie, M.; Liu, X.; Arunachalam, N.; Rau, A.; Lechner, B.; Busch, F. Platooning of Autonomous Public Transport Vehicles: The Influence of Ride Comfort on Travel Delay. Sustainability 2019, 11, 5237. [CrossRef] 31. López-Lambas, M.E.; Alonso, A. The Driverless Bus: An Analysis of Public Perceptions and Acceptability. Sustainability 2019, 11, 4986. [CrossRef] 32. Wang, F.-Y.; Zeng, D.; Yang, L. Smart cars on smart roads: An IEEE intelligent transportation systems society update. IEEE Pervasive Comput. 2006, 5, 68–69. [CrossRef] 33. Hasan, U.; Chegenizadeh, A.; Nikraz, H. Nanotechnology future and present in construction industry: Applications in geotechnical engineering. In Advanced Research on Nanotechnology for Civil Engineering Applications; IGI Global: Hershey, PA, USA, 2016; pp. 141–179. 34. Simon, H.A. The Sciences of the Artificial, 3rd ed.; MIT Press: Cambridge, MA, USA, 1996; p. 248. 35. Wang, F.-Y. Agent-based control for networked trac management systems. IEEE Intell. Syst. 2005, 20, 92–96. [CrossRef] 36. Builder, C.H.; Bankes, S.C. Artificial Societies: A Concept for Basic Research on the Societal Impacts of Information Technology; RAND Corporation: Santa Monica, CA, USA, 1991; p. 26. 37. Wang, F.-Y.; Tang, S. Artificial societies for integrated and sustainable development of metropolitan systems. IEEE Intell. Syst. 2004, 19, 82–87. [CrossRef] 38. KPMG; Center for Automotive Research. Self-Driving Cars: The Next Revolution; KPMG and CAR: Ann Arbor, MI, USA, 2012; p. 36. 39. Dey, K.C.; Rayamajhi, A.; Chowdhury, M.; Bhavsar, P.; Martin, J. Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication in a heterogeneous wireless network—Performance evaluation. Transp. Res. Part C Emerg. Technol. 2016, 68, 168–184. [CrossRef] 40. Ndashimye, E.; Ray, S.K.; Sarkar, N.I.; Gutiérrez, J.A. Vehicle-to-infrastructure communication over multi-tier heterogeneous networks: A survey. Comput. Netw. 2017, 112, 144–166. [CrossRef] 41. Huang, Y.; Bird, R.; Bell, M. A comparative study of the emissions by road maintenance works and the disrupted trac using life cycle assessment and micro-simulation. Transp. Res. Part D Transp. Environ. 2009, 14, 197–204. [CrossRef] 42. Zhou, Y.; Tupper, L.; Chowdhury, M.; Klotz, L. Green credits versus environmentally sustainable trac operations: Comparison of contributions to energy and emissions reductions. Transp. Res. Rec. J. Transp. Res. Board 2010, 2163, 103–111. [CrossRef] 43. Rister, B.; Graves, C. The Cost of Construction Delays and Trac Control for Life-Cycle Cost Analysis of Pavements; Kentucky Transportation Center: Lexington, KY, USA, 2002; p. 46. 44. Gupta, S.; Kalmanje, S.; Kockelman, K.M. Road pricing simulations: Trac, land use and welfare impacts for Austin, Texas. Transp. Plan. Technol. 2006, 29, 1–23. [CrossRef] 45. Litman, T. Online Transportation Demand Management Encyclopaedia; Victoria Transportation Policy Institute: Victoria, BC, Canada, 2013. 46. van Arem, B.; Abbas, M.M.; Li, X.; Head, L.; Zhou, X.; Chen, D.; Bertini, R.; Mattingly, S.P.; Wang, H.; Orosz, G. Integrated trac flow models and analysis for automated vehicles. In Road Vehicle Automation 3, 1st ed.; Meyer, G., Beiker, S., Eds.; Springer International Publishing: Cham, Switzerland, 2016; pp. 249–258. 47. Atiyeh, C. Predicting Trac Patterns, One Honda at a Time. MSN Auto, 25 June 2012. 48. Dadashzadeh, N.; Ergun, M. An Integrated Variable Speed Limit and ALINEA Ramp Metering Model in the Presence of High Bus Volume. Sustainability 2019, 11, 6326. [CrossRef] 49. Cambridge Systematics. Crashes vs. Congestion: What’s the Cost to Society? American Automobile Association: Washington, DC, USA, 2011; p. 58. 50. Farhadi, A.; Endres, I.; Hoiem, D.; Forsyth, D. Describing objects by their attributes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (CVPR 2009), Miami, FL, USA, 20–25 June 2009; IEEE: Piscataway, NJ, USA, 2009. 51. Campbell, M.; Egerstedt, M.; How, J.P.; Murray, R.M. Autonomous driving in urban environments: Approaches, lessons and challenges. Philos. Philos. Trans. R. Soc. Lond. A Math. Phys. Eng. Sci. 2010, 368, 4649–4672. [CrossRef] [PubMed] Appl. Syst. Innov. 2020, 3, 1 19 of 21 52. Zarkeshev, A.; Csiszár, C. Are People Ready to Entrust Their Safety to an Autonomous Ambulance as an Alternative and More Sustainable Transportation Mode? Sustainability 2019, 11, 5595. [CrossRef] 53. Tupper, L.L.; Chowdhury, M.A.; Klotz, L.; Fries, R.N. Measuring sustainability: How trac incident management through intelligent transportation systems has greater energy and environmental benefits than common construction-phase strategies for “green” roadways. Int. J. Sustain. Transp. 2012, 6, 282–297. [CrossRef] 54. Hasan, U. Experimental Study on Bentonite Stabilisation Using Construction Waste and Slag. Ph.D. Thesis, Department of Civil Engineering, Curtin University, Perth, Australia, 2015; p. 181. 55. Hasan, U.; Chegenizadeh, A.; Budihardjo, M.A.; Nikraz, H. Experimental Evaluation of Construction Waste and Ground Granulated Blast Furnace Slag as Alternative Soil Stabilisers. Geotech. Geol. Eng. 2016, 34, 1707–1722. [CrossRef] 56. Hasan, U.; Chegenizadeh, A.; Budihardjo, M.; Nikraz, H. A review of the stabilisation techniques on expansive soils. Aust. J. Basic Appl. Sci. 2015, 9, 541–548. 57. Hasan, U.; Chegenizadeh, A.; Budihardjo, M.A.; Nikraz, H. Shear Strength Evaluation of Bentonite Stabilised With Recycled Materials. J. Geoengin. 2016, 11, 59–73. 58. Schrank, D.; Lomax, T. The 2004 Urban Mobility Report; Texas Transportation Institute: College Station, TX, USA, 2004; p. 26. 59. Fries, R.; Chowdhury, M.; Ma, Y. Accelerated incident detection and verification: A benefit to cost analysis of trac cameras. J. Intell. Transp. Syst. 2007, 11, 191–203. [CrossRef] 60. Carnegie Mellon University. EIO-LCA: Free, Fast, Easy Life Cycle Assessment; Carnegie Mellon University: Pittsburgh, PA, USA, 2011. 61. Vianna, M.M.B.; da Silva Portugal, L.; Balassiano, R. Intelligent transportation systems and parking management: Implementation potential in a Brazilian city. Cities 2004, 21, 137–148. [CrossRef] 62. How Vehicle Automation Will Cut Fuel Consumption. Available online: https://www.technologyreview. com/s/425850/how-vehicle-automation-will-cut-fuel-consumption (accessed on 18 December 2019). 63. Shoup, D.C. The High Cost of Free Parking; Planners Press, American Planning Association: Chicago, IL, USA, 2011; p. 808. 64. Litman, T.A. Parking Management: Strategies, Evaluation and Planning; Victoria Transport Policy Institute: Victoria, BC, Canada, 2012; p. 31. 65. Meyer, J.; Becker, H.; Bösch, P.M.; Axhausen, K.W. Autonomous vehicles: The next jump in accessibilities? Res. Transp. Econ. 2017, 62, 80–91. [CrossRef] 66. Carrese, S.; Nigro, M.; Patella, S.M.; Toniolo, E. A preliminary study of the potential impact of autonomous vehicles on residential location in Rome. Res. Transp. Econ. 2019, 75, 55–61. [CrossRef] 67. Rode, P.; Floater, G.; Thomopoulos, N.; Docherty, J.; Schwinger, P.; Mahendra, A.; Fang, W. Accessibility in Cities: Transport and Urban Form. In Disrupting Mobility: Impacts of Sharing Economy and Innovative Transportation on Cities; Meyer, G., Shaheen, S., Eds.; Springer International Publishing: Cham, Switzerland, 2017; pp. 239–273. 68. Buehler, R. Can public transportation compete with automated and connected cars? J. Public Transp. 2018, 21, 2. [CrossRef] 69. Hasan, U.; Whyte, A.; Al Jassmi, H. Life-Cycle Asset Management in Residential Developments Building on Transport System Critical Attributes via a Data-Mining Algorithm. Buildings 2018, 9, 1. [CrossRef] 70. Lu, Z.; Du, R.; Dunham-Jones, E.; Park, H.; Crittenden, J. Data-enabled public preferences inform integration of autonomous vehicles with transit-oriented development in Atlanta. Cities 2017, 63, 118–127. [CrossRef] 71. Stephens, T.S.; Gonder, J.; Chen, Y.; Lin, Z.; Liu, C.; Gohlke, D. Estimated Bounds and Important Factors for Fuel Use and Consumer Costs of Connected and Automated Vehicles; United States Department of Energy: Golden, CO, USA, 2016; p. 48. 72. Hazan, J.; Lang, N.; Ulrich, P.; Chua, J.; Doubara, X.; Ste ens, T. Will Autonomous Vehicles Derail Trains? The Boston Consulting Group: Seattle, WA, USA, 2016; p. 16. 73. Bösch, P.M.; Becker, F.; Becker, H.; Axhausen, K.W. Cost-based analysis of autonomous mobility services. Transp. Policy 2018, 64, 76–91. [CrossRef] 74. Luers, A.L.; Mastrandrea, M.D.; Hayhoe, K.; Frumho , P.C. How to Avoid Dangerous Climate Change: A Target for US Emissions Reductions; Union of Concerned Scientists: Cambridge, MA, USA, 2007; p. 34. Appl. Syst. Innov. 2020, 3, 1 20 of 21 75. Shaheen, S.A.; Lipman, T.E. Reducing greenhouse emissions and fuel consumption: Sustainable approaches for surface transportation. IATSS Res. 2007, 31, 6–20. [CrossRef] 76. Green Highways Partnership. Available online: http://www.greenhighwayspartnership.org/index.php (accessed on 18 December 2019). 77. Anderson, J.; Weiland, C.; Muench, S. Greenroads Manual V. 1.5; University of Washington: Seattle, WA, USA, 78. Clevenger, C.M.; Ozbek, M.E.; Simpson, S. Review of sustainability rating systems used for infrastructure projects. In Proceedings of the 49th ASC Annual International Conference Proceedings, San Luis Obispo, CA, USA, 10–13 April 2013. 79. Hasan, U.; Whyte, A.; Al Jassmi, H. Critical review and methodological issues in integrated life-cycle analysis on road networks. J. Clean. Prod. 2019, 206, 541–558. [CrossRef] 80. Whyte, A. Life-Cycle Assessment of Built Asset Waste Materials-Sustainable Disposal Options, 1st ed.; Lambert Academic Publishing (LAP): Saarbrücken, Germany, 2012; p. 201. 81. Kawakami, A.; Nitta, H.; Kanou, T.; Kubo, K. Study on CO Emissions of Pavement Recycling Methods, 2nd ed.; Public Works Research Institute: Tsukuba-Shi, Japan, 2010; p. 10. 82. Barros, C.B.; Dmytrow, S. Weather-mix asphalt: Warm approach works in California, where climates of all kinds play. Roads Bridges 2009, 47, 26–28. 83. Clyne, T. Warm mix asphalt. In Proceedings of the North Dakota Asphalt Conference, Bismark, ND, USA, 6–7 April 2010. 84. Brown, D. Warm mix: The light is green. Hot Mix Asph. Technol. 2008, 13, 20–32. 85. Walker, D. Gaining experience with warm mix asphalt. Asphalt 2009, 24, 22–28. 86. Patella, S.M.; Scrucca, F.; Asdrubali, F.; Carrese, S. Carbon Footprint of autonomous vehicles at the urban mobility system level: A trac simulation-based approach. Transp. Res. Part D Transp. Environ. 2019, 74, 189–200. [CrossRef] 87. Patella, S.M.; Aletta, F.; Mannini, L. Assessing the impact of Autonomous Vehicles on urban noise pollution. Noise Mapp. 2019, 6, 72. [CrossRef] 88. Srinivasan, S.; Smith, S.; Milakis, D. Implications of vehicle automation for planning. In Road Vehicle Automation 3, 1st ed.; Meyer, G., Beiker, S., Eds.; Springer International Publishing: Cham, Switzerland, 2016; pp. 287–295. 89. Loeb, B.; Kockelman, K.M. Fleet performance and cost evaluation of a shared autonomous electric vehicle (SAEV) fleet: A case study for Austin, Texas. Transp. Res. Part A Policy Pract. 2019, 121, 374–385. [CrossRef] 90. Anthony, S. Google’s Self-Driving Car Passes 700,000 Accident-Free Miles, Can Now Avoid Cyclists, Stop at Railroad Crossings, Extreme Tech; Lendino, J., Ed.; Zi Davis Media: New York City, NY, USA, 2014. 91. Economist Technology Quarterly: Q3. Inside Story: Look, no Hands. In The Economist; The Economist Group: Chicago, IL, USA, 2012; pp. 17–19. 92. Tientrakool, P.; Ho, Y.C.; Maxemchuk, N.F. Highway capacity benefits from using vehicle-to-vehicle communication and sensors for collision avoidance. In Proceedings of the 2011 IEEE Vehicular Technology Conference (VTC Fall), San Francisco, CA, USA, 5–8 September 2011; IEEE: Piscataway, NJ, USA, 2011. 93. Zielinski, S. Whole system connectivity: Focus on user. In Proceedings of the 4th Automated Vehicle Symposium, Albany, NY, USA, 2 December 2015; Association for Unmanned Vehicle Systems International: Ann Arbor, MI, USA, 2015. 94. U.S. Department of Transportation. Beyond Trac: 2045 Final Report; U.S. Department of Transportation: Washington, DC, USA, 2015; p. 230. 95. Lesh, M. Multimodal ITS transit research direction—Integrated, connected, on-demand shared use mobility. In Proceedings of the 4th Automated Vehicle Symposium, Albany, NY, USA, 2 December 2015; Association for Unmanned Vehicle Systems International: Ann Arbor, MI, USA, 2015. 96. Polzin, S.E. How New Technologies and Autonomous Vehicles May Change Public Transportation; PSTA Board Workshop, Center for Urban Transportation Research, University of South Florida: Tampa, FL, USA, 2016. 97. Raney, S.; Young, S.E. Morgantown people mover–updated description. In Proceedings of the Transportation Research Board Annual Meeting 2005, Washington, DC, USA, 15 November 2004. 98. United States Government Publishing Oce. Part 393—Parts and Accessories Necessary for Safe Operation. Code of Federal Regulations: Title 49—Transportation. Available online: https://www.gpo.gov/fdsys/pkg/ CFR-2011-title49-vol5/xml/CFR-2011-title49-vol5-part393.xml (accessed on 18 December 2019). Appl. Syst. Innov. 2020, 3, 1 21 of 21 99. Hameed, F.; Hancock, K. Incorporating Costs of Life-Cycle Impacts into Transportation Program Development. Transp. Res. Rec. J. Transp. Res. Board 2014, 2453, 77–83. [CrossRef] 100. Lopes Gil, J.; Duarte, J.P. Tools for evaluating the sustainability of urban design: A review. Proc. ICE-Urban Des. Plan. 2013, 166, 311–325. 101. Santos, J.; Ferreira, A.; Flintsch, G. A life cycle assessment model for pavement management: Road pavement construction and management in Portugal. Int. J. Pavement Eng. 2015, 16, 315–336. [CrossRef] © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied System Innovation Multidisciplinary Digital Publishing Institute

