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Evaluation of Shared Space to Reduce Traffic Congestion

Evaluation of Shared Space to Reduce Traffic Congestion Hindawi Journal of Advanced Transportation Volume 2019, Article ID 6510396, 10 pages https://doi.org/10.1155/2019/6510396 Research Article 1 2 3 Colin Frosch, David Martinelli, and Avinash Unnikrishnan Kimley-Horn, Raleigh, NC, USA Department of Civil and Environmental Engineering, West Virginia University, Morgantown, WV 26506, USA Department of Civil and Environmental Engineering, Portland State University, Portland, OR 97207, USA Correspondence should be addressed to Avinash Unnikrishnan; avinashu@gmail.com Received 27 December 2018; Accepted 26 May 2019; Published 13 June 2019 Academic Editor: Ludovic Leclercq Copyright © 2019 Colin Frosch et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Pedestrian and vehicle interactions oen ft lead to conflicts that bring about safety, traffic congestion, and priority or right of way issues. Common methods used in the past to combat said issues have largely relied on the principle of separating the motions of pedestrians and vehicles by means of bridges, tunnels, signals, and access restrictions. A different approach known as shared space aims to solve the same problems with a less structured and defined environment which instead places more reliance on human interaction and perception. Although it has been used in multiple scenarios across Europe with success, instances of shared spaces in the United States are few. In the past, the success of shared space has mainly focused on safety, aesthetic, and pedestrian use metrics, with little quantitative knowledge regarding the traffic congestion relief benefits. This research focuses on evaluating and quantifying the traffic congestion relief abilities of shared space designs utilizing Vissim traffic microsimulation software and the economic impact these changes can make. A major pedestrian crossing location on West Virginia University’s Downtown campus along a major urban arterial was chosen as the case location upon which the model was to be built. This location posed unique aspects, which made it a prime choice for this research as the major concern for years has been traffic congestion, in addition to pedestrian safety and aesthetic appeal. eTh results of the analysis show that shared space can reduce vehicle travel time by up to 50% and delays by 66%. 1. Introduction rely on tracffi signals, signs, and designated crosswalks to create distinct rules of priority, they must instead be more Shared space traffic designs have been used in an increasing alert to their surroundings and communicate with other number of countries around the world to solve conges- users. Users quickly realize this upon entering a shared space tion, safety, accessibility, and community issues. This design environment and begin to proceed with more caution and make more eye contact between users. es Th e actions by the concept was first pioneered in the Netherlands by Hans Monderman in the late 1900s, but has since been adapted individual user have been scientica fi lly shown to increase to tfi other case-specific areas within Western Europe and the pedestrian safety and decrease the average vehicle speeds most recently in North America. eTh applications of shared [1, 2]. It has also been noted anecdotally that the congestion space range between urban and suburban locations and have and travel time through a specified area has been reduced been found most suitable for areas used by multiple modes of as result of a shared space implementation; however, the transport [1, 2]. data to back up these claims is rare. Finally, shared space Although the specific implementation details of each designs have been shown to greatly increase the public’s perception of an intersection, corridor, or locale through shared space have varied greatly across time and location, the core features of ridding an area of most if not all the use of improved aesthetic elements. These now more traffic signs, demarcations, and traditional controls, to allow welcoming areas have also in turn experienced improved and revitalized economic markets brought about by an multimodal users more independence and less decision making reliance on these engineered elements have been increase in mostly bicycle and pedestrian users to the area presented. By forcing drivers and pedestrians to no longer [3, 4]. 2 Journal of Advanced Transportation As mentioned above, there is an untapped potential associated with reductions in most types of traffic conflicts with shared space to address congestion and traffic delay [7]. In certain cases, e.g., Poynton, shared spaces have led issues that arise at multimodal intersections or corridors. to improved traffic flow. However, to date, there exists little eTh tracffi flow dynamics based on user behavior within a to no published work documenting the quanticfi ation of the shared spacehavethe potentialtoreducethiscongestion congestion benefit (or lack thereof) of shared space, which is and delay problem. Municipalities and design firms have themainfocus ofthis paper. unfortunately not yet turned to shared space as a viable means of addressing congestion problems. A simple, yet effective, 3. Microsimulation Model way to examine tracffi efficiency eeff cts of an implemented shared space design is needed if shared space is to be taken Some researchers have developed microsimulation models, seriously by engineers, planners, government officials, and based on social force theory, to model shared space [17– community members. 19]. Although the methodology used to create said models eo Th bjectiveofthisresearchistoevaluatethe tracffi could be replicated and perhaps expanded to measure traffic congestion and vehicular delay impacts provided by a shared parameters, it has been deemed to be too complex to be space design alternative compared to a traditional design. generalized. Our goal is to develop a framework which can be eTh research develops a microsimulation traffic modelling used by transportation designers and planners to measure the method using existing tools which would provide sufficient congestion impacts of shared space. A modeling technique information to be used in decision making processes based which can capture the shared space dynamics, while also on metrics such as travel time and delay. We present a novel being available to simulate more traditional designs within way to adapt PTV Vissim’s existing capabilities to obtain a small network, is needed in the industry to fill the chasm a conservative estimate of the impact of shared space on between current methods and what is required to advance congestion levels. A case study location known as Grumbein’s shared space as a viable alternative. With this in mind, PTV Island, a major pedestrian crossing on the West Virginia Vissimwaschosentobetheplatform forthismodel dueto University downtown campus, was chosen for its widely known vehicular congestion and delay problem stemming its comprehensiveness, industry prevalence, reputation, and flexibility. However, Vissim (and all other microsimulation from the very cyclical pattern of student pedestrians crossing at a single location. Vehicular and pedestrian traffic volume as platforms) is not explicitly capable of modeling shared space; well as turning movement percentages was collected in order therefore, adaptations to it are necessary. to create a realistic model in PTV Vissim. In addition to the Within PTV Vissim, the current standard to determine data collected, satellite images of the current roadway design rightofwaybetweenvehicles, pedestrians,orvehiclesand and a conceptual design drawing are used as a baseline for the pedestrians is by using conflict areas or priority rules. Both PTV Vissim model. This model is then used to extract travel methods allow the modeler to dictate which direction of flow time and delay in order to assess the impact of a shared space has priority over the other. The flow without priority will design. then yield to the other movement of traffic. er Th e is also the option of not placing any rules regarding priority in the simulation which results in simulated users not seeing each 2. Literature Review other and behaving as such. eTh last option exists only within Since various types of shared space projects have been emerg- the conflict area tool and allows one to express potential ing in several countries, engineering and research studies conflicts to the simulated users, but that it is not defined. are now emerging with some regularity. eTh table, Table 1, In this case, simulated users can see the other users and provides an overview of the benefits observed at shared space know that their movement will conflict. eTh y are then left implementations in Europe and the United States. to their own devices to decide which user shall proceed In addition, quasishared