A Review of the Transformation of Road Transport Systems: Are We Ready for the Next Step in Artificially Intelligent Sustainable Transport?

Loading next page...
 
/lp/multidisciplinary-digital-publishing-institute/a-review-of-the-transformation-of-road-transport-systems-are-we-ready-AKc0012WPL

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
Multidisciplinary Digital Publishing Institute
Copyright
© 1996-2019 MDPI (Basel, Switzerland) unless otherwise stated Terms and Conditions Privacy Policy
ISSN
2571-5577
DOI
10.3390/asi3010001
Publisher site
See Article on Publisher Site

Abstract

Review A Review of the Transformation of Road Transport Systems: Are We Ready for the Next Step in Artificially Intelligent Sustainable Transport? 1 , 1 2 Umair Hasan * , Andrew Whyte and Hamad Al Jassmi School of Civil and Mechanical Engineering, Curtin University, Perth, WA 6845, Australia; andrew.whyte@curtin.edu.au Director of Roadway, Transportation and Trac Safety Research Centre (RTTSRC), United Arab Emirates University, Al Ain, UAE; h.aljasmi@uaeu.ac.ae * Correspondence: umair.hasan@postgrad.curtin.edu.au; Tel.: +61-46-716-2000 Received: 19 November 2019; Accepted: 18 December 2019; Published: 19 December 2019 Abstract: Mobility is experiencing a revolution, as advanced communications, computers with big data capacities, ecient networks of sensors, and signals, are developing value-added applications such as intelligent spaces and autonomous vehicles. Another new technology that is both promising and might even be pervasive for faster, safer and more environmentally-friendly public transport (PT) is the development of autonomous vehicles (AVs). This study aims to understand the state of the current research on the artificially intelligent transportation system (ITS) and AVs through a critical evaluation of peer-reviewed literature. This study’s findings revealed that the majority of existing research (around 82% of studies) focused on AVs. Results show that AVs can potentially reduce more than 80% of pollutant emissions per mile if powered by alternate energy resources (e.g., natural gas, biofuel, electricity, hydrogen cells, etc.). Not only can private vehicle ownership be cut down by bringing in ridesharing but the average vehicle miles travelled (VMT) should also be reduced through improved PT. The main benefits of AV adoption were reported in the literature to be travel time, trac congestion, cost and environmental factors. Findings revealed barriers such as technological uncertainties, lack of regulation, unawareness among stakeholders and privacy and security concerns, along with the fact that lack of simulation and empirical modelling data from pilot studies limit the application. AV–PT was also found to be the most sustainable strategy in dense urban areas to shift the heavy trip load from private vehicles. Keywords: autonomous cars; urban mobility; rapid bus transport; greenhouse gas (GHG) emissions; energy conservation 1. Introduction The buzz around autonomous mobility, intelligent transportation system and smart cities has propelled the ventures of personal and public transit into new domains. In its early purview, concept of autonomous vehicles was integrated with urban bus transit in the form of the United States Department of Transportation (US DoT)’s Vehicle Assist and Automation (VAA) programme (tested with 1 m spaced permanent magnetic markers over a 3-mile route in a maintenance yard), and California’s PATH, a completely automated highway bus fleet in 2003. While the VAA tests on buses were performed in 2014 by operating an equipped bus for 6–8 h/day, completely autonomous bus transit operations have yet to be developed, as most current systems require drivers with the partially automated speed or lane controls [1]. At present, automated vehicles are categorised by the National Highway Trac Safety Administration (NHTSA) [2–4] according to their automation functionality into five levels of increasing automation (ranging from zero automation to a completely unmanned autonomous vehicle). Appl. Syst. Innov. 2020, 3, 1; doi:10.3390/asi3010001 www.mdpi.com/journal/asi Appl. Syst. Innov. 2020, 3, 1 2 of 21 Appl. Syst. Innov. 2019, 2, x FOR PEER REVIEW 2 of 20 For example, the VAA programme demonstrated a Level 2 in the US state of Oregon, that is, For example, the VAA programme demonstrated a Level 2 in the US state of Oregon, that is, precision docking and lane adherence by the steering automation, while with the manned braking precision docking and lane adherence by the steering automation, while with the manned braking and throttling of on-transit bus. Figure 1, prepared after Liu [5], covers the state of research on the and throttling of on-transit bus. Figure 1, prepared after Liu [5], covers the state of research on the autonomous transportations systems in urban areas around the world based upon the vehicular size, autonomous transportations systems in urban areas around the world based upon the vehicular size, capacity and operational features. capacity and operational features. Figure 1. Mode classification of autonomous urban transportation (based on Liu [5]). Figure 1. Mode classification of autonomous urban transportation (based on Liu [5]). On the note of autonomous buses, CityMobil2 is an innovative PT project, tested at the University On the note of autonomous buses, CityMobil2 is an innovative PT project, tested at the La Sapienza in Rome to investigate the feasibility of autonomous public transit by studying the shared University La Sapienza in Rome to investigate the feasibility of autonomous public transit by and on-demand mobility on an automated guideway transit, road user interactions and bus user studying the shared and on-demand mobility on an automated guideway transit, road user experiences, where a considerably positive outlook from the public (See Section 2.3) was observed. interactions and bus user experiences, where a considerably positive outlook from the public (See Automated guideway transit was defined by Liu [6] and Liu et al. [1] as a mode of urban transportation Section 2.3) was observed. Automated guideway transit was defined by Liu [6] and Liu et al. [1] as a where dedicated tracks are used for the operation of completely autonomous vehicles. In the case of mode of urban transportation where dedicated tracks are used for the operation of completely autonomous personal transportation, companies such as Volvo and Nissan have expressed interest autonomous vehicles. In the case of autonomous personal transportation, companies such as Volvo in commercially marketing autonomous vehicles by the year 2020 [7,8]. While according to Google and Nissan have expressed interest in commercially marketing autonomous vehicles by the year 2020 co-founder Sergey Brin, autonomous vehicles may be a reality on U.S roads within the next five years [9] [7,8]. While according to Google co-founder Sergey Brin, autonomous vehicles may be a reality on or so. Researchers such as Wang [10] and Fagnant and Kockelman [11] proposed that the operation of U.S roads within the next five years [9] or so. Researchers such as Wang [10] and Fagnant and autonomous personal cars may result in safer and less congested travel. As already evidenced by the Kockelman [11] proposed that the operation of autonomous personal cars may result in safer and less increased fuel savings, roadway capacity, accident aversion and lower emissions are now due to the congested travel. As already evidenced by the increased fuel savings, roadway capacity, accident introduction of some level of automation (e.g., assisted parking and adaptive cruise control) in newer aversion and lower emissions are now due to the introduction of some level of automation (e.g., cars. Apart from fully automated cars such as Massachusetts Institute of Technology (MIT)’s fully assisted parking and adaptive cruise control) in newer cars. Apart from fully automated cars such as autonomous Land Rover LR3 Talos [12], shared autonomous vehicles (SAVs) have also been researched Massachusetts Institute of Technology (MIT)’s fully autonomous Land Rover LR3 Talos [12], shared as an extension of the emerging e-hail or car-sharing [13]. Fagnant and Kockelman [14] simulated autonomous vehicles (SAVs) have also been researched as an extension of the emerging e-hail or car- the behaviour of these SAVs and observed that technological barriers of completely operational and sharing [13]. Fagnant and Kockelman [14] simulated the behaviour of these SAVs and observed that a ordable autonomous vehicles, as well as regulations regarding commercial automated taxi service technological barriers of completely operational and affordable autonomous vehicles, as well as licensing, are still to be drafted. regulations regarding commercial automated taxi service licensing, are still to be drafted. In the domain of on-demand mobility, Tang et al. [15] have respectively expressed the ability of an In the domain of on-demand mobility, Tang et al. [15] have respectively expressed the ability of overall ITS in improving user behaviour, air quality, travel time, productivity and safety, propelled an overall ITS in improving user behaviour, air quality, travel time, productivity and safety, by revolutions in the fields of electronics, robotics, information technology and cybernetics. Due to propelled by revolutions in the fields of electronics, robotics, information technology and cybernetics. logical and temporal constraints, accurate recognition and distinction between objects and humans on Due to logical and temporal constraints, accurate recognition and distinction between objects and a roadway are most critical for autonomous vehicles on urban roads, followed by complex manoeuvre, humans on a roadway are most critical for autonomous vehicles on urban roads, followed by complex m blocked anoeuvrroutes, e, blocked congested routes, conge trac s operations ted traffic op and erat obeying ions and ob tra ey cin rules g traf [f 16 ic], ru as les [ well 16], as as we the ll overall as the overall sustainability sustainability o of commissioning f commission AVsing from AVs public frominvestments. public investments. The purpose of the current paper is to perform a critical review of peer-reviewed research in the The purpose of the current paper is to perform a critical review of peer-reviewed research in field of vehicular automation, so as to map the status of autonomous urban transportation, and the field of vehicular automation, so as to map the status of autonomous urban transportation, and specifically to ask the question of how driverless vehicles are changing the way we think of artificially intelligent transportation systems. Appl. Syst. Innov. 2020, 3, 1 3 of 21 specifically to ask the question of how driverless vehicles are changing the way we think of artificially Appl. Syst. Innov. 2019, 2, x FOR PEER REVIEW 3 of 20 intelligent transportation systems. The question of the smart cities of the future and technological transformation of urban roadway The question of the smart cities of the future and technological transformation of urban roadway systems is also closely linked with the triple-bottom-line sustainability approach: cost, environment systems is also closely linked with the triple-bottom-line sustainability approach: cost, environment and social interactions [17]. This current study is intended to serve as a platform for a pilot study on and social interactions [17]. This current study is intended to serve as a platform for a pilot study on the exploration of artificially intelligent sustainable urban transport with the city of Abu Dhabi as a the exploration of artificially intelligent sustainable urban transport with the city of Abu Dhabi as a case study. Our keywords: autonomous cars, sustainable transport, smart cities, intelligent urban case study. Our keywords: autonomous cars, sustainable transport, smart cities, intelligent urban transportation and ITS, were entered, and the 22 primary and 79 supplementary studies returned from transportation and ITS, were entered, and the 22 primary and 79 supplementary studies returned Google Scholar and ScienceDirect were analysed, and are described in the following sections. from Google Scholar and ScienceDirect were analysed, and are described in the following sections. 