space like zones can be observed first. In the model, this is determined by multiple metrics in major cities such as Barrack Street in Sydney, Chapel Road thatareeithermeasuredorrandomlyassignedtodrivers in Bankstown, and Jack Mundey Place at The Rocks in Sydney and pedestrians, such as which user arrived rst fi , vehicle [5]. Jordan is looking to implement shared space in roads such speed, distance away from the potential conflict, level of as Al Medina Street in Amman which historically have had a driver/pedestrian aggression, etc. These metrics are the same healthy pedestrian automobile mix but have lost their identity as those used in the social force approach models discussed to improve vehicular traffic [13]. earlier [17–19] and the variables that real-life shared space Note that in almost all the above implementations, shared users would encounter in order to make a decision as either space has resulted in improved pedestrian safety which might apedestrianordriver. eTh se factorsmakethisapproach be attributed to speed reductions [14, 15]. Monderman’s theclosest andmostsuitableforsimulating shared space. theory that at lower speeds, pedestrians and drivers would When this undetermined priority was placed between a be able to establish eye contact and “socially interact” to single vehicle and single pedestrian, the users behaved nearly anticipate each other’s behavior and determine their own identically as if the pedestrian was given priority. In the appropriate response has been successful in reducing acci- literature review, it was noted that previous models and dents and injuries [16]. Past implementations also show that efi ld data both show that shared space interactions between shared space has also been successful in both busy streets vehicles and pedestrians can be summarized by vehicles in urban areas as well as rural areas. Shared space is also staying on course and only accelerating or decelerating to Journal of Advanced Transportation 3 Table 1: Observed benefits of shared space implementation. Location Benetfi s Oudehaske and Makkinga, 40% reduction in vehicle speed Netherlands [1, 2] Drachten, Netherlands [5] Reduction in accidents from an average of 11 per year to only 2 in the first year Reduction in accidents on a busy street (12000 vpd) from one per week to none in Bohmte, Germany [6] four weeks Average speed fell to 20 mph, travel time decreased and congestion improved, safety improved with one minor accident in the first 3 years compared to 4-7 in previous Poynton, England [3, 4] years, and economic revitalization with 80% of retailers reporting increased turnover Exhibition Road, London, Reduction of number and severity of conflicts England [7] Reduction in vehicular speeds, improvement of social interactions and awareness, Graz, Austria [8] and no reported accidents in first four months Noordlaren [9] Reduction of speeds by 6-7 mph Bell Street Park, Seattle; Davis Street, Portland; Low vehicle speeds, improved safety Santana Row, Promenade, San Jose, USA [10] Cambridge, MA [11] Increased pedestrian activity and improved vibrancy Market Square, Pittsburgh Improved business and social activity [12] avoid collisions. Note that pedestrians perform the opposite a limited number of crossing paths [18]. This knowledge behavior and vary their route to avoid collisions but remain allows the modeler to choose how many pedestrian crossing at a constant speed. Since pedestrians are unable in PTV paths, and where to place them, in the PTV Vissim model, Vissim to stray from their link’s path, and the vehicles would based on knowledge of current pedestrian trip generators. always yield to them, it was deemed that this undetermined eTh number of paths required to simulate the shared space conflict area was the appropriate and conservative option as closely as possible will vary between sites based on the when attempting to estimate the tracffi delay, but could also be surrounding environment. Note that the pedestrian routes replicated with the pedestrians having outright priority and chosen should represent the shortest path for a pedestrian resulting in similar outcomes. group and will therefore be more likely to cross the space Finally, the lane/link width for vehicles was reduced to at an acute angle rather than at a perfect ninety-degree 6 ft. in order to reduce the distance between pedestrians angle(asis typicalfor designatedcrosswalks).Thecom- and vehicles to trigger a conflict. This allows vehicles in bined paths modeled in PTV Vissim should characterize the model to only yield to pedestrians when a collision will the desired movements of the majority of the pedestrian occur, rather than yielding to all pedestrians in the lane or users. crosswalk, even when a collision will not occur. The result It should also be noted that the spacing of the pedestrian is a smaller headway between vehicles and pedestrians that crossing paths is of critical importance. Due to the setup of exists in real-world shared space scenarios. Likewise, the the PTV Vissim software, there must be sucffi ient space on pedestrian links were also reduced in width to represent the a vehicular travel link between two neighboring pedestrian width of 1-2 pedestrians crossing rather than an entire width crossing points to accommodate the largest class of vehicles of a crosswalk, therefore better representing the space they being modeled. Innately, PTV Vissim does not allow a vehicle actually occupy. to cross a pedestrian path (conflict point) until it knows Theoretically in a shared space environment, pedestrians that the vehicle can traverse that conflict point without arefreetocrossthestreetinwhichever path they desire. being stopped at the next one and consequently block the This lack of designated crossing points leads to an infinite preceding conflict point. Therefore, without enough space number of O-D pairs and crossing points that would need for a vehicle to stop between two conflict points, the vehicle to be addressed in the model. Knowing, however, that must wait until both conflict points are clear. This does not pedestrians will ordinarily take the shortest possible path, mimic the real workings of a shared space, and therefore it is we can reduce the number of crossing points necessary imperative that there be enough space on each vehicular link to include in the model by identifying major origins and between neighboring conflict points for the largest vehicle to destinations and mapping the crossing paths between them. stop. This layout of pedestrian crossing paths allows vehicles Research has shown that in a real shared space scenario, to incrementally progress through the intersection as they vehicle, bicyclist, and pedestrian routes tend to cluster around would in a real shared space. 4 Journal of Advanced Transportation Figure 1: Current Grumbein’s Island configuration on University Avenue in Morgantown, WV (Google). Another main change which was applied in comparison of pedestrians crossing University Avenue directly in front to a traditional traffic model in Vissim was the vehicular of the Mountainlair for a 10-20-minute period between speeds. In this case, our research points us to the fact that classes every hour on Monday, Wednesday, and Fridays and shared space designs, even without the use of posted speed approximately every 90 minutes on Tuesdays and Thursdays. limits, will reduce observed vehicle speeds to the range of 10- The influx of pedestrians to a single unsignalized crosswalk 20 mph. Depending on the location and dynamic aspects of causes drivers to stop and wait for an extended period of time vehicle and pedestrian volume, the speed at any given shared as the headway between pedestrians is typically insufficient spacecanvarybetweenthesetworanges. Sincethismodel to drive through. Long vehicle queues begin to form rapidly will be used to test the congestion relief aspects of shared at this crosswalk as the rate of vehicles entering the queue space, the more conservative value of 10 mph was chosen. from other streets is much greater than the rate at which cars can cross this single crosswalk. Aer ft the approximately 20- minute period is over, the queue begins to recede until normal 4. Case Study traffic flow is resumed after an additional 10-20 minutes pass. This means that at multiple times during the day, there is an eTh case study location selected is located on the Downtown campus of West Virginia University in Morgantown, WV, as almost 40-minute period every hour in which traffic on this showninFigure1.WVU iscurrentlyhometoapproximately street isbacked up,movingslowly, or potentiallystopped for an extended period of time. 30,000 students and another 30,000 citizens within the city limits. er Th e are two main arteries, Beechurst and University During the weeks of March 23rd-30th and March 30th- Avenues, which run in the general North-South direction April 6th, 2014, a two-week-long data collection period was within the Downtown campus. University Avenue bisects the undertaken to provide base data to create a model of the Downtown campus of WVU with many of the freshman current scenario and base the parameters for the shared space model on. Mounted radar vehicle counters and manual dorm facilities as well as the student union, known as the Mountainlair, on one side, and the majority of the academic turning counters, operated by a group of volunteers, were buildings on the opposite side of the street. Therefore, a used to collect the data during this collection period. Figures 2 and 3 present a summary of the data collected pedestrian crosswalk was necessitated, and in the 1930s, a pedestrian island and single unsignalized crosswalk were during this rfi st data collection period, which included vehi- installed at this location under the direction of then facilities cle volumes during peak and nonpeak hours, vehicle turning manager and professor Dr. Grumbein to facilitate the safe ratios at intersections, as well as pedestrian volumes on a crossing of students, faculty, and citizens. As the student pop- 15-minute time interval. This out-of-the-ordinary pedestrian volume time period was chosen to capture the unique volume ulation has greatly increased over the past 80 years, this loca- tion now known as Grumbein’s Island, experiences daily con- changes over the course of time at a university campus. More gestion and traffic delays for drivers on University Avenue. details of the data are provided in [20]. Once the PTV Vissim model was created for the current The arrangement of the WVU facilities with one main “crosswalk” on University Avenue results in a large number congfi uration, and travel time values were measured in Hough St. Hough St. Prospect St. Prospect St. 70 70 College Ave. College Ave. Journal of Advanced Transportation 5 PM Peak North High Street 37 University Avenue 52 Figure 2: Traffic volume. Pedestrians Peak (15 minute increment) North High Street 350 900 20 University Avenue Figure 3: Pedestrian volume. Maiden Ln. Maiden Ln. Willey St. Willey St. 6 Journal of Advanced Transportation Figure 4: Forecasted pedestrian O-D pairs (blue: origin/destination, yellow: routes, and red: shared space boundary) (Google). Figure 5: Designated conflict areas in PTV Vissim simulation. the model, in-person trial travel time runs were taken to four 15-minute time periods were created placing varying verify the model. This verification process was a success and levels of stress on the shared space design during the peak warranted further progress on the model to now include the 15-minute period. The first configuration was based on data shared space design. and conclusions made by observing pedestrians on two To identify pedestrian routes, major origin and desti- separate occasions at the site. eTh second two configurations nation locations were identied fi in the near vicinity of the shifted the peak 15-minute time period intensity slightly. The shared space boundaries. In the case of WVU’s campus, this second level of variation was provided by altering the ratio correlated to mainly academic and student service facilities. of preferred routes between origins and destinations that Pedestrian routes were then transcribed on to a satellite pedestrians would take. This varied the individual volume on each pedestrian link. By combining the two sets of image connecting these designated origins and destinations. scenarios, with three time variations and two route variations, Pedestrian routes rst fi followed sidewalks and pathways to respectively,wewereabletomakeatotalofsix scenariosto get to the approximate boundary of the shared space and test the model. then were made to have a single straight line crossing of the street to the chosen destination. eTh resulting web of O-D pedestrianpairsisshowninFigure4. 5. Analysis of Results In collaboration with WVU administrators and Stantec Consulting Services Inc., a conceptual design rendering was The analyses were based on travel time and delay which can be createdandused as thefoundationforthegeometric layout easily obtained from the PTV VISSIM simulation platform. of the model in PTV VISSIM as shown in Figure 5. eTh results shown below are for the 60-minute PM peak. eTh We created multiple different scenarios of pedestrian trends were consistent for the AM peak also. dispersion for a total of six shared space simulation iterations. Figure 6 shows the average travel time across both First, three different pedestrian volume dispersions over the directions. We can see that the average travel time was lower Journal of Advanced Transportation 7 Vehicular Travel Time Statistics Average Median Stand. Dev. Statistic Current Shared Space_4 Shared Space_1 Shared Space_5 Shared Space_2 Shared Space_6 Shared Space_3 Figure 6: Vehicle travel time statistics. Distribution of Vehicular Travel Times 35.00% 30.00% 25.00% 20.00% 15.00% 10.00% 5.00% 0.00% More 0 50 100 150 200 250 300 350 Time (Seconds) Current Shared Space_4 Shared Space_1 Shared Space_5 Shared Space_2 Shared Space_6 Shared Space_3 Figure 7: Distribution of vehicular travel time. for all 6 iterations of the shared space simulation compared to is counterbalanced by the extreme outliers on the other end the current traffic scenario. On average, across the six shared of the spectrum. Therefore, in the current scenario, drivers space simulations, the travel time for vehicles decreased by have the chance of experiencing very little traffic and getting 13% and by 19% for Simulation 1 which represents the best through quickly, but risk of being stuck in the queue for a long estimate of pedestrian behavior. We can see that the standard time if they do hit the inevitable tracffi . On the other hand, in deviation drastically decreased from the current scenario to the shared space simulation, it is almost certain that a driver’s all six of the shared space simulations. This equates to the travel time would be within a much smaller range. risk of a driver not being able to traverse the prescribed Figure 8 summarizes the vehicular travel time statistics area within the average time. eTh variance of the shared for the peak pedestrian period. First looking at the average space travel time is signicfi antly lower than that of the travel time, we can see that the rfi st shared space model current scenario, meaning drivers could expect a much more decreased by more than 170 seconds. On average, the shared consistent commute within the shared space. The current space models decreased the travel time by 166 seconds, scenario had multiple outliers which stretched the variance which represented 54% of the current travel time for vehicles of the observed travel time data to an extreme extent. during the peak pedestrian period. The median and standard Figure 7 exhibits the presence of extreme outliers, where deviation averaged across all six shared space models also 7.63% of all vehicles traveling along University Avenue in the showed significant drops of 42% and 72%, respectively. current model incurred a total travel time of more than 375 Figure 9 shows the distribution of travel time occurrences seconds. Compare this to the shared space simulations which during the peak pedestrian period. We can see here that more have barely any occurrences above 200 seconds of travel time than 40% of drivers observed in this time period experienced across all six models. It can also be seen that minimum travel a travel time exceeding 375 seconds in the current model. time, or free flow travel time, for the current scenario is lower On the other hand, some of the shared space models had no than the shared space simulations. Remember that this is due observed travel times above 250 seconds. eTh models that did to the inherently lower speed limit set as described in the had very few drivers above this range. methodology within the shared space models at half of the In addition to analyzing and comparing the raw travel current speed limit. This short free flow travel time however time data from the seven separate models, a PERT analysis Frequency Travel Time (Seconds) 8 Journal of Advanced Transportation Vehicular Travel Time Statistics (Peak Pedestrian The PERT analysis was again performed on the delay data Period) observed in the models. The current model expected delay was found to be 157.5 seconds. eTh six shared space models returned PERT expected delay values ranging between 37.5 and67.5seconds,withanaverage of54.6 seconds. This 100 represents a 102.9-second, or 65.34%, drop in delay between thecurrent andsharedspace designs.Again,thisPERT Average Median Stand. Dev. calculation helps show the significant advantage of utilizing Statistic sharedspaceinthisscenario. As we know through experience, Morgantown is a very Current Shared Space_4 dense network of urban streets meeting at signalized and Shared Space_1 Shared Space_5 stop-controlled