2. Dimensions of Artificially Intelligent Transport Systems 2. Dimensions of Artificially Intelligent Transport Systems This section discusses the primary dimensions of artificially intelligent transport systems (Figure 2) This section discusses the primary dimensions of artificially intelligent transport systems (Figure across the existing literature, as: on-demand mobility, shared autonomous vehicles, autonomous public 2) across the existing literature, as: on-demand mobility, shared autonomous vehicles, autonomous bus transport, lane control and vehicular guidance systems. The benefits of smart technologies and public bus transport, lane control and vehicular guidance systems. The benefits of smart technologies artificially intelligent transport systems for travel time reduction and easing congestions, along with and artificially intelligent transport systems for travel time reduction and easing congestions, along incident management, are also discussed in this section. A general finding was that the majority of with incident management, are also discussed in this section. A general finding was that the majority existing studies on the performance evaluation of such systems largely focused on programming of existing studies on the performance evaluation of such systems largely focused on programming and technological aspects, public law and transport law and profit generation. Although demand and technological aspects, public law and transport law and profit generation. Although demand management was also extensively studied in recent literature, little attention is paid to the long-term management was also extensively studied in recent literature, little attention is paid to the long-term impact of implementing artificially intelligent transport systems and the resulting travel pattern impact of implementing artificially intelligent transport systems and the resulting travel pattern variations, particularly in the context of sustainability performance evaluations. variations, particularly in the context of sustainability performance evaluations. Figure 2. Dimensions of a connected artificially intelligent transport system: on-demand mobility, Figure 2. Dimensions of a connected artificially intelligent transport system: on-demand mobility, smart city-spaces, users, PT and vehicle guidance systems. smart city-spaces, users, PT and vehicle guidance systems. 2.1. On-Demand Mobility Solutions 2.1. On-Demand Mobility Solutions The advances in on-demand mobility have increased their popularity before the introduction The advances in on-demand mobility have increased their popularity before the introduction of of automation (e.g., Uber, Lyft, Hailo etc.). These programmes have been aimed at replacement of automation (e.g., Uber, Lyft, Hailo etc.). These programmes have been aimed at replacement of private automobile trips from vehicle ownership to a rental service that is run on-demand. Fagnant private automobile trips from vehicle ownership to a rental service that is run on-demand. Fagnant and Kockelman [14] have commented on the increasing popularity of these services, for example, the users in the United States double every year or two. Greenblatt and Shaheen [13], in their study reviewing automated, on-demand mobility and the associated environmental impacts, found that around eighty percent or more greenhouse gas (GHG) Appl. Syst. Innov. 2020, 3, 1 4 of 21 and Kockelman [14] have commented on the increasing popularity of these services, for example, the users in the United States double every year or two. Greenblatt and Shaheen [13], in their study reviewing automated, on-demand mobility and the Appl. Syst. Innov. 2019, 2, x FOR PEER REVIEW 4 of 20 associated environmental impacts, found that around eighty percent or more greenhouse gas (GHG) emissions and energy use reduction may be observed with the use of autonomous vehicles. They also emissions and energy use reduction may be observed with the use of autonomous vehicles. They also proposed that social benefits (lower VMTs and ownership reduction), environmental load suppression, proposed that social benefits (lower VMTs and ownership reduction), environmental load land-use and mass transit benefits may be achievable through on-demand mobility. Adding AVs suppression, land-use and mass transit benefits may be achievable through on-demand mobility. may result in further reduction as smaller and more ecient SAVs become a reality, as these vehicles Adding AVs may result in further reduction as smaller and more efficient SAVs become a reality, as may result in overcoming the ride-sharing obstacles of users travelling to a reserved spot to access these vehicles may result in overcoming the ride-sharing obstacles of users travelling to a reserved rental cars. spot to access rental cars. 2.2. Shared Autonomous Mobility Solutions 2.2. Shared Autonomous Mobility Solutions Preceded by existing, commercial, car-sharing rental services (e.g., ZipCar and Car2Go), probably Preceded by existing, commercial, car-sharing rental services (e.g., ZipCar and Car2Go), one of the most widely researched applications of autonomous vehicles as a solution to urban transit probably one of the most widely researched applications of autonomous vehicles as a solution to problems is the SAV. The concept of shared car mobility came from the relatively low ratio between urban transit problems is the SAV. The concept of shared car mobility came from the relatively low the number of private vehicles owned and the actual number of vehicles in use during a typical day. ratio between the number of private vehicles owned and the actual number of vehicles in use during a t Santos ypica et l d al. ay[.2 Sant ] suggested os et al. [2 that ] su appr ggest oximately ed that ap 10% proxi ofm vehicles ately 10% o (models f vehicl > 10 es (m years) odels ar> 1 e on 0 y road ears)on are a o repr n ro esentative ad on a reday pre,swhich entative became day, w slightly hich behigher came s(17%) lightlywhen highe newer r (17%v ) w ehicles hen n (models ewer ve hic 10 les years) (modwer els ≤ e 10 years) were considered. Buying more cars is not the mobility solution for the present trip demands considered. Buying more cars is not the mobility solution for the present trip demands in most areas, in most areas, and a transitioning towards a subscribed or pay-as-you-go vehicle in suitable areas and a transitioning towards a subscribed or pay-as-you-go vehicle in suitable areas may have more may have more economical and environmental viability as the vehicle kilometres travelled are economical and environmental viability as the vehicle kilometres travelled are reduced. Shaheen and reduced. Shaheen and Cohen [18] found that the ride-sharing users increased to be 890,000 in the U.S. Cohen [18] found that the ride-sharing users increased to be 890,000 in the U.S. by 2013 from 12,000 in by 2013 from 12,000 in 2002. A follow-up survey by Shaheen and Cohen [19] found that around 2002. A follow-up survey by Shaheen and Cohen [19] found that around 104,000 shared vehicles are 104,000 shared vehicles are currently serving 4.8 million users across 1531 cities globally, with the currently serving 4.8 million users across 1531 cities globally, with the fleet distribution in Figure 3, and fleet distribution in Figure 3, and with Mexico and Italy accounting for the highest (131:1) and second with Mexico and Italy accounting for the highest (131:1) and second highest (107:1) member-vehicle highest (107:1) member-vehicle ratios, respectively. ratios, respectively. 60% 50% 40% 30% 20% 10% 0% Europe North America Others Carsharing Membership Vehicle Deployment Figure 3. Percentile carsharing membership and vehicle deployment distribution of shared Figure 3. Percentile carsharing membership and vehicle deployment distribution of shared vehicle vehicle services. services. To the end of on-demand, autonomous mobility or aTaxis, as dubbed by Kornhauser [20] To the end of on-demand, autonomous mobility or aTaxis, as dubbed by Kornhauser [20] complementing passenger transit in urban centres, a number of research works have been commissioned. complementing passenger transit in urban centres, a number of research works have been For example, the replacement of private cars in metropolitan areas by SAV taxi services was analysed commissioned. For example, the replacement of private cars in metropolitan areas by SAV taxi by Ford [21] and Kornhauser [20], where passengers were required to travel to taxi stations, similar services was analysed by Ford [21] and Kornhauser [20], where passengers were required to travel to to the Car2Go services. Rigole [22] in the metropolitan Stockholm region, focused on developing taxi stations, similar to the Car2Go services. Rigole [22] in the metropolitan Stockholm region, focused a framework to assist in dynamically allocating SAVs. This was achieved through the use of the on developing a framework to assist in dynamically allocating SAVs. This was achieved through the use of the present roadway network depiction, private commute-demand and multi-criteria evaluation (i.e., fleet size, travelling and waiting times), along with rerouting vacant vehicles based on varying values of these variables. The life-cycle analysis was performed to evaluate the environmental burdens of both traditional (diesel and petrol) and electric cars. Their results showed that by only utilising under 10% parking spots and private vehicles, an on-demand personal SAV transportation system can provide highly Percentile distribution (carsharing growth) Appl. Syst. Innov. 2020, 3, 1 5 of 21 present roadway network depiction, private commute-demand and multi-criteria evaluation (i.e., fleet size, travelling and waiting times), along with rerouting vacant vehicles based on varying values of these variables. The life-cycle analysis was performed to evaluate the environmental burdens of both traditional (diesel and petrol) and electric cars. Their results showed that by only utilising under 10% parking spots and private vehicles, an on-demand personal SAV transportation system can provide highly ecient “door-to-door” services. However, the results of the study are exceedingly hampered by the decision of users to accept ride-sharing. On average around a 15% increase in passenger travel times and 10 min starting time window was expected along with lower trac congestion and environmental impacts, which greatly increased as ride-sharing was replaced with vacant car drives between trips. Results are compatible with the market need for a shared vehicle which gave rise to projects such as ZipCar, for example, which aims for a “simple and responsible urban living” [23], and is supported through smart cards and Android/iPhone apps for identifying vehicle location. A ordability has been cited [1] as the primary driving force for the popularity of ZipCar with millennials, and especially in densely populated urban areas, with approximately 470 cities supported by 10,000 cars or so. Therefore, the potential application of SAVs on the mobility spectrum is promising. Evaluation of the benefits of on-demand autonomous vehicles was conducted by Fagnant and Kockelman [11] and Fagnant and Kockelman [24]. Approximately 8.7% of the distances travelled by the 56,000 SAVs every day were found to be vacant, reduced to 4.5% by allowing for car-sharing. Three market penetration rates: 10%, 50% and 90%; with VMT per AV 10% higher at 90% and 20% higher at 10% market penetration were used, for representing the introduction of new technology and probably a more accepting public perception over the years. The study used the discount rate and dollar-value of passenger vehicle time based upon the cost data (17 USD/person-hr) and the 2020 trac flow patterns, of Schrank et al. [25], as the baseline. Results illustrated benefits in the form of a reduced demand for parking, fuel savings and lowered congestion levels. Shladover et al. [26] proposed that congestion may fall in freeways by approximately 60% if market penetration is assumed at 90%. The study proposed that a careful evaluation of market penetration and public perception is the key to the ecient operation of SAVs. 2.3. Autonomous Public Bus Transport Personalised rapid transport (PRT) is currently being researched as a sustainable transit solution with the use of electric autonomous single cabin vehicles in a pilot study by the Masdar Institute of Science and Technology, Abu Dhabi [27]. However, the future vision of PT in smart cities is expected to harness the potential promised by automation, as discussed earlier. The initial feedback received from the VAA field tests included lesser stress experienced by drivers and the ability of the steering system to aid the drivers in any complex manoeuvres through narrow rights-of-way. Stakeholder responses from the CityMobil2 project [28] have shown as being overwhelmingly predisposed towards autonomous vehicles, with the exception of Vantaa respondents, operating on dedicated lanes as illustrated in Figure 4 [28]. The potential for automated buses to change the fundamental nature of PT has been promising, but design sensibility and accounting for the social implications are considerably important for planners. Understanding that technological Darwinism has produced an overt emphasis over advanced mobility, projects such as Atkin’s Venturer in Bristol for testing the sensors and communication networks of primarily AV buses, ARUP’s UK Autodrive and the 8 million GBP government and consortium members has led the three years “Innovate UK” urban deliveries project in Greenwich. One of the most notable projects on driverless vehicles is the safer, cleaner and innovative transport being researched by the Transport Research Laboratory’s GATEway (Greenwich Automated Transport Environment) project, which uses 8 to 10 passengers’ capacity vehicles. In all phases of trials, public engagement and feedback were collected to further ascertain the perception of the public regarding autonomous vehicles, further discussed in the perception section of this study. Appl. Syst. Innov. 