intersections. Our hypothesis through obser- Shared Space_2 Shared Space_6 vation is that Grumbein’s Island location was not only a Shared Space_3 catalyst for congestion and delay for drivers along University Figure 8: Vehicular travel time statistics (peak pedestrian period). Avenue but also throughout the downtown network. This is evidenced by the long queues exhibited in the current model. These queues would oeft n extend beyond the study area and into surrounding intersections setting off a chain effect was also performed. eTh PERT technique, or Program Eval- uation and Review Technique, is typically used in project leading closer to gridlock. Shared space on the other hand management applications to gain a better understanding drastically reduced the queue lengths, therefore taking away forthe expecteddurationofaprojectorprogram.This the direct interaction between intersections and reducing projected duration is calculated using the minimum, mode, the spread of congestion and grid lock. eTh slower speeds and maximum duration expectations in a weighted average of vehicles in a shared space also help reduce the speed format as shown in the equation below: and promulgation of traffic waves through the system. To truly capture these eeff cts, a larger more complex model is 𝑀 𝑥𝐸𝐷 needed, but our current results certainly are in support of (1) these predictions. 𝑚+4∗ + 6. Conclusions The calculation results indicated that the current scenario wouldhaveameanorexpectedtraveltimeof200 seconds. This study has successfully shown how simulation software Comparatively, the shared space model PERT expected travel such as PTV Vissim can be utilized to simulate and analyze timesrangedbetween 107.5and 127.5seconds,withanoverall shared space as a proposed solution for tracffi congestion average expected value of 115.4 seconds. That is an 84.6- problems. By taking advantage of built-in functionalities second drop, or 42.3% decrease, in expected travel time from within the existing PTV software package, the planner or the current operation. engineer can replicate these efforts to assess unique shared Figure 10 summarizes the statistics for the vehicular delay space designs. While several studies have documented the average over the entire 60 simulation periods. We can see that efficiency and safety improvements from a pedestrian per- the average delay decreased by nearly 50% for the average spective, to the best of our knowledge, this study is among shared space model and by 56% for the primary shared space the rfi st to quantify the potential congestion benefits of shared model. The resulting average delay for all six shared space space. A major pedestrian crossing location on West Virginia modelsis43.57secondsacrosstheentirestudy area.Itshould University’s Downtown campus along a major urban arterial also be noted that the median and standard deviation also was chosen as the case location. This location posed unique decreasedsignicfi antly, andmoresothanthetravel time.By aspects which made it a prime choice for this research as evaluating the distribution of observations in Figure 11 for the major concern for years has been traffic congestion, in the vehicular delay, we can see how all the observations from addition to pedestrian safety and aesthetic appeal. The results the six shared space models are highly congregated towards oftheanalysisshowthatsharedspace canreducevehicle the lowest bin at 15 seconds. The remaining shared space travel time by up to 43% and delays by 66%. Shared space was observations tail off quickly, with only a rare occurrence of an also found to improve the reliability of travel times and hence observation above 150 seconds. In the current operation, on reduce the chances of being stuck in traffic for longer periods. the other hand, the highest percentage of observations does Limitations of modeling shared space using PTV Vis- not fall until the 60- and 75-second bins with approximately sim were also noted in this study. In order to define the 19% of the observations in each. eTh observations for the routes of vehicles and pedestrians, limited discrete origin- current operation also tail off quickly up to the 150-second destination pairs needed to be defined. In scenarios like the point; however, there is again the presence of a large group of case study, this was feasible based on the limited amount extreme outliers. Just like for the travel time statistics, 7.63% of trip generators on the periphery of the shared space for of the observed vehicles experienced a travel delay in excess pedestrians and the intersection of only two main streets of 375 seconds. for vehicles. Theoretically, if a shared space had multiple Travel Time (Seconds) 𝑖𝑚𝑢𝑚𝑚𝑎𝑥 𝑚𝑜𝑑𝑒 𝑖𝑚𝑢𝑚𝑖𝑛 𝑖𝑜𝑛𝑢𝑟𝑎𝑡𝑝𝑒𝑐𝑡𝑒𝑑𝑜𝑟𝑒𝑎𝑛 Journal of Advanced Transportation 9 Distribution of Vehicular Travel Times (Peak Pedestrian Period) 50.00% 45.00% 40.00% 35.00% 30.00% 25.00% 20.00% 15.00% 10.00% 5.00% 0.00% More 0 50 100 150 200 250 300 350 Time (Seconds) Current Shared Space_4 Shared Space_1 Shared Space_5 Shared Space_2 Shared Space_6 Shared Space_3 Figure 9: Distribution of vehicular travel time during peak pedestrian period. Vehicular Delay Statistics Average Median Stand. Dev. Statistic Current Shared Space_4 Shared Space_1 Shared Space_5 Shared Space_2 Shared Space_6 Shared Space_3 Figure 10: Vehicular delay statistics. Distribution of Vehicular Delay 40.00% 35.00% 30.00% 25.00% 20.00% 15.00% 10.00% 5.00% More 0.00% 0 50 100 150 200 250 300 350 Time (Seconds) Current Shared Space_4 Shared Space_1 Shared Space_5 Shared Space_2 Shared Space_6 Shared Space_3 Figure 11: Distribution of vehicular delay. pedestrian or vehicular routes that were too numerous to spaces occur along vehicular corridors where there is a set space appropriately within the conn fi es of the model as route for drivers or at intersections with a few intersecting discussed previously, they would need to be combined which roads. Likewise for pedestrians, the origins and destinations would skew the results. This scenario is not likely to occur in are ordinarily set by store fronts at a minimum which would the real world though. As found in the literature, most shared be spaced sufficiently far apart to allow modeling in PTV Frequency Frequency Delay (Seconds) 10 Journal of Advanced Transportation Vissim. 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Singureanu, “Coping with con- eTh authors declare that there are no conflicts of interest gestion: shared spaces,” Theoretical and Empirical Researches in regarding the publication of this paper. Urban Management,vol.7,no.4, pp.53–62,2012. [17] R. Scho¨nauer,M.Stubenschrott,W.Huang,C.Rudlo,a ff ndM. Acknowledgments Fellendorf, “Modeling concepts for mixed traffic,” Transporta- tion Research Record, no. 2316, pp. 114–121, 2012. The authors would also like to acknowledge the Dwight David [18] C. Rudlo,ff R. Sch ona ¨ uer, and M. Fellendorf, “Comparing Eisenhower Transportation Fellowship Program (DDETFP) calibrated shared space simulation model with real-life data,” for funding this work through DDETFP Graduate Fellow- Transportation Research Record, no. 2390, pp. 44–52, 2013. ship. [19] F.Pascucci,N.C.Rinke,B.Schiermeyer,Friedrich.,andV. 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Shared space in an Australian context,” University of New South Wales, Bachelor of Planning,2009. [6] C. Whitlock, “A green light for common sense,” The Washington Post Foreign Service, 2007, http://www.washingtonpost.com/ wp-dyn/content/article/2007/12/23/AR2007122302487.html. [7] I. Kaparias, M. G. H. Bell, W. Dong et al., “Analysis of pedestrian-vehicle traffic conflicts in street designs with ele- ments of shared space,” Transportation Research Record: Journal of the Transportation Research Board,vol.2393, pp.21–30,2013. [8] W. Fischer, “Shared space in graz (austria),” Eltis, http://www .eltis.org/discover/news/shared-space-graz-austria-0, 2011. [9] Allianz, “Why making streets risky improves road safety,” https://www.allianz.com/en/about us/open-knowledge/topics/ International Journal of Advances in Rotating Machinery Multimedia Journal of The Scientific Journal of Engineering World Journal Sensors Hindawi Hindawi Publishing Corporation Hindawi Hindawi Hindawi Hindawi www.hindawi.com Volume 2018 http://www www.hindawi.com .hindawi.com V Volume 2018 olume 2013 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 Journal of Control Science and Engineering Advances in Civil Engineering Hindawi Hindawi www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 Submit your manuscripts at www.hindawi.com Journal of Journal of Electrical and Computer Robotics Engineering Hindawi Hindawi www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 VLSI Design Advances in OptoElectronics International Journal of Modelling & Aerospace International Journal of Simulation Navigation and in Engineering Engineering Observation Hindawi Hindawi Hindawi Hindawi Volume 2018 Volume 2018 Hindawi www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com www.hindawi.com www.hindawi.com Volume 2018 International Journal of Active and Passive International Journal of Antennas and Advances in Chemical Engineering Propagation Electronic Components Shock and Vibration Acoustics and Vibration Hindawi Hindawi Hindawi Hindawi Hindawi www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Advanced Transportation Hindawi Publishing Corporation

Evaluation of Shared Space to Reduce Traffic Congestion

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Hindawi Publishing Corporation
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Copyright © 2019 Colin Frosch et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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10.1155/2019/6510396
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Abstract

Hindawi Journal of Advanced Transportation Volume 2019, Article ID 6510396, 10 pages https://doi.org/10.1155/2019/6510396 Research Article 1 2 3 Colin Frosch, David Martinelli, and Avinash Unnikrishnan Kimley-Horn, Raleigh, NC, USA Department of Civil and Environmental Engineering, West Virginia University, Morgantown, WV 26506, USA Department of Civil and Environmental Engineering, Portland State University, Portland, OR 97207, USA Correspondence should be addressed to Avinash Unnikrishnan; avinashu@gmail.com Received 27 December 2018; Accepted 26 May 2019; Published 13 June 2019 Academic Editor: Ludovic Leclercq Copyright © 2019 Colin Frosch et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Pedestrian and vehicle interactions oen ft lead to conflicts that bring about safety, traffic congestion, and priority or right of way issues. Common methods used in the past to combat said issues have largely relied on the principle of separating the motions of pedestrians and vehicles by means of bridges, tunnels, signals, and access restrictions. A different approach known as shared space aims to solve the same problems with a less structured and defined environment which instead places more reliance on human interaction and perception. Although it has been used in multiple scenarios across Europe with success, instances of shared spaces in the United States are few. In the past, the success of shared space has mainly focused on safety, aesthetic, and pedestrian use metrics, with little quantitative knowledge regarding the traffic congestion relief benefits. This research focuses on evaluating and quantifying the traffic congestion relief abilities of shared space designs utilizing Vissim traffic microsimulation software and the economic impact these changes can make. A major pedestrian crossing location on West Virginia University’s Downtown campus along a major urban arterial was chosen as the case location upon which the model was to be built. This location posed unique aspects, which made it a prime choice for this research as the major concern for years has been traffic congestion, in addition to pedestrian safety and aesthetic appeal. eTh results of the analysis show that shared space can reduce vehicle travel time by up to 50% and delays by 66%. 1. Introduction rely on tracffi signals, signs, and designated crosswalks to create distinct rules of priority, they must instead be more Shared space traffic designs have been used in an increasing alert to their surroundings and communicate with other number of countries around the world to solve conges- users. Users quickly realize this upon entering a shared space tion, safety, accessibility, and community issues. This design environment and begin to proceed with more caution and make more eye contact between users. es Th e actions by the concept was first pioneered in the Netherlands by Hans Monderman in the late 1900s, but has since been adapted individual user have been scientica fi lly shown to increase to tfi other case-specific areas within Western Europe and the pedestrian safety and decrease the average vehicle speeds most recently in North America. eTh applications of shared [1, 2]. It has also been noted anecdotally that the congestion space range between urban and suburban locations and have and travel time through a specified area has been reduced been found most suitable for areas used by multiple modes of as result of a shared space implementation; however, the transport [1, 2]. data to back up these claims is rare. Finally, shared space Although the specific implementation details of each designs have been shown to greatly increase the public’s perception of an intersection, corridor, or locale through shared space have varied greatly across time and location, the core features of ridding an area of most if not all the use of improved aesthetic elements. These now more traffic signs, demarcations, and traditional controls, to allow welcoming areas have also in turn experienced improved and revitalized economic markets brought about by an multimodal users more independence and less decision making reliance on these engineered elements have been increase in mostly bicycle and pedestrian users to the area presented. By forcing drivers and pedestrians to no longer [3, 4]. 2 Journal of Advanced Transportation As mentioned above, there is an untapped potential associated with reductions in most types of traffic conflicts with shared space to address congestion and traffic delay [7]. In certain cases, e.g., Poynton, shared spaces have led issues that arise at multimodal intersections or corridors. to improved traffic flow. However, to date, there exists little eTh tracffi flow dynamics based on user behavior within a to no published work documenting the quanticfi ation of the shared spacehavethe potentialtoreducethiscongestion congestion benefit (or lack thereof) of shared space, which is and delay problem. Municipalities and design firms have themainfocus ofthis paper. unfortunately not yet turned to shared space as a viable means of addressing congestion problems. A simple, yet effective, 3. Microsimulation Model way to examine tracffi efficiency eeff cts of an implemented shared space design is needed if shared space is to be taken Some researchers have developed microsimulation models, seriously by engineers, planners, government officials, and based on social force theory, to model shared space [17– community members. 19]. Although the methodology used to create said models eo Th bjectiveofthisresearchistoevaluatethe tracffi could be replicated and perhaps expanded to measure traffic congestion and vehicular delay impacts provided by a shared parameters, it has been deemed to be too complex to be space design alternative compared to a traditional design. generalized. Our goal is to develop a framework which can be eTh research develops a microsimulation traffic modelling used by transportation designers and planners to measure the method using existing tools which would provide sufficient congestion impacts of shared space. A modeling technique information to be used in decision making processes based which can capture the shared space dynamics, while also on metrics such as travel time and delay. We present a novel being available to simulate more traditional designs within way to adapt PTV Vissim’s existing capabilities to obtain a small network, is needed in the industry to fill the chasm a conservative estimate of the impact of shared space on between current methods and what is required to advance congestion levels. A case study location known as Grumbein’s shared space as a viable alternative. With this in mind, PTV Island, a major pedestrian crossing on the West Virginia Vissimwaschosentobetheplatform forthismodel dueto University downtown campus, was chosen for its widely known vehicular congestion and delay problem stemming its comprehensiveness, industry prevalence, reputation, and flexibility. However, Vissim (and all other microsimulation from the very cyclical pattern of student pedestrians crossing at a single location. Vehicular and pedestrian traffic volume as platforms) is not explicitly capable of modeling shared space; well as turning movement percentages was collected in order therefore, adaptations to it are necessary. to create a realistic model in PTV Vissim. In addition to the Within PTV Vissim, the current standard to determine data collected, satellite images of the current roadway design rightofwaybetweenvehicles, pedestrians,orvehiclesand and a conceptual design drawing are used as a baseline for the pedestrians is by using conflict areas or priority rules. Both PTV Vissim model. This model is then used to extract travel methods allow the modeler to dictate which direction of flow time and delay in order to assess the impact of a shared space has priority over the other. The flow without priority will design. then yield to the other movement of traffic. er Th e is also the option of not placing any rules regarding priority in the simulation which