2019, 2, x FOR PEER REVIEW 5 of 20 efficient “door-to-door” services. However, the results of the study are exceedingly hampered by the decision of users to accept ride-sharing. On average around a 15% increase in passenger travel times and 10 min starting time window was expected along with lower traffic congestion and environmental impacts, which greatly increased as ride-sharing was replaced with vacant car drives between trips. Results are compatible with the market need for a shared vehicle which gave rise to projects such as ZipCar, for example, which aims for a “simple and responsible urban living” [23], and is supported through smart cards and Android/iPhone apps for identifying vehicle location. Affordability has been cited [1] as the primary driving force for the popularity of ZipCar with millennials, and especially in densely populated urban areas, with approximately 470 cities supported by 10,000 cars or so. Therefore, the potential application of SAVs on the mobility spectrum is promising. Evaluation of the benefits of on-demand autonomous vehicles was conducted by Fagnant and Kockelman [11] and Fagnant and Kockelman [24]. Approximately 8.7% of the distances travelled by the 56,000 SAVs every day were found to be vacant, reduced to 4.5% by allowing for car-sharing. Three market penetration rates: 10%, 50% and 90%; with VMT per AV 10% higher at 90% and 20% higher at 10% market penetration were used, for representing the introduction of new technology and probably a more accepting public perception over the years. The study used the discount rate and dollar-value of passenger vehicle time based upon the cost data (17 USD/person-hr) and the 2020 traffic flow patterns, of Schrank et al. [25], as the baseline. Results illustrated benefits in the form of a reduced demand for parking, fuel savings and lowered congestion levels. Shladover et al. [26] proposed that congestion may fall in freeways by approximately 60% if market penetration is assumed at 90%. The study proposed that a careful evaluation of market penetration and public perception is the key to the efficient operation of SAVs. 2.3. Autonomous Public Bus Transport Personalised rapid transport (PRT) is currently being researched as a sustainable transit solution with the use of electric autonomous single cabin vehicles in a pilot study by the Masdar Institute of Science and Technology, Abu Dhabi [27]. However, the future vision of PT in smart cities is expected to harness the potential promised by automation, as discussed earlier. The initial feedback received from the VAA field tests included lesser stress experienced by drivers and the ability of the steering system to aid the drivers in any complex manoeuvres through narrow rights-of-way. Stakeholder responses from the CityMobil2 project [28] have shown as being overwhelmingly predisposed towards autonomous vehicles, with the exception of Vantaa respondents, operating on dedicated Appl. Syst. Innov. 2020, 3, 1 6 of 21 lanes as illustrated in Figure 4 [28]. Trikala Vantaa Lausanne La Rochelle 0% 20% 40% 60% 80% 100% Stakeholder response distribution (%) On dedicated lanes Shared roads (other motorised vehicles) Shared roads (pedestrians and cyclists) Figure 4. Stakeholder response distribution towards autonomous bus mode choices. Figure 4. Stakeholder response distribution towards autonomous bus mode choices. It should be noted here that the large-scale application of AV buses has not been implemented for real-world mobility issues. Nonetheless, AV buses can be integrated with the already-used PT priority schemes [29] to tackle congestion issues. These integrated transport systems (i.e., AV buses equipped with transit prioritisation schemes) may have improved ride comfort and travel time over conventional buses, as also demonstrated in a microsimulation study by Nguyen et al. [30]. This may aid in a largescale acceptance of AV buses by the public due to time savings, low cost and high ride comfort [31]. 2.4. Lane Control, Remote Sensing and Vehicular Guidance System: Vehicle to Infrastructure Communication A common feature of paved roadways around the world is the presence of lane control features, such as painted boundaries, lane markers, road signs, etc. The application of ITS for modernising the roadways and improving lane control, dynamic message signs and remote radio-frequency identification (RFID)-type controllable sensors have been cited by researchers [32,33] as a critical feature of modern cities. Better communication between ITS roadway sensors and AVs may also be observed which can reduce average vehicle mileage, crashes and trac congestions. The big data and machine learning revolution [34] is expected to result in intelligent control, trac pattern recognition, embedded electronic markers (similar to barcodes), cognition of roadway users and adapting according to trac behaviour. According to Wang [35], agent-based controlling of transportation management on an on-demand basis may be a more cost-e ective and reliable alternative to trac control while providing flexibility. Wang [10] commented that instead of traditional reliance on trac lights, cooperative intelligent-space technology may be implemented at intersections over ad-hoc networks. Sustainable, ITS-controlled transportation systems may also account for the social and climatic variations [36,37] of automated transit gateways through a comprehensive agent-based model. At present, there are many sensor-based, vehicular guidance systems available for partially automated assistance in navigation, parking assistance, crash aversion and warnings in lane departures, such as the adaptive cruise control (ACC). Shladover et al. [26] estimated that the e ective capacities of roadway lanes may be increased by up to 80% (for a 90% market penetration of ACC), depending upon market shares. KPMG and the Center for Automotive Research [38] found that improvements in trac congestion not only depend upon automated driving, but also ecient roadway sensors providing active in/out vehicle-environment sensing. For instance, vehicle to the roadway (e.g., trac signals) and vehicle to vehicle communications to increase system performance and improve operational safety. Dey et al. [39] note that reliable, ecient and e ective wireless communication between connected vehicles (i.e., vehicles using vehicular guidance system for Level 1–2 automation) or autonomous vehicles and the appropriate infrastructure, is essential for modern transportations Appl. Syst. Innov. 2020, 3, 1 7 of 21 systems. The authors [39] also highlighted the need for a safe, sustainable and secure wireless network for a large-scale operation to tackle mobility issues for real-world applications. Future research on vehicle guidance and vehicle-to-infrastructure communication for ITS application in transportation should acknowledge the safety-related issues as also noted by Ndashimye et al. [40]. Nonetheless, public investments should be directed accordingly at exploring all dimensions of the transport system’s life-cycle, including social factors, environmental, mobility and safety features. 2.5. Trac Congestion and Travel Time Reduction So far, it has been discussed that the majority of travel time and environmental burdens to users on a roadway network come from the supported trac [41], and a transit system may considerably reduce the emissions [42]. The cost of traveller time delays has been found by Rister and Graves [43] as a significant contributor to the total cost of the operation of a roadway. Gupta et al. [44] and Litman [45] have proposed that if the demand–management strategies are not carefully developed, the miles travelled by roadway users are expected to greatly increase. Trac flow patterns are expected to fluctuate if e ective vehicle–environment communication and autonomous vehicles are to become a reality on future smart cities. Research on connected automated vehicles (CAVs), has been proposed in the 2015 Automated Vehicles Symposium [46] to heavily rely on their impact on network routing, trac control and bottleneck capacity recognition. Utilisation of adaptive cruise control and ITS-based monitoring of trac flow have been reported by Atiyeh [47] to increase trac speeds (8–13%) and fuel savings (23–39%). Similar to the agent-based model for ITS roadway systems, described earlier, another study focusing on the design of an agent-based model for SAVs was conducted by Fagnant and Kockelman [14]. They studied the environmental benefits of SAVs comparative to the traditional privately-owned vehicles by mimicking trips based on real-time travelling profiles, departure and destination times and origin and destinations in a grid-based, urban setting. The case study model, run over 100 days, was equipped with the relocation of unused vehicles (which was observed for some vehicles when the model was run), varying fleet size, and service and trip generation distribution and congestion levels. Relocation of SAVs may result in 11% higher travelling as vacant vehicles move to towards the subsequent travel, showing dependence on user concentration, but may replace 10 times of privately owned vehicles. Nonetheless, the model predicted that the block-to-block relocation strategy resulted in more benefits compared to zonal relocation. It may be more suitable for reducing trac congestion if a dynamic SAV relocation strategy is applied followed by the use of an alternate source of energy and newer vehicles in the network to reduce environmental loads. 2.6. Incident Management The conversation on autonomous driving mostly ventures towards crash aversion and incident management, with a common argument given that autonomous vehicles are not expected to tailgate, speed or drive under the influence of drugs and alcohol. The National Highway Trac Safety Administration [3] notes that approximately 90% of crashes are caused by an error on the part of drivers and also that fatigue, distraction and alcohol/drug-use is behind 40% of fatal accidents [11]. Additionally, improper incident management on busy roads and highways contributes to trac congestions that can have adverse impacts on driving behaviours, ride quality and travel time [48]. Nonetheless, Cambridge Systematics [49] noted that the cost of congestion is actually half than that of crashes, which cost approximately 277 billion USD/year. However, as noted by researchers like Farhadi et al. [50] and Campbell et al. [51], it may be dicult for the currently developed autonomous vehicles to safely tackle all crash scenarios and distinguish between objects and roadways. The immediate application of autonomous vehicles for incident management purposes can be in the form of ambulances and other emergency services, provided that public acceptance is achieved [52]. On the other hand, Tupper et al. [53] compared the benefits of the ITS incident management system (trac cameras) against savings from sustainable construction strategies: warm-mix asphalt, pavement Appl. Syst. Innov. 2020, 3, 1 8 of 21 recycling [54,55], reduction of fossil fuel reliance and using regionally-sourced materials [56,57]. Schrank and Lomax [58] have commented that urban areas prone to trac delays by incidents often utilise cameras as an ITS-based incident management strategy. Fries et al. [59] also used trac cameras for incident management, which forms the basis of Tupper et al. [53]’s study. Paramics’ trac micro-simulator with trac pattern, control and geometric information as inputs and EIO-LCA [60] were used to compare savings in fuel consumption and reduction in direct GHG emissions on a six-lane, 16.4 km interstate segment. However, the disturbance of trac flow patterns during construction was not modelled. Mobile6 was used to determine the emission rates which were later used as inputs for Paramics. The simulation was supported by incident modelling through a dedicated algorithm to randomly select incident detection time from 1–5 min historical distribution. In general, the results illustrated that over 3–4 years of operation of ITS incident management strategy, the benefits of this system surpassed those from the sustainable construction-phase strategies. The study also recommended that higher weightage should be assigned to ITS incident management in the Greenroads rating system. 