results in simulated users not seeing each 2. Literature Review other and behaving as such. eTh last option exists only within Since various types of shared space projects have been emerg- the conflict area tool and allows one to express potential ing in several countries, engineering and research studies conflicts to the simulated users, but that it is not defined. are now emerging with some regularity. eTh table, Table 1, In this case, simulated users can see the other users and provides an overview of the benefits observed at shared space know that their movement will conflict. eTh y are then left implementations in Europe and the United States. to their own devices to decide which user shall proceed In addition, quasishared space like zones can be observed first. In the model, this is determined by multiple metrics in major cities such as Barrack Street in Sydney, Chapel Road thatareeithermeasuredorrandomlyassignedtodrivers in Bankstown, and Jack Mundey Place at The Rocks in Sydney and pedestrians, such as which user arrived rst fi , vehicle [5]. Jordan is looking to implement shared space in roads such speed, distance away from the potential conflict, level of as Al Medina Street in Amman which historically have had a driver/pedestrian aggression, etc. These metrics are the same healthy pedestrian automobile mix but have lost their identity as those used in the social force approach models discussed to improve vehicular traffic [13]. earlier [17–19] and the variables that real-life shared space Note that in almost all the above implementations, shared users would encounter in order to make a decision as either space has resulted in improved pedestrian safety which might apedestrianordriver. eTh se factorsmakethisapproach be attributed to speed reductions [14, 15]. Monderman’s theclosest andmostsuitableforsimulating shared space. theory that at lower speeds, pedestrians and drivers would When this undetermined priority was placed between a be able to establish eye contact and “socially interact” to single vehicle and single pedestrian, the users behaved nearly anticipate each other’s behavior and determine their own identically as if the pedestrian was given priority. In the appropriate response has been successful in reducing acci- literature review, it was noted that previous models and dents and injuries [16]. Past implementations also show that efi ld data both show that shared space interactions between shared space has also been successful in both busy streets vehicles and pedestrians can be summarized by vehicles in urban areas as well as rural areas. Shared space is also staying on course and only accelerating or decelerating to Journal of Advanced Transportation 3 Table 1: Observed benefits of shared space implementation. Location Benetfi s Oudehaske and Makkinga, 40% reduction in vehicle speed Netherlands [1, 2] Drachten, Netherlands [5] Reduction in accidents from an average of 11 per year to only 2 in the first year Reduction in accidents on a busy street (12000 vpd) from one per week to none in Bohmte, Germany [6] four weeks Average speed fell to 20 mph, travel time decreased and congestion improved, safety improved with one minor accident in the first 3 years compared to 4-7 in previous Poynton, England [3, 4] years, and economic revitalization with 80% of retailers reporting increased turnover Exhibition Road, London, Reduction of number and severity of conflicts England [7] Reduction in vehicular speeds, improvement of social interactions and awareness, Graz, Austria [8] and no reported accidents in first four months Noordlaren [9] Reduction of speeds by 6-7 mph Bell Street Park, Seattle; Davis Street, Portland; Low vehicle speeds, improved safety Santana Row, Promenade, San Jose, USA [10] Cambridge, MA [11] Increased pedestrian activity and improved vibrancy Market Square, Pittsburgh Improved business and social activity [12] avoid collisions. Note that pedestrians perform the opposite a limited number of crossing paths [18]. This knowledge behavior and vary their route to avoid collisions but remain allows the modeler to choose how many pedestrian crossing at a constant speed. Since pedestrians are unable in PTV paths, and where to place them, in the PTV Vissim model, Vissim to stray from their link’s path, and the vehicles would based on knowledge of current pedestrian trip generators. always yield to them, it was deemed that this undetermined eTh number of paths required to simulate the shared space conflict area was the appropriate and conservative option as closely as possible will vary between sites based on the when attempting to estimate the tracffi delay, but could also be surrounding environment. Note that the pedestrian routes replicated with the pedestrians having outright priority and chosen should represent the shortest path for a pedestrian resulting in similar outcomes. group and will therefore be more likely to cross the space Finally, the lane/link width for vehicles was reduced to at an acute angle rather than at a perfect ninety-degree 6 ft. in order to reduce the distance between pedestrians angle(asis typicalfor designatedcrosswalks).Thecom- and vehicles to trigger a conflict. This allows vehicles in bined paths modeled in PTV Vissim should characterize the model to only yield to pedestrians when a collision will the desired movements of the majority of the pedestrian occur, rather than yielding to all pedestrians in the lane or users. crosswalk, even when a collision will not occur. The result It should also be noted that the spacing of the pedestrian is a smaller headway between vehicles and pedestrians that crossing paths is of critical importance. Due to the setup of exists in real-world shared space scenarios. Likewise, the the PTV Vissim software, there must be sucffi ient space on pedestrian links were also reduced in width to represent the a vehicular travel link between two neighboring pedestrian width of 1-2 pedestrians crossing rather than an entire width crossing points to accommodate the largest class of vehicles of a crosswalk, therefore better representing the space they being modeled. Innately, PTV Vissim does not allow a vehicle actually occupy. to cross a pedestrian path (conflict point) until it knows Theoretically in a shared space environment, pedestrians that the vehicle can traverse that conflict point without arefreetocrossthestreetinwhichever path they desire. being stopped at the next one and consequently block the This lack of designated crossing points leads to an infinite preceding conflict point. Therefore, without enough space number of O-D pairs and crossing points that would need for a vehicle to stop between two conflict points, the vehicle to be addressed in the model. Knowing, however, that must wait until both conflict points are clear. This does not pedestrians will ordinarily take the shortest possible path, mimic the real workings of a shared space, and therefore it is we can reduce the number of crossing points necessary imperative that there be enough space on each vehicular link to include in the model by identifying major origins and between neighboring conflict points for the largest vehicle to destinations and mapping the crossing paths between them. stop. This layout of pedestrian crossing paths allows vehicles Research has shown that in a real shared space scenario, to incrementally progress through the intersection as they vehicle, bicyclist, and pedestrian routes tend to cluster around would in a real shared space. 4 Journal of Advanced Transportation Figure 1: Current Grumbein’s Island configuration on University Avenue in Morgantown, WV (Google). Another main change which was applied in comparison of pedestrians crossing University Avenue directly in front to a traditional traffic model in Vissim was the vehicular of the Mountainlair for a 10-20-minute period between speeds. In this case, our research points us to the fact that classes every hour on Monday, Wednesday, and Fridays and shared space designs, even without the use of posted speed approximately every 90 minutes on Tuesdays and Thursdays. limits, will reduce observed vehicle speeds to the range of 10- The influx of pedestrians to a single unsignalized crosswalk 20 mph. Depending on the location and dynamic aspects of causes drivers to stop and wait for an extended period of time vehicle and pedestrian volume, the speed at any given shared as the headway between pedestrians is typically insufficient spacecanvarybetweenthesetworanges. Sincethismodel to drive through. Long vehicle queues begin to form rapidly will be used to test the congestion relief aspects of shared at this crosswalk as the rate of vehicles entering the queue space, the more conservative value of 10 mph was chosen. from other streets is much greater than the rate at which cars can cross this single crosswalk. Aer ft the approximately 20- minute period is over, the queue begins to recede until normal 4. Case Study traffic flow is resumed after an additional 10-20 minutes pass. This means that at multiple times during the day, there is an eTh case study location selected is located on the Downtown campus of West Virginia University in Morgantown, WV, as almost 40-minute period every hour in which traffic on this showninFigure1.WVU iscurrentlyhometoapproximately street isbacked up,movingslowly, or potentiallystopped for an extended period of time. 30,000 students and another 30,000 citizens within the city limits. er Th e are two main arteries, Beechurst and University During the weeks of March 23rd-30th and March 30th- Avenues, which run in the general North-South direction April 6th, 2014, a two-week-long data collection period was within the Downtown campus. University Avenue bisects the undertaken to provide base data to create a model of the Downtown campus of WVU with many of the freshman current scenario and base the parameters for the shared space model on. Mounted radar vehicle counters and manual dorm facilities as well as the student union, known as the Mountainlair, on one side, and the majority of the academic turning counters, operated by a group of volunteers, were buildings on the opposite side of the street. Therefore, a used to collect the data during this collection period. Figures 2 and 3 present a summary of the data collected pedestrian crosswalk was necessitated, and in the 1930s, a pedestrian island and single unsignalized crosswalk were during this rfi st data collection period, which included vehi- installed at this location under the direction of then facilities cle volumes during peak and nonpeak hours, vehicle turning manager and professor Dr. Grumbein to facilitate the safe ratios at intersections, as well as pedestrian volumes on a crossing of students, faculty, and citizens. As the student pop- 15-minute time interval. This out-of-the-ordinary pedestrian volume time period was chosen to capture the unique volume ulation has greatly increased over the past 80 years, this loca- tion now known as Grumbein’s Island, experiences daily con- changes over the course of time at a university campus. More gestion and traffic delays for drivers on University Avenue. details of the data are provided in [20]. Once the PTV Vissim model was created for the current The arrangement of the WVU facilities with one main “crosswalk” on University Avenue results in a large number congfi uration, and travel time values were measured in Hough St. Hough St. Prospect St. Prospect St. 70 70 College Ave. College Ave. Journal of Advanced Transportation 5 PM Peak North High Street 37 University Avenue 52 Figure 2: Traffic volume. Pedestrians Peak (15 minute increment) North High Street 350 900 20 University Avenue Figure 3: Pedestrian volume. Maiden Ln. Maiden Ln. Willey St. Willey St. 6 Journal of Advanced Transportation Figure 4: Forecasted pedestrian O-D pairs (blue: origin/destination, yellow: routes, and red: shared space boundary) (Google). Figure 5: Designated conflict areas in PTV Vissim simulation. the model, in-person trial travel time runs were taken to four 15-minute time periods were created placing varying verify the model. This verification process was a success and levels of stress on the shared space design during the peak warranted further progress on the model to now include the 15-minute period. The first configuration was based on data shared space design. and conclusions made by observing pedestrians on two To identify pedestrian routes, major origin and desti- separate occasions at the site. eTh second two configurations nation locations were identied fi in the near vicinity of the shifted the peak 15-minute time period intensity slightly. The shared space boundaries. In the case of WVU’s campus, this second level of variation was provided by altering the ratio correlated to mainly academic and student service facilities. of preferred routes between origins and destinations that Pedestrian routes were then transcribed on to a satellite pedestrians would take. This varied the individual volume on each pedestrian link. By combining the two sets of image connecting these designated origins and destinations. scenarios, with three time variations and two route variations, Pedestrian routes rst fi followed sidewalks and pathways to respectively,wewereabletomakeatotalofsix scenariosto get to the approximate boundary of the shared space and test the model. then were made to have a single straight line crossing of the street to the chosen destination. eTh resulting web of O-D pedestrianpairsisshowninFigure4. 5. Analysis of Results In collaboration with WVU administrators and Stantec Consulting Services Inc., a conceptual design rendering was The analyses were based on travel time and delay which can be createdandused as thefoundationforthegeometric layout easily obtained from the PTV VISSIM simulation platform. of the model in PTV VISSIM as shown in Figure 5. eTh results shown below are for the 60-minute PM peak. eTh We created multiple different scenarios of pedestrian trends were consistent for the AM peak also. dispersion for a total of six shared space simulation iterations. Figure 6 shows the average travel time across both First, three different pedestrian volume dispersions over the directions. We can see that the average travel time was lower Journal of Advanced Transportation 7 Vehicular Travel Time Statistics Average Median Stand. Dev. Statistic Current Shared Space_4 Shared Space_1 Shared Space_5 Shared Space_2 Shared Space_6 Shared Space_3 Figure 6: Vehicle travel time statistics. Distribution of Vehicular Travel Times 35.00% 30.00% 25.00% 20.00% 15.00% 10.00% 5.00% 0.00% More 0 50 100 150 200 250 300 350 Time (Seconds) Current Shared Space_4 Shared Space_1 Shared Space_5 Shared Space_2 Shared Space_6 Shared Space_3 Figure 7: Distribution of vehicular travel time. for all 6 iterations of the shared space simulation compared to is counterbalanced by the extreme outliers on the other end the current traffic scenario. On average, across the six shared of the spectrum. Therefore, in the current scenario, drivers space simulations, the travel time for vehicles decreased by have the chance of experiencing very little traffic and getting 13% and by 19% for Simulation 1 which represents the best through quickly, but risk of being stuck in the queue for a long estimate of pedestrian behavior. We can see that the standard time if they do hit the inevitable tracffi . On the other hand, in deviation drastically decreased from the current scenario to the shared space simulation, it is almost certain that a driver’s all six of the shared space simulations. This equates to the travel time would be within a much smaller range. risk of a driver not being able to traverse the prescribed Figure 8 summarizes the vehicular travel time statistics area within the average time. eTh variance of the shared for the peak pedestrian period. First looking at the average space travel time is signicfi antly lower than that of the travel time, we can see that the rfi st shared space model current scenario, meaning drivers could expect a much more decreased by more than 170 seconds. On average, the shared consistent commute within the shared space. The current space models decreased the travel time by 166 seconds, scenario had multiple outliers which stretched the variance which represented 54% of the current travel time for vehicles of the observed travel time data to an extreme extent. during the peak pedestrian period. The median and standard Figure 7 exhibits the presence of extreme outliers, where deviation averaged across all six shared space models also 7.63% of all vehicles traveling along University Avenue in the showed significant drops of 42% and 72%, respectively. current model incurred a total travel time of more than 375 Figure 9 shows the distribution of travel time occurrences seconds. Compare this to the shared space simulations which during the peak pedestrian period. We can see here that more have barely any occurrences above 200 seconds of travel time than 40% of drivers observed in this time period experienced across all six models. It can also be seen that minimum travel a travel time exceeding 375 seconds in the current model. time, or free flow travel time, for the current scenario is lower On the other hand, some of the shared space models had no than the shared space simulations. Remember that this is due observed travel times above 250 seconds. eTh models that did to the inherently lower speed limit set as described in the had very few drivers above this range. methodology within the shared space models at half of the In addition to analyzing and comparing the raw travel current speed limit. This short free flow travel time however time data from the seven separate models, a PERT analysis Frequency Travel Time (Seconds) 8 Journal of Advanced Transportation Vehicular Travel Time