2.7. Tackling Problem of Parking Maintaining equilibrium between the demand and supply of parking spots within urban regions has been critical for many transportation agencies around the city, as parking control forms an integral part of trip flow management. So far, the design of ITS-supported parking management systems has been researched, primarily focused on the close integration of entire roadway systems and parking facilities, thereby discarding the older approach of a secluded evaluation of parking-reserved areas in urban regions. A feasibility study on the use of telematics resources for the data management and logic architecture development of parking systems in the Rio de Janeiro metropolitan area of Niteroi was performed by Vianna et al. [61]. The study found that utilisation of telematics may potentially increase transport system coordination, the eciency of control routine implementation and existing parking spots’ management, which can then reduce pollution and trac congestion. The primary outcome of the study was that the seemingly technological transportation systems are faced by problems that may not be entirely technical. The main obstacles in implementing ITS parking are the defragmentation of planning agencies, lack of policies, social and political issues, control and inspection, administration of various parking types (on-, o - and commercial) and a lack of will from parking operating agencies. Moreover, the parking systems should be planned together with the transportation systems, and cities should focus on a rational assignment of PT systems, the highest-burden of trip distribution load. Another solution to the parking problem, specifically user time wasted in the so-called “cruising for parking” may be avoided by AVs [62,63]. One of the studies, cited earlier [24], has indicated that approximately 10 user-owned vehicles may be replaced by a single SAV. Moreover, SAVs may also decrease the parking cost. Currently parking in general urban regions may cost 1400–3700 USD/spot and 3300–5600 USD/spot in central business districts, as Litman [64] has reported. Shladover et al. [26] commented potentially 250 USD/AV may be saved in parking costs, depending upon sharing rates. 3. Long-Term Implications: The Question of Urban Development and Sustainability 3.1. Urbanism and Urban Sprawl A question that is not usually addressed in the AV-based research for transport systems is what automation in urban mobility will mean for urban development. Urban sprawl and the relocation of residents to cheaper neighbourhoods is a direct result of car-dependence, a behaviour that may witness further increases if AV private vehicles are readily available in the consumer market [65]. A preliminary study on the potential impact of AVs by Carrese et al. [66] was conducted in the city of Rome. The authors found that more than 70% of survey respondents showed interest in relocation to suburban areas based upon the market penetration rate of private AVs. The authors also projected through choice modelling and trac simulation that by the year 2050, around a 50% penetration rate Appl. Syst. Innov. 2020, 3, 1 9 of 21 of private AVs is expected to result in excessive urban sprawl, which in turn will result in heavy trac congestions in the suburban road network. On the other hand, PT usage may see a rapid decline after AV introduction in the private vehicle market [67] if adequate long-term planning measures are not implemented. Transit-oriented development (TOD) can be adopted to reduce the overburden on personalised vehicle transit, and encourage PT uptake if mixed-use, high-density, residential developments are clustered around ecient, fast and reliable PT [68,69]. Lu et al. [70] performed a choice modelling study for the residents of Atlanta, GA, USA. They found that the integration of SAVs with TOD can result in extending the PT service radius of TOD to 3.22 km. This study by Lu et al. [70] assumed that SAVs are expected to serve the first and last-mile need of local residents, and the survey results showed around 39.3% of the residents supported AV integration with TOD. However, Lu et al. [70] also recommended that future research should use real-word trac flow data to develop trac simulation models and investigate the application of AV-based PT system towards solving mobility issues. The AV-based intelligent transport study, currently being performed by the authors of this paper, is aimed towards addressing this recommendation. 3.2. Cost Implications Further application of strategies such as intelligent transportation spaces, automated transit gateways and AVs may result in higher savings. For example, 4000 USD/year/AV from social benefits of parking, fuel eciency, travel time and crash aversion as Fagnant and Kockelman [11] reported. Stephens et al. [71] found that the per-passenger mile cost of AVs is approximately $0.2 in the United States, provided that large-scale ride-sharing can be implemented. These results were considerably lower than the Netherlands-based findings of around $0.1 by Hazan et al. [72] for ride-sharing services using AV passenger cars. However, their study relied on average values and less detailed vehicle data, disregarding some stages of the vehicle life-cycle as well as the overhead cost. Bösch et al. [73] conducted a detailed study on the cost implications of the vehicle fleet automation for the Swedish trac. They found that the life-cycle costs of electric-powered AVs for the di erent vehicle types (shared/taxi vehicles, bus and rails) were lower than the respective types of conventional vehicles (CVs). This was mainly due to the high purchase costs being o set by cost savings in the low fuel, maintenance and insurance costs. In general, these operational level costs (per passenger-kilometre) showed that the AV-based bus, rail and shared taxi cars were 54.7%, 6.4% and 82% lower than their CV-based counterparts, respectively. The only exception was the passenger car where the AV-based car was slightly (4%) more expensive than the conventional cars due to the significantly higher share of the initial purchase cost in the vehicle life-cycle cost. However, the study did not account for the cost externalities, such as costs of growing a PT fleet, mode choice variation, user time costs and vehicle occupancy, as well as the per-unit cost of the environmental emissions. 3.3. Environmental Implications Cities around the world are constantly pursuing energy conservation and climate preservation (i.e., to reduce adverse environmental emissions). Luers et al. [74] projected that in order to avoid the drastic climatic changes, GHG emissions must be considerably reduced. While Shaheen and Lipman [75] found the transportation sector to be the primary source of emissions (14%) around the world, and as such, the sustainability problems in roadway projects should be addressed. Although, there have been various programmes, e.g., Green Highway Partnership and Greenroads [76–78], for promoting partnership and innovation in sustainable transportation planning. Recycling of pavement materials [79,80], e.g., ~99% in Japan [81], and the introduction of newer materials such as warm-mix asphalt [82] have increasingly become popular. However, as Clyne [83] commented, the impacts on the performance of pavements are still uncertain. The triple-bottom-line sustainability approach has also been researched, yet, the implementation of ITS as a tool for increasing sustainability of urban roadways is also uncertain. A life-cycle assessment Appl. Syst. Innov. 2020, 3, 1 10 of 21 study of Tupper et al. [53] on the urban South Carolina interstate found that an ITS strategy can reduce CO emissions (992 tons over 2 years) more than five times than any of the Greenroads-recommended sustainable construction stage strategies, explained earlier. The study emphasised over the importance of ITS as a sustainability tool, especially since the fuel consumption reductions from ITS were 30 times higher than all four strategies combined over eight years of repaving. They further iterated that despite the emphasis of transportation agencies on strategies such as warm-mix asphalt (WMA) [84,85], ITS can potentially provide higher savings. It should be noteworthy that the study did not account for the cost parameter of sustainability; nonetheless, these environmental savings were noted for the implementation of only one ITS strategy. Apart from the long-term implication of ITS in terms of environmental impacts, the life-cycle environmental performance of AVs in the transport system has also been investigated in the literature to a certain degree. A recent trac simulation study by Patella et al. [86] in the city of Rome (assuming 100% battery-electric AV private vehicle penetration rate and 15-year vehicle service life) projected that the GHG emissions in the 100% AV scenario can be 60% (~744 tonne CO eq.) lower than the non-AV scenario over the 15 years period. Noise emissions are also a critical concern in modern societies with an increase in trac levels. Patella et al. [87] investigated the acoustic environment of future cities in a hypothetical scenario (100% electric AV passenger cars in Rome) using trac simulation. They found due to the di erences in propulsion noises between conventional and AV passenger cars a better utilisation of road capacity by AVs (i.e., platooning) and improved trac flow in the AV scenario. It should be noted here that these studies only focused on the automation of private vehicles and disregarded mix-type trac flow situations. Similarly, the mode choice patterns of passengers, ride-sharing and population growth, etc., were not considered. Future work on estimating the environmental impact of AVs can build upon the preliminary results of these studies to develop trac simulation models utilising the real-world trac flow data for the mix-type trac containing origin–destination, occupancy, mode choice and speed–time–volume profiles of all vehicle types. This way the impact of AV on modern cities can be accurately estimated towards aiding long-term policy development by the relevant municipal agencies. 4. Limitations of Past Research and Implementation Barriers The current research identifies that multi-disciplinary problems are associated with making ITS and AVs an everyday reality. Intelligent transportation systems in general, and AVs in particular, have been viewed as a potential driver of the safer, cleaner and more ecient futuristic system. As the field is ever-changing, heavily dependence on the investment decisions of policy-makers, and its benefits and shortcomings deeply embedded in the question of on-demand mobility, uncertainty remains in its overall impact on the life-cycle of the urban roadway system. Hence, based on the agendas identified, Table 1 provided below, covers a comparative review of the studies covered in this paper to identify the important findings and limitations of existing research that can be further investigated in future research work. In general, a review of the above 22 studies shows that if energy-ecient, urban transport systems have to be implemented, factors such as the value of commuting time, knock-ons to economic productivity, stress and time implications on travellers (whilst complex to evaluate), require consideration in the planning of modern ITS and AV-based transportation. Approximately, 18% of the studies focused on ITS, while more than 82% of studies focused on AV application. Current transport literature has largely analysed AV technology for ride-sharing (32% studies), public transportation (28% studies) and personal/on-demand transport (9% studies). The benefits of ITS and AV technology in transportation systems were mainly identified in terms of travel time and congestion (46%), emissions and energy use (36%), cost factors (28%) and road capacity (9%). The major concerns and barriers were stakeholder acceptance (23% studies), and policy and technological shortcomings (18%). The limitations of existing studies that can be investigated in future research are discussed in the next section (Section 5). Appl. Syst. Innov. 2020, 3, 1 11 of 21 Table 1. A critical comparison of the methodologies, findings and limitations of 22 primary research studies covered in this paper. Researchers Methodology Findings Limitations (for Future Works) - AV and ride-sharing interaction - Prioritisation SAVs. needed for AV-PRTs. - Policy and regulations. Liu et al. [1] Analytical - AV-buses need dedicated roads. - Lane-keeping and docking. - Vehicle fatality rates may reduce. - Mode-choices e ects. - Vehicle gap reduction. - Ride-sharing Fagnant and Kockelman - High penetration rate is needed Analytical - Increase in trac due to AV use as [11] - Data privacy, hacking, security, a private vehicle. cost and regulatory concerns exist. - Mode shift e ects. - Ride-sharing will increase. - Electric power grid source. - Lower GHG and energy use. Greenblatt and Shaheen - Growth in car trac. Analytical - Trac congestion will decrease. [13] - Occupancy level. - VMT will change. - Mixed-use trac. - Dynamic ride-share (DRS). - Ride-sharing had positive results. - Life-cycle impacts. - Single SAV can replace 11 CVs. - Simulation of actual data. - SAV adds 10% in travel distance. - Di erent types of vehicles. - Most travellers waited for  5 min. - Real trac speed changes. - SAV sedans decreased energy - SAV relocation for parking. used by 12% (144 GJ) compared to Fagnant and Kockelman - Modelling of longer trips. Agent-based model light-duty vehicles. [14] - SAV occupancy levels. - GHG emissions decreased by - SAV replacement rate. 5.6%. - Alternate SAV fuel. - SO , PM and NO reduced by 2 x - Emission regulations. 19%, 6.5% and 18%, respectively, - Mode choice, destination and due to SAVs. time-of-day models. - AV technology aspects. - SAVs used < 10% of CV parking - Socio-economics. - SAVs provide high service level. - Transport system analysis. - SAVs increased travel time by - Stakeholder engagement. 