Statistics (Peak Pedestrian The PERT analysis was again performed on the delay data Period) observed in the models. The current model expected delay was found to be 157.5 seconds. eTh six shared space models returned PERT expected delay values ranging between 37.5 and67.5seconds,withanaverage of54.6 seconds. This 100 represents a 102.9-second, or 65.34%, drop in delay between thecurrent andsharedspace designs.Again,thisPERT Average Median Stand. Dev. calculation helps show the significant advantage of utilizing Statistic sharedspaceinthisscenario. As we know through experience, Morgantown is a very Current Shared Space_4 dense network of urban streets meeting at signalized and Shared Space_1 Shared Space_5 stop-controlled intersections. Our hypothesis through obser- Shared Space_2 Shared Space_6 vation is that Grumbein’s Island location was not only a Shared Space_3 catalyst for congestion and delay for drivers along University Figure 8: Vehicular travel time statistics (peak pedestrian period). Avenue but also throughout the downtown network. This is evidenced by the long queues exhibited in the current model. These queues would oeft n extend beyond the study area and into surrounding intersections setting off a chain effect was also performed. eTh PERT technique, or Program Eval- uation and Review Technique, is typically used in project leading closer to gridlock. Shared space on the other hand management applications to gain a better understanding drastically reduced the queue lengths, therefore taking away forthe expecteddurationofaprojectorprogram.This the direct interaction between intersections and reducing projected duration is calculated using the minimum, mode, the spread of congestion and grid lock. eTh slower speeds and maximum duration expectations in a weighted average of vehicles in a shared space also help reduce the speed format as shown in the equation below: and promulgation of traffic waves through the system. To truly capture these eeff cts, a larger more complex model is 𝑀 𝑥𝐸𝐷 needed, but our current results certainly are in support of (1) these predictions. 𝑚+4∗ + 6. Conclusions The calculation results indicated that the current scenario wouldhaveameanorexpectedtraveltimeof200 seconds. This study has successfully shown how simulation software Comparatively, the shared space model PERT expected travel such as PTV Vissim can be utilized to simulate and analyze timesrangedbetween 107.5and 127.5seconds,withanoverall shared space as a proposed solution for tracffi congestion average expected value of 115.4 seconds. That is an 84.6- problems. By taking advantage of built-in functionalities second drop, or 42.3% decrease, in expected travel time from within the existing PTV software package, the planner or the current operation. engineer can replicate these efforts to assess unique shared Figure 10 summarizes the statistics for the vehicular delay space designs. While several studies have documented the average over the entire 60 simulation periods. We can see that efficiency and safety improvements from a pedestrian per- the average delay decreased by nearly 50% for the average spective, to the best of our knowledge, this study is among shared space model and by 56% for the primary shared space the rfi st to quantify the potential congestion benefits of shared model. The resulting average delay for all six shared space space. A major pedestrian crossing location on West Virginia modelsis43.57secondsacrosstheentirestudy area.Itshould University’s Downtown campus along a major urban arterial also be noted that the median and standard deviation also was chosen as the case location. This location posed unique decreasedsignicfi antly, andmoresothanthetravel time.By aspects which made it a prime choice for this research as evaluating the distribution of observations in Figure 11 for the major concern for years has been traffic congestion, in the vehicular delay, we can see how all the observations from addition to pedestrian safety and aesthetic appeal. The results the six shared space models are highly congregated towards oftheanalysisshowthatsharedspace canreducevehicle the lowest bin at 15 seconds. The remaining shared space travel time by up to 43% and delays by 66%. Shared space was observations tail off quickly, with only a rare occurrence of an also found to improve the reliability of travel times and hence observation above 150 seconds. In the current operation, on reduce the chances of being stuck in traffic for longer periods. the other hand, the highest percentage of observations does Limitations of modeling shared space using PTV Vis- not fall until the 60- and 75-second bins with approximately sim were also noted in this study. In order to define the 19% of the observations in each. eTh observations for the routes of vehicles and pedestrians, limited discrete origin- current operation also tail off quickly up to the 150-second destination pairs needed to be defined. In scenarios like the point; however, there is again the presence of a large group of case study, this was feasible based on the limited amount extreme outliers. Just like for the travel time statistics, 7.63% of trip generators on the periphery of the shared space for of the observed vehicles experienced a travel delay in excess pedestrians and the intersection of only two main streets of 375 seconds. for vehicles. Theoretically, if a shared space had multiple Travel Time (Seconds) 𝑖𝑚𝑢𝑚𝑚𝑎𝑥 𝑚𝑜𝑑𝑒 𝑖𝑚𝑢𝑚𝑖𝑛 𝑖𝑜𝑛𝑢𝑟𝑎𝑡𝑝𝑒𝑐𝑡𝑒𝑑𝑜𝑟𝑒𝑎𝑛 Journal of Advanced Transportation 9 Distribution of Vehicular Travel Times (Peak Pedestrian Period) 50.00% 45.00% 40.00% 35.00% 30.00% 25.00% 20.00% 15.00% 10.00% 5.00% 0.00% More 0 50 100 150 200 250 300 350 Time (Seconds) Current Shared Space_4 Shared Space_1 Shared Space_5 Shared Space_2 Shared Space_6 Shared Space_3 Figure 9: Distribution of vehicular travel time during peak pedestrian period. Vehicular Delay Statistics Average Median Stand. Dev. Statistic Current Shared Space_4 Shared Space_1 Shared Space_5 Shared Space_2 Shared Space_6 Shared Space_3 Figure 10: Vehicular delay statistics. Distribution of Vehicular Delay 40.00% 35.00% 30.00% 25.00% 20.00% 15.00% 10.00% 5.00% More 0.00% 0 50 100 150 200 250 300 350 Time (Seconds) Current Shared Space_4 Shared Space_1 Shared Space_5 Shared Space_2 Shared Space_6 Shared Space_3 Figure 11: Distribution of vehicular delay. pedestrian or vehicular routes that were too numerous to spaces occur along vehicular corridors where there is a set space appropriately within the conn fi es of the model as route for drivers or at intersections with a few intersecting discussed previously, they would need to be combined which roads. Likewise for pedestrians, the origins and destinations would skew the results. This scenario is not likely to occur in are ordinarily set by store fronts at a minimum which would the real world though. As found in the literature, most shared be spaced sufficiently far apart to allow modeling in PTV Frequency Frequency Delay (Seconds) 10 Journal of Advanced Transportation Vissim. Bicycle users were also not incorporated into this mobility/articles/-why-making-streets-risky-improves-road- safety.html/, 2012. modelastheywerenot observed toconstitute asignicfi ant portion of the total users. If bicycles were incorporated, [10] G. Behrens, “Sharing the street: shared space in an american context,” University of Washington, Urban Planning,2014. it is anticipated that the overall traffic flow and resulting travel time and delay statistics would vary as bicycle users [11] P. Langdon, “US shared space: starting small,” Better Cities inasharedspace sharesomeofthecharacteristicsofboth &Towns, 2010, http://bettercities.net/article/us-shared-space- starting-small-13673. pedestrians and vehicles. [12] T. Snyder, “Bikes, cars, and people co-exist on pittsburgh’s shared streets,” Streetsblog USA, 2014, http://usa.streetsblog.org/ Data Availability 2014/07/03/bikes-cars-and-people-co-exist-on-pittsburghs- shared-streets/. 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Singureanu, “Coping with con- eTh authors declare that there are no conflicts of interest gestion: shared spaces,” Theoretical and Empirical Researches in regarding the publication of this paper. Urban Management,vol.7,no.4, pp.53–62,2012. [17] R. Scho¨nauer,M.Stubenschrott,W.Huang,C.Rudlo,a ff ndM. Acknowledgments Fellendorf, “Modeling concepts for mixed traffic,” Transporta- tion Research Record, no. 2316, pp. 114–121, 2012. The authors would also like to acknowledge the Dwight David [18] C. Rudlo,ff R. Sch ona ¨ uer, and M. Fellendorf, “Comparing Eisenhower Transportation Fellowship Program (DDETFP) calibrated shared space simulation model with real-life data,” for funding this work through DDETFP Graduate Fellow- Transportation Research Record, no. 2390, pp. 44–52, 2013. ship. [19] F.Pascucci,N.C.Rinke,B.Schiermeyer,Friedrich.,andV. 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