30%. Rigole [22] Simulation modelling - Benefits of SAVs are dependent on - Dynamic simulation user ’s willingness to - Urbanism impacts. accept ride-sharing. - Safety and legal aspects. - DRS of SAVs reduces travel cost. - Private operators can earn 19% annual return on investment. - Trac emissions. Fagnant and Kockelman Simulation modelling - DRS critical in limiting VMTs. - Life-cycle costs. [24] - DRS reduce waiting by 4.5 min. - Service time reduced by 0.3 min. - ACC had minimal change on - Dynamic response of lane capacity. ACC systems. - Cooperative ACC can increase - Response to a strong Shladover et al. [26] Microscopic simulation lane capacity (almost double) with trac disturbance. high market penetration rate - Emergency stopping conditions. (around 100%). - Restricted trials. - Restricted AV-bus use showed - Regulations. user acceptance. - AV-buses have less likelihood of - Mode-shift and mode choices Alessandrini and Analytical speed-limit violations. - Sustainable development. Mercier-Handisyde [28] - AV-bus showed reduced fuel - Infrastructure design. consumption and emissions. - System-wide integration. Appl. Syst. Innov. 2020, 3, 1 12 of 21 Table 1. Cont. Researchers Methodology Findings Limitations (for Future Works) - AV PT bus platooning is feasible. - Delay impacts of AV PT. - Multiple trac demand. - AV-buses had 60 s wait times and Nguyen et al. [30] Microscopic simulation 44 s travel times. - Vehicle-road surface interactions. - Interpretability of results - Psychological barriers for AVs. among countries. - AV buses can reduce personal costs, congestions, waiting times - Technology awareness López-Lambas and Analytical of respondents. and emissions. Alonso [31] - Vehicle costs, infrastructure costs - Stakeholder engagement. and safety risks exist for AV bus. - Actual performance results. - ITS can be applied for trac data - Technology dependent. collection and collision warning. - Data security. Dey et al. [39] Field experiment - ITS reduce network interruption. - Changes in data network. - ITS ramp metering and speed limits improve travel time by 9%. - Impact of di erent trac - Bus delays reduced by 81.5%. management scenarios. - Trac delays reduced by 29.1%. - Open-source simulation software. Dadashzadeh and Ergun Microscopic simulation - Average speed increase by 12.7%. - Performance testing using several [48] modelling - ITS reduce bottlenecks by 2.8%. merging points. - Fuel use reduced by 78.2%. - Automatic model optimisation. - Emissions reduced by 17.3%. - Respondents expressed negative - Level of awareness. feelings for AV ambulances. - Exposure of people to AV ambulance service. - Respondents from Kazakhstan Zarkeshev and Csiszár less trusting than - Representative population Analytical [52] Hungarian respondents. surveys needed. - Readiness to ride is not influenced - Sensitivity to emergency level by gender. and severity. - ITS trac camera-generated - Trac impacts of construction. Economic input-output higher fuel savings than - Ramp metering. LCA and sustainable road construction. Tupper et al. [53] - Automation of transport. microsimulation - ITS strategy should be weighed - Social factors. modelling more for sustainable roads. - AV impact on PT travel time and services. - AVs favour urban sprawl. - Intra-zonal demand. - Only average trac data - AVs may replace PT in Meyer et al. [65] Transport modelling low-density urban areas. were modelled. - Road capacity. - Cost factors. - AVs may result in urban sprawl. - Travel time costs and fares. - AVs increase travel time (suburb - Small sample size (~200) Analytical (surveys) and to city centre) by 12%, due to considered (i.e., population Carrese et al. [66] trac simulation urban relocation. representation needed). - SAVs reduce travel time by 19%. - Mode choice impacts. - AVs may result in urban sprawl. - Ownership of AVs. - AVs and TOD integration. - SAV integration with AV PT and - Heterogeneity in preferences for transport system. TOD and AVs. Lu et al. [70] Analytical - Real trac data for - Results were dependent on time simulation modelling. savings and productivity - Cost and policy initiatives. opportunities from AV use. Appl. Syst. Innov. 2020, 3, 1 13 of 21 Table 1. Cont. Researchers Methodology Findings Limitations (for Future Works) - Demand-bundling is necessary to - AV impact on parking. maintain the competitiveness of - Mode choice impacts. PT against SAVs and private AV - Government policies. cars in dense urban areas. - Personnel costs. Bösch et al. [73] Analytical - High purchase and cleaning cost - Market price fluctuations. of SAVs is still a deterrence. - Alternate fuel use and costs. - AVs may remain in private use - Emissions results. due to low variable costs. - AVs decrease travel time and - More likely internal combustion increase average vehicle speeds. hybrid electric AVs must - AVs increase VMT on long roads. be modelled. - AVs increase GHG impacts on Trac simulation - AV-specific modelling software Patella et al. [86] construction and modelling should be used. procurement level - Mode-shift, ride-sharing & - AVs decrease environmental population growth impacts impacts at mobility level by 60%. - Central city area benefits from - Modelling of di erent types of Trac simulation lower noise emissions due to the vehicles and mixed trac in Patella et al. [87] modelling high penetration rate of AVs. actual trac fleet. - Technological, policy and user - Pilot projects to analyse the Srinivasan et al. [88] Analytical acceptance are barriers. transport impacts of AVs. - Gasoline hybrid SAVs had better - Carbon taxes. cost and travel time performance - Vehicle occupancy levels. than electric SAVs. - Cost benefit of gasoline hybrid - Trip combinations. Loeb and Kockelman SAVs depends on electric SAV - Trip load reduction. Simulation [89] price and gasoline cost. - Comparison with PT services - Electric SAV capable of and automation. zero-carbon transport if coupled - Emissions and energy use. with a renewable power grid. 4.1. Public Acceptance Manufacturers like Google currently plan to release AVs by the year 2020 [13], which may result in the safer and more ecient use of roadways. The goal of mobility innovation and automation is to transform the travel choices of people, and as such, its implications on urban PT. Automation carries many social prospects in addition to the legal precepts of licensing and security. Future travellers may have the choice of using smartphones, consuming meals and engaging in recreational activities during their private or shared transit. Even though Google has logged over 1.1 million kilometres [90] through AVs on public roads in California, high costs limit the spread of autonomous cars on the consumer market [38,91], a challenge that may best be addressed through autonomous PT if the perception of the public is duly considered, and initiatives to promote a general understanding of autonomous PT (e.g., buses)-mainly safety features are conveyed to the users. Adoption of closely travelling autonomous buses on dedicated lanes may increase the capacity of lanes [92], which may in return mean fuel savings, as less brake-accelerate manoeuvre will be needed. Preliminary data from the CityMobil2 automated bus project has revealed that there is currently a positive attitude from the general public, as Figure 5 [28] illustrates. Appl. Syst. Innov. 2019, 2, x FOR PEER REVIEW 13 of 20 the studies focused on ITS, while more than 82% of studies focused on AV application. Current transport literature has largely analysed AV technology for ride-sharing (32% studies), public transportation (28% studies) and personal/on-demand transport (9% studies). The benefits of ITS and AV technology in transportation systems were mainly identified in terms of travel time and congestion (46%), emissions and energy use (36%), cost factors (28%) and road capacity (9%). The major concerns and barriers were stakeholder acceptance (23% studies), and policy and technological shortcomings (18%). The limitations of existing studies that can be investigated in future research are discussed in the next section (Section 5). 4.1. Public Acceptance Manufacturers like Google currently plan to release AVs by the year 2020 [13], which may result in the safer and more efficient use of roadways. The goal of mobility innovation and automation is to transform the travel choices of people, and as such, its implications on urban PT. Automation carries many social prospects in addition to the legal precepts of licensing and security. Future travellers may have the choice of using smartphones, consuming meals and engaging in recreational activities during their private or shared transit. Even though Google has logged over 1.1 million kilometres [90] through AVs on public roads in California, high costs limit the spread of autonomous cars on the consumer market [38,91], a challenge that may best be addressed through autonomous PT if the perception of the public is duly considered, and initiatives to promote a general understanding of autonomous PT (e.g., buses)-mainly safety features are conveyed to the users. Adoption of closely travelling autonomous buses on dedicated lanes may increase the capacity of lanes [92], which may in return mean fuel savings, as less brake-accelerate manoeuvre will be needed. Preliminary data from the CityMobil2 automated bus project has revealed that there is currently a positive attitude Appl. Syst. Innov. 2020, 3, 1 14 of 21 from the general public, as Figure 5 [28] illustrates. La Rochelle (n = 425) Lausanne (n = 586) Vantaa (n =118) Trikala (n = 470) Coventional Buses Autonomous Buses Figure 5. Public responses on mode choices: conventional vs. autonomous buses under the same Figure 5. Public responses on mode choices: conventional vs. autonomous buses under the same route route parameters parameters 4.2. Policy and Technological Shortcomings 4.2. Policy and Technological Shortcomings Routine infrastructure planning set aside, it is often expected of the government agencies to provide a platform, encourage discussion and dialogue between stakeholders, and commission Routine infrastructure planning set aside, it is often expected of the government agencies to research projects promoting equity, safety and technological integration and development policies as provide a platform, encourage discussion and dialogue between stakeholders, and commission the resea machine-brawn rch projects promoting eq artificial intelligence uity, safety revolution and technologi engulfs cal i transportation ntegration and industry development polic . Algorithmsies as able to the mach crunchine- big brawn data and artslicker ificial int transportation elligence revol pr u omised tion engulf by autonomous s transportati vehicles on indust opens ry. Algori a newthm window s able, to crunch big data and slicker transportation promised by autonomous vehicles opens a new yet due to the substantial uncertainties associated with their long-term deployment impacts, partial window, yet due to the substantial uncertainties associated with their long-term deployment impacts, information makes policies dicult to draft. Srinivasan et al. [88], through a metropolitan case study partial information makes policies difficult to draft. Srinivasan et al. [88], through a metropolitan case from the Netherlands, found that despite the uncertainties associated with AV implementation, there study from the Netherlands, found that despite the uncertainties associated with AV implementation, is a need for encouraging systematic, approach-based pilot projects to study the long-term impacts. there is a need for encouraging systematic, approach-based pilot projects to study the long-term Researchers such as Liu et al. [1] asserted the need for developing a consensus on the transformation of the traditional transport systems through the emergence of vehicle automation technologies. Future research on the autonomous vehicle, and particularly autonomous PT as a mobility solution, should address how these alternate vehicle technologies can be adopted to reduce overburden on personalised vehicle transit and achieve city-wide sustainability goals of municipal authorities in congested and heavy trac density cities around the world. 5. Future Research Directions Although, information sharing initiatives such as Sustainability Mobility and Accessibility Research and Transformation (SMART) [93], have been aimed at carefully analysing the trade-o s between investment decisions, sustainability and innovative urban mobility. This is primarily because infrastructure investments are high budgeted and may become locked-in once the decisions have been made, and cities may be stuck with irreversible bad decisions, which predicament platforms such as SMART aim to avoid. Other programmes such as the global mobility start-up database Mobi [1] and smartphone app-powered commercial door-to-door shuttle service of Transdev also aim to provide an alternate and multi-modal solution to the urban transportation problem. Nevertheless, at present most government agencies around the world have yet to catch up to the technology of automated transit. For example, as Liu et al. [1] report, the vagueness in the Federal Transit Administration and ITS Joint Program Oce-led, on-demand mobility programme’s description of newer multi-modal mobility concepts and the absence of autonomous mobility from the U.S. Department of Transport strategic plan [94]. Due to the decreasing reliance of millennial travellers on single-occupancy vehicles and the divergence of trip load towards PT, researchers such as Liu et al. [1], Alessandrini et al. [27], Lesh [95] and Polzin [96] have commented that transportation agencies should also focus on long-term Number of survey respondents Appl. Syst. Innov. 2020, 3, 1 15 of 21 autonomous transit, SAVs, connected AVs and exclusive rights-of-way for AVs. Moreover, building upon the success of projects like the Morgan Town People mover [97], ideas like automated rapid bus transit, an automated vehicle serving > 15 travellers on dedicated lanes [5,98], may become the new mode choice for mass transit. Although at present the procurement and purchase costs of private and public AVs may be a concern [31] for practical implications, the research covered in Table 1 above shows that these high initial costs may be o set by life-cycle ownership costs, specifically fuel cost savings. Environmental impacts notwithstanding, the travel time and ride comfort factors may be the primary drivers of AV adoption for public transport and ride-sharing transit systems, and as such, should be focused in future research. It is also uncertain how the mode choice of travellers shall be a ected by the introduction of ITS and AV-based transportation systems. Similarly, the detailed impact of AV–PT, private AV cars and SAVs on the daily operation of trac fleet as well as life-cycle implications need to be empirically calculated. Scenario analyses of various AV adoption rates, AV adoption methods and fuel sources should be performed with the simultaneous engagement of stakeholders (government, users and private sectors). Additionally, the following research directions are critical for the real-world application of artificially intelligent and sustainable transport systems in line with the literature studies covered in Table 1. SAV prioritization, vehicle occupancy and sharing technique (dynamic vs. serial ride-sharing). Mode-shift and mode choices. Variation of trac density due to AV use in trac fleet. Renewable electric power grid and alternate fuel sources. Long-term trac and population growth. Use of actual trac count data and growth models for simulations and empirical calculations. High-resolution (per vehicle level) calculation of cost, energy use and pollutant emissions. The penetration rate of vehicles following new emission regulations (e.g., distribution percentages of Euro 1–6 emission standards). System-wide analysis and modelling of the transportation network. Dynamic vehicle simulation and dynamic choice-modelling. Urbanism, urban relocation and zonal developments (TODs) integration with AVs and ITS. Impacts of AV trac on the construction, maintenance and operation of road infrastructures. Impact of market fluctuations in AV prices, fuel prices and government taxes on AV adoption. Diversion of trip load towards AV-based public transport. Increasing the engagement level of public and government agencies in the decision-making process of transport systems to explore the benefits of automation and artificial intelligence. Benefits of autonomous vehicles may result in a greater contribution to the life-cycle sustainability of transportation infrastructures. As stated by Hameed and Hancock [99], unused parking spaces and sustainable transportation initiatives may reduce the GHG emissions and energy consumption needs of urban mobility. Researchers [100,101] have highlighted that the sustainability triple-bottom-line approach should be the primary concern of decision-makers. Even with the uncertainties associated with the deployment and operation of autonomous public transport, it might be apt for the decision- and policy-makers to consider AVs as part of the sustainability investigations of urban public transportation solutions. 6. Conclusions The goal of a future smart city is to have mass and goods mobility primarily supported by an entirely autonomous vehicle system. The goal behind this paper was to critically analyse the state-of-the-art of the research on AVs and ITS transport systems, specifically focused on establishing the research methodologies, main findings and limitations that can be researched in future works. Appl. Syst. Innov. 2020, 3, 1 16 of 21 A total of 22 primary and 79 supplementary researches on the topic were reviewed from peer-reviewed transport literature. The results show that it is expected that in the next thirty years or so, autonomous vehicles may become a reality on public roads. Research has shown that the benefits of AVs and ITS in terms of insurance, congestion and parking costs are in the range of $2960–3900 per year per vehicle. The research on private autonomous cars concluded with a positive outlook on the SAV mobility if electric/hybrid electric autonomous cars were to be the driving force behind the system. The situation of future transport systems is further complicated by the social, environmental, political, and more so, budgetary, constraints of transportation agencies, making sustainability the primary concern. Results of this review paper showed that automated urban mass transit may also be a promising alternative to personalised travel, where users can either subscribe or operate on a pay-as-you-go basis without the need for a driver or attendant for the service operation. The expected benefits of AV–PT are the reduction of land-use, cessation of trac congestion, low user costs, fewer time delays and environmental burdens, mobility solutions to elderly and disabled people in the form of ecient and safe transport. The integration of shared mobility solutions with automated gateways and autonomous PT systems has also been investigated in the literature to certain degrees. Trac simulation, analytical reviews and stated-preference surveys and limited filed data have been used to perform scenario analysis of various AV-based PT and ITS strategies in the existing transport literature. However, the review also found that the majority of literature lacked in concise integration of stakeholder engagement, real-world trac data, cost and emission models and detailed trac simulation modelling for life-cycle implications of ITS and AV transport. Results also show that barriers regarding the technical, technological and policy-making exist for the practical, large-scale implementation of automated transit. Fragmentation of the government agencies involved, the distance between the research and stakeholder spectrums and divided public opinion further complicates the situation. The sustainability triple-bottom-line approach may be used for determining the long-term impacts of autonomous transit. As such, this study, which acts as a platform for describing the current state and need for a life-cycle analysis method for evaluating the application of autonomous rapid PT, identifies that there may be a potential for autonomous bus rapid transit (BRT) services to increase the productivity, energy and fuel savings capacity, as well as safety, of the urban transportation systems. Author Contributions: Conceptualisation, methodology, formal analysis, and writing—original draft, U.H.; supervision, writing—review and editing, and validation, A.W.; review and editing, H.A.J. All authors reviewed the results and approved the final version of the manuscript. All authors have read and agreed to the published version of the manuscript. Funding: This research project is supported by an Australian Government Research Training Program (RTP) scholarship. Conflicts of Interest: The authors declare no conflict of interest. References 1. Liu, R.R.; Fagnant, D.J.; Zhang, W.-B. Beyond single occupancy vehicles: Automated transit and shared mobility. In Road Vehicle Automation 3, 1st ed.; Meyer, G., Beiker, S., Eds.; Springer International Publishing: Cham, Switzerland, 2016; pp. 259–275. 2. Santos, A.; McGuckin, N.; Nakamoto, H.Y.; Gray, D.; Liss, S. Summary of Travel Trends: 2009 National Household Travel Survey; Department of Transportation: Washington, DC, USA, 2011; p. 82. 3. National Highway Trac Safety Administration. Preliminary Statement of Policy Concerning Automated Vehicles; National Highway Trac Safety Administration (NHTSA): Washington, DC, USA, 2013. 4. IHS Automotive. Emerging Technologies: Autonomous Cars—Not if, but When; IHS Automotive: London, UK, 5. Liu, R.R. Automated Transit: Planning, Operation, and Applications; John Wiley & Sons: Hoboken, NJ, USA, 2016; p. 224. Appl. Syst. Innov. 2020, 3, 1 17 of 21 6. Liu, R.R. Spectrum of automated guideway transit (AGT) technology and its applications. In Handbook of Transportation Engineering, Volume II: Applications and Technologies, 2nd ed.; Kutz, M., Ed.; McGraw-Hill Education: New York, NY, USA, 2011. 7. Massey, R. Volvo Develops the ‘No Death’ Car: Vehicles Which Drive Themselves and are Totally Crashproof Could be on British Roads in Eight Years. In Daily Mail; DMG Media: London, UK, 2012. 8. Nissan Motor Company. Nissan Announces Unprecedented Autonomous Drive Benchmarks; Nissan Motor Co. Ltd.: Irvine, CA, USA, 2013. 9. Sergey Brin Hopes People Will Be Driving Google Robot Cars in “Several Years”. Available online: http://www.siliconbeat.com/2012/09/25/sergey-brin-hopes-people-will-be-driving-google-robot- cars-in-several-years (accessed on 18 December 2019). 10. Wang, F.-Y. Driving into the future with ITS. IEEE Intell. Syst. 2006, 21, 94–95. [CrossRef] 11. Fagnant, D.J.; Kockelman, K. Preparing a nation for autonomous vehicles: Opportunities, barriers and policy recommendations. Transp. Res. Part A Policy Pract. 2015, 77, 167–181. [CrossRef] 12. Leonard, J.; How, J.; Teller, S.; Berger, M.; Campbell, S.; Fiore, G.; Fletcher, L.; Frazzoli, E.; Huang, A.; Karaman, S.; et al. A perception-driven autonomous urban vehicle. J. Field Robot. 2008, 25, 727–774. [CrossRef] 13. Greenblatt, J.B.; Shaheen, S. Automated Vehicles, On-Demand Mobility, and Environmental Impacts. Curr. Sustain. Renew. Energy Rep. 2015, 2, 74–81. [CrossRef] 14. Fagnant, D.J.; Kockelman, K.M. The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios. Transp. Res. Part C Emerg. Technol. 2014, 40, 1–13. [CrossRef] 15. Tang, S.; Wang, F.-Y.; Miao, Q. ITSC 05: Current issues and research trends. IEEE Intell. Syst. 2006, 21, 96–102. [CrossRef] 16. Kuwata, Y.; Teo, J.; Fiore, G.; Karaman, S.; Frazzoli, E.; How, J.P. Real-time motion planning with applications to autonomous urban driving. IEEE Trans. Control Syst. Technol. 2009, 17, 1105–1118. [CrossRef] 17. Hasan, U.; Whyte, A.; Al Jassmi, H. Framework for Delivering an AV-based Mass Mobility Solution: Integrating Government-Consumer Actors and Life-cycle Analysis of Transportation Systems. In Proceedings of the 46th European Transport Conference, Dublin, Ireland, 10–12 October 2018. 18. Shaheen, S.; Cohen, A. North American carsharing market trends. In Innovative Mobility Carsharing Outlook: Carsharing Market Overview, Analysis, and Trends; University of Berkeley: Berkeley, CA, USA, 2013; Volume 2, pp. 1–6. 19. Shaheen, S.; Cohen, A. Worldwide carsharing growth continues. In Innovative Mobility Carsharing Outlook: Carsharing Market Overview, Analysis, and Trends; University of Berkeley: Berkeley, CA, USA, 2016; pp. 1–6. 20. Kornhauser, A. Uncongested Mobility for All: New Jersey’s Area-Wide a Taxi System; Princeton University Operations Research and Financial Engineering: Princeton, NJ, USA, 2013; p. 62. 21. Ford, H.J. Shared Autonomous Taxis: Implementing an Ecient Alternative to Automobile Dependency; Princeton University: Princeton, NJ, USA, 2012; p. 104. 22. Rigole, P.-J. Study of a Shared Autonomous Vehicles Based Mobility Solution in Stockholm. Master ’s Thesis, Royal Institute of Technology (KTH), Stockholm, Sweden, 2014; p. 41. 23. Holmes, J. Zipcar—The Promise of Automated Vehicles. In Proceedings of the 4th Automated Vehicle Symposium, Albany, NY, USA, 2 December 2015; Association for Unmanned Vehicle Systems International: Ann Arbor, MI, USA, 2015. 24. Fagnant, D.J.; Kockelman, K.M. Dynamic ride-sharing and optimal fleet sizing for a system of shared autonomous vehicles. In Proceedings of the Transportation Research Board 94th Annual Meeting, Washington, DC, USA, 11–15 January 2015. 25. Schrank, D.; Eisele, B.; Lomax, T. TTI’s 2012 Urban Mobility Report; Texas A&M Transportation Institute: Bryan, TX, USA, 2012. 26. Shladover, S.; Su, D.; Lu, X.-Y. Impacts of cooperative adaptive cruise control on freeway trac flow. Transp. Res. Rec. J. Transp. Res. Board 2012, 2324, 63–70. [CrossRef] 27. Alessandrini, A.; Campagna, A.; Delle Site, P.; Filippi, F.; Persia, L. Automated vehicles and the rethinking of mobility and cities. Transp. Res. Procedia 2015, 5, 145–160. [CrossRef] 28. Alessandrini, A.; Mercier-Handisyde, P. CityMobil 2 experience and recommendations. Retrieved Febr. 2016, 22, 2017. Appl. Syst. Innov. 2020, 3, 1 18 of 21 29. Dadashzadeh, N.; Ergun, M. Spatial bus priority schemes, implementation challenges and needs: An overview and directions for future studies. Public Transp. 2018, 10, 545–570. [CrossRef] 30. Nguyen, T.; Xie, M.; Liu, X.; Arunachalam, N.; Rau, A.; Lechner, B.; Busch, F. Platooning of Autonomous Public Transport Vehicles: The Influence of Ride Comfort on Travel Delay. Sustainability 2019, 11, 5237. [CrossRef] 31. López-Lambas, M.E.; Alonso, A. The Driverless Bus: An Analysis of Public Perceptions and Acceptability. Sustainability 2019, 11, 4986. [CrossRef] 32. Wang, F.-Y.; Zeng, D.; Yang, L. Smart cars on smart roads: An IEEE intelligent transportation systems society update. IEEE Pervasive Comput. 2006, 5, 68–69. [CrossRef] 33. Hasan, U.; Chegenizadeh, A.; Nikraz, H. Nanotechnology future and present in construction industry: Applications in geotechnical engineering. In Advanced Research on Nanotechnology for Civil Engineering Applications; IGI Global: Hershey, PA, USA, 2016; pp. 141–179. 34. Simon, H.A. The Sciences of the Artificial, 3rd ed.; MIT Press: Cambridge, MA, USA, 1996; p. 248. 35. Wang, F.-Y. Agent-based control for networked trac management systems. IEEE Intell. Syst. 2005, 20, 92–96. [CrossRef] 36. Builder, C.H.; Bankes, S.C. Artificial Societies: A Concept for Basic Research on the Societal Impacts of Information Technology; RAND Corporation: Santa Monica, CA, USA, 1991; p. 26. 37. Wang, F.-Y.; Tang, S. Artificial societies for integrated and sustainable development of metropolitan systems. IEEE Intell. Syst. 2004, 19, 82–87. [CrossRef] 38. KPMG; Center for Automotive Research. Self-Driving Cars: The Next Revolution; KPMG and CAR: Ann Arbor, MI, USA, 2012; p. 36. 39. Dey, K.C.; Rayamajhi, A.; Chowdhury, M.; Bhavsar, P.; Martin, J. Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication in a heterogeneous wireless network—Performance evaluation. Transp. Res. Part C Emerg. Technol. 2016, 68, 168–184. [CrossRef] 40. Ndashimye, E.; Ray, S.K.; Sarkar, N.I.; Gutiérrez, J.A. Vehicle-to-infrastructure communication over multi-tier heterogeneous networks: A survey. Comput. Netw. 2017, 112, 144–166. [CrossRef] 41. Huang, Y.; Bird, R.; Bell, M. A comparative study of the emissions by road maintenance works and the disrupted trac using life cycle assessment and micro-simulation. Transp. Res. Part D Transp. Environ. 2009, 14, 197–204. [CrossRef] 42. Zhou, Y.; Tupper, L.; Chowdhury, M.; Klotz, L. Green credits versus environmentally sustainable trac operations: Comparison of contributions to energy and emissions reductions. Transp. Res. Rec. J. Transp. Res. Board 2010, 2163, 103–111. [CrossRef] 43. Rister, B.; Graves, C. The Cost of Construction Delays and Trac Control for Life-Cycle Cost Analysis of Pavements; Kentucky Transportation Center: Lexington, KY, USA, 2002; p. 46. 44. Gupta, S.; Kalmanje, S.; Kockelman, K.M. Road pricing simulations: Trac, land use and welfare impacts for Austin, Texas. Transp. Plan. Technol. 2006, 29, 1–23. [CrossRef] 45. Litman, T. Online Transportation Demand Management Encyclopaedia; Victoria Transportation Policy Institute: Victoria, BC, Canada, 2013. 46. van Arem, B.; Abbas, M.M.; Li, X.; Head, L.; Zhou, X.; Chen, D.; Bertini, R.; Mattingly, S.P.; Wang, H.; Orosz, G. Integrated trac flow models and analysis for automated vehicles. In Road Vehicle Automation 3, 1st ed.; Meyer, G., Beiker, S., Eds.; Springer International Publishing: Cham, Switzerland, 2016; pp. 249–258. 47. Atiyeh, C. Predicting Trac Patterns, One Honda at a Time. MSN Auto, 25 June 2012. 48. Dadashzadeh, N.; Ergun, M. An Integrated Variable Speed Limit and ALINEA Ramp Metering Model in the Presence of High Bus Volume. Sustainability 2019, 11, 6326. [CrossRef] 49. Cambridge Systematics. Crashes vs. Congestion: What’s the Cost to Society? American Automobile Association: Washington, DC, USA, 2011; p. 58. 50. Farhadi, A.; Endres, I.; Hoiem, D.; Forsyth, D. Describing objects by their attributes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (CVPR 2009), Miami, FL, USA, 20–25 June 2009; IEEE: Piscataway, NJ, USA, 2009. 51. Campbell, M.; Egerstedt, M.; How, J.P.; Murray, R.M. Autonomous driving in urban environments: Approaches, lessons and challenges. Philos. Philos. Trans. R. Soc. Lond. A Math. Phys. Eng. Sci. 2010, 368, 4649–4672. [CrossRef] [PubMed] Appl. Syst. Innov. 2020, 3, 1 19 of 21 52. Zarkeshev, A.; Csiszár, C. Are People Ready to Entrust Their Safety to an Autonomous Ambulance as an Alternative and More Sustainable Transportation Mode? Sustainability 2019, 11, 5595. [CrossRef] 53. Tupper, L.L.; Chowdhury, M.A.; Klotz, L.; Fries, R.N. Measuring sustainability: How trac incident management through intelligent transportation systems has greater energy and environmental benefits than common construction-phase strategies for “green” roadways. Int. J. Sustain. Transp. 2012, 6, 282–297. [CrossRef] 54. Hasan, U. Experimental Study on Bentonite Stabilisation Using Construction Waste and Slag. Ph.D. Thesis, Department of Civil Engineering, Curtin University, Perth, Australia, 2015; p. 181. 55. Hasan, U.; Chegenizadeh, A.; Budihardjo, M.A.; Nikraz, H. Experimental Evaluation of Construction Waste and Ground Granulated Blast Furnace Slag as Alternative Soil Stabilisers. Geotech. Geol. Eng. 2016, 34, 1707–1722. [CrossRef] 56. Hasan, U.; Chegenizadeh, A.; Budihardjo, M.; Nikraz, H. A review of the stabilisation techniques on expansive soils. Aust. J. Basic Appl. Sci. 2015, 9, 541–548. 57. Hasan, U.; Chegenizadeh, A.; Budihardjo, M.A.; Nikraz, H. Shear Strength Evaluation of Bentonite Stabilised With Recycled Materials. J. Geoengin. 2016, 11, 59–73. 58. Schrank, D.; Lomax, T. The 2004 Urban Mobility Report; Texas Transportation Institute: College Station, TX, USA, 2004; p. 26. 59. Fries, R.; Chowdhury, M.; Ma, Y. Accelerated incident detection and verification: A benefit to cost analysis of trac cameras. J. Intell. Transp. Syst. 2007, 11, 191–203. [CrossRef] 60. Carnegie Mellon University. EIO-LCA: Free, Fast, Easy Life Cycle Assessment; Carnegie Mellon University: Pittsburgh, PA, USA, 2011. 61. Vianna, M.M.B.; da Silva Portugal, L.; Balassiano, R. Intelligent transportation systems and parking management: Implementation potential in a Brazilian city. Cities 2004, 21, 137–148. [CrossRef] 62. How Vehicle Automation Will Cut Fuel Consumption. Available online: https://www.technologyreview. com/s/425850/how-vehicle-automation-will-cut-fuel-consumption (accessed on 18 December 2019). 63. Shoup, D.C. The High Cost of Free Parking; Planners Press, American Planning Association: Chicago, IL, USA, 2011; p. 808. 64. Litman, T.A. Parking Management: Strategies, Evaluation and Planning; Victoria Transport Policy Institute: Victoria, BC, Canada, 2012; p. 31. 65. Meyer, J.; Becker, H.; Bösch, P.M.; Axhausen, K.W. Autonomous vehicles: The next jump in accessibilities? Res. Transp. Econ. 2017, 62, 80–91. [CrossRef] 66. Carrese, S.; Nigro, M.; Patella, S.M.; Toniolo, E. A preliminary study of the potential impact of autonomous vehicles on residential location in Rome. Res. Transp. Econ. 2019, 75, 55–61. [CrossRef] 67. Rode, P.; Floater, G.; Thomopoulos, N.; Docherty, J.; Schwinger, P.; Mahendra, A.; Fang, W. Accessibility in Cities: Transport and Urban Form. In Disrupting Mobility: Impacts of Sharing Economy and Innovative Transportation on Cities; Meyer, G., Shaheen, S., Eds.; Springer International Publishing: Cham, Switzerland, 2017; pp. 239–273. 68. Buehler, R. Can public transportation compete with automated and connected cars? J. Public Transp. 2018, 21, 2. [CrossRef] 69. Hasan, U.; Whyte, A.; Al Jassmi, H. Life-Cycle Asset Management in Residential Developments Building on Transport System Critical Attributes via a Data-Mining Algorithm. Buildings 2018, 9, 1. [CrossRef] 70. Lu, Z.; Du, R.; Dunham-Jones, E.; Park, H.; Crittenden, J. Data-enabled public preferences inform integration of autonomous vehicles with transit-oriented development in Atlanta. Cities 2017, 63, 118–127. [CrossRef] 71. Stephens, T.S.; Gonder, J.; Chen, Y.; Lin, Z.; Liu, C.; Gohlke, D. Estimated Bounds and Important Factors for Fuel Use and Consumer Costs of Connected and Automated Vehicles; United States Department of Energy: Golden, CO, USA, 2016; p. 48. 72. Hazan, J.; Lang, N.; Ulrich, P.; Chua, J.; Doubara, X.; Ste ens, T. Will Autonomous Vehicles Derail Trains? The Boston Consulting Group: Seattle, WA, USA, 2016; p. 16. 73. Bösch, P.M.; Becker, F.; Becker, H.; Axhausen, K.W. Cost-based analysis of autonomous mobility services. Transp. Policy 2018, 64, 76–91. [CrossRef] 74. Luers, A.L.; Mastrandrea, M.D.; Hayhoe, K.; Frumho , P.C. How to Avoid Dangerous Climate Change: A Target for US Emissions Reductions; Union of Concerned Scientists: Cambridge, MA, USA, 2007; p. 34. Appl. Syst. Innov. 2020, 3, 1 20 of 21 75. Shaheen, S.A.; Lipman, T.E. Reducing greenhouse emissions and fuel consumption: Sustainable approaches for surface transportation. IATSS Res. 2007, 31, 6–20. [CrossRef] 76. Green Highways Partnership. Available online: http://www.greenhighwayspartnership.org/index.php (accessed on 18 December 2019). 77. Anderson, J.; Weiland, C.; Muench, S. Greenroads Manual V. 1.5; University of Washington: Seattle, WA, USA, 78. Clevenger, C.M.; Ozbek, M.E.; Simpson, S. Review of sustainability rating systems used for infrastructure projects. In Proceedings of the 49th ASC Annual International Conference Proceedings, San Luis Obispo, CA, USA, 10–13 April 2013. 79. Hasan, U.; Whyte, A.; Al Jassmi, H. Critical review and methodological issues in integrated life-cycle analysis on road networks. J. Clean. Prod. 2019, 206, 541–558. [CrossRef] 80. Whyte, A. Life-Cycle Assessment of Built Asset Waste Materials-Sustainable Disposal Options, 1st ed.; Lambert Academic Publishing (LAP): Saarbrücken, Germany, 2012; p. 201. 81. Kawakami, A.; Nitta, H.; Kanou, T.; Kubo, K. Study on CO Emissions of Pavement Recycling Methods, 2nd ed.; Public Works Research Institute: Tsukuba-Shi, Japan, 2010; p. 10. 82. Barros, C.B.; Dmytrow, S. Weather-mix asphalt: Warm approach works in California, where climates of all kinds play. Roads Bridges 2009, 47, 26–28. 83. Clyne, T. Warm mix asphalt. In Proceedings of the North Dakota Asphalt Conference, Bismark, ND, USA, 6–7 April 2010. 84. Brown, D. Warm mix: The light is green. Hot Mix Asph. Technol. 2008, 13, 20–32. 85. Walker, D. Gaining experience with warm mix asphalt. Asphalt 2009, 24, 22–28. 86. Patella, S.M.; Scrucca, F.; Asdrubali, F.; Carrese, S. Carbon Footprint of autonomous vehicles at the urban mobility system level: A trac simulation-based approach. Transp. Res. Part D Transp. Environ. 2019, 74, 189–200. [CrossRef] 87. Patella, S.M.; Aletta, F.; Mannini, L. Assessing the impact of Autonomous Vehicles on urban noise pollution. Noise Mapp. 2019, 6, 72. [CrossRef] 88. Srinivasan, S.; Smith, S.; Milakis, D. Implications of vehicle automation for planning. In Road Vehicle Automation 3, 1st ed.; Meyer, G., Beiker, S., Eds.; Springer International Publishing: Cham, Switzerland, 2016; pp. 287–295. 89. Loeb, B.; Kockelman, K.M. Fleet performance and cost evaluation of a shared autonomous electric vehicle (SAEV) fleet: A case study for Austin, Texas. Transp. Res. Part A Policy Pract. 2019, 121, 374–385. [CrossRef] 90. Anthony, S. Google’s Self-Driving Car Passes 700,000 Accident-Free Miles, Can Now Avoid Cyclists, Stop at Railroad Crossings, Extreme Tech; Lendino, J., Ed.; Zi Davis Media: New York City, NY, USA, 2014. 91. Economist Technology Quarterly: Q3. Inside Story: Look, no Hands. In The Economist; The Economist Group: Chicago, IL, USA, 2012; pp. 17–19. 92. Tientrakool, P.; Ho, Y.C.; Maxemchuk, N.F. Highway capacity benefits from using vehicle-to-vehicle communication and sensors for collision avoidance. In Proceedings of the 2011 IEEE Vehicular Technology Conference (VTC Fall), San Francisco, CA, USA, 5–8 September 2011; IEEE: Piscataway, NJ, USA, 2011. 93. Zielinski, S. Whole system connectivity: Focus on user. In Proceedings of the 4th Automated Vehicle Symposium, Albany, NY, USA, 2 December 2015; Association for Unmanned Vehicle Systems International: Ann Arbor, MI, USA, 2015. 94. U.S. Department of Transportation. Beyond Trac: 2045 Final Report; U.S. Department of Transportation: Washington, DC, USA, 2015; p. 230. 95. Lesh, M. Multimodal ITS transit research direction—Integrated, connected, on-demand shared use mobility. In Proceedings of the 4th Automated Vehicle Symposium, Albany, NY, USA, 2 December 2015; Association for Unmanned Vehicle Systems International: Ann Arbor, MI, USA, 2015. 96. Polzin, S.E. How New Technologies and Autonomous Vehicles May Change Public Transportation; PSTA Board Workshop, Center for Urban Transportation Research, University of South Florida: Tampa, FL, USA, 2016. 97. Raney, S.; Young, S.E. Morgantown people mover–updated description. In Proceedings of the Transportation Research Board Annual Meeting 2005, Washington, DC, USA, 15 November 2004. 98. United States Government Publishing Oce. Part 393—Parts and Accessories Necessary for Safe Operation. Code of Federal Regulations: Title 49—Transportation. Available online: https://www.gpo.gov/fdsys/pkg/ CFR-2011-title49-vol5/xml/CFR-2011-title49-vol5-part393.xml (accessed on 18 December 2019). Appl. Syst. Innov. 2020, 3, 1 21 of 21 99. Hameed, F.; Hancock, K. Incorporating Costs of Life-Cycle Impacts into Transportation Program Development. Transp. Res. Rec. J. Transp. Res. Board 2014, 2453, 77–83. [CrossRef] 100. Lopes Gil, J.; Duarte, J.P. Tools for evaluating the sustainability of urban design: A review. Proc. ICE-Urban Des. Plan. 2013, 166, 311–325. 101. Santos, J.; Ferreira, A.; Flintsch, G. A life cycle assessment model for pavement management: Road pavement construction and management in Portugal. Int. J. Pavement Eng. 2015, 16, 315–336. [CrossRef] © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Journal

Applied System InnovationMultidisciplinary Digital Publishing Institute

Published: Dec 19, 2019

References