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Technology acceptance comparison between on-road dynamic message sign and on-board human machine interface for connected vehicle-based variable speed limit in fog area

Technology acceptance comparison between on-road dynamic message sign and on-board human machine... Purpose – Connected vehicle-based variable speed limit (CV-VSL) systems in fog area use multi-source detection data to indicate drivers to make uniform change in speed when low visibility conditions suddenly occur. The purpose of the speed limit is to make the driver’s driving behavior more consistent, so as to improve traffic safety and relieve traffic congestion. The on-road dynamic message sign (DMS) and on-board human–machine interface (HMI) are two types of warning technologies for CV-VSL systems. This study aims to analyze drivers’ acceptance of the two types of warning technologies in fog area and its influencing factors. Design/methodology/approach – This study developed DMS and on-board HMI for the CV-VSL system in fog area on a driving simulator. The DMS and on-board HMI provided the driver with weather and speed limit information. In all, 38 participants participated in the experiment and completed questionnaires on drivers’ basic information, perceived usefulness and ease of use of the CV-VSL systems. Technology acceptance model (TAM) was developed to evaluate the drivers’ acceptance of CV-VSL systems. A variance analysis method was used to study the influencing factors of drivers’ acceptance including drivers’ characteristics, technology types and fog density. Findings – The results showed that drivers’ acceptance of on-road DMS was significantly higher than that of on-board HMI. The fog density had no significant effect on drivers’ acceptance of on-road DMS or on-board HMI. Drivers’ gender, age, driving year and driving personality were associated with the acceptance of the two CV-VSL technologies differently. This study is beneficial to the functional improvement of on-road DMS, on-board HMI and their market prospects. Originality/value – Previous studies have been conducted to evaluate the effectiveness of CV-VSL systems. However, there were rare studies focused on the drivers’ attitude toward using which was also called as acceptance of the CV-VSL systems. Therefore, this research calculated the drivers’ acceptance of two normally used CV-VSL systems including on-road DMS and on-board HMI using TAM. Furthermore, variance analysis was conducted to explore whether the factors such as drivers’ characteristics (gender, age, driving year and driving personality), technology types and fog density affected the drivers’ acceptance of the CV-VSL systems. Keywords Technology acceptance model (TAM), Connected vehicle (CV), Dynamic message sign (DMS), Human machine interface (HMI), Variable speed limit (VSL) Paper type Research paper Several measures have been made to reduce traffic crash 1. Introduction frequency in fog area such as fog detection and warning Fog reduces visibility on roads which is a critical factor in systems, low visibility driving safety campaigns, and driver drivers’ perceptions of the driving environment. Crashes are training. Some studies have further attempted to address the always possible in fog area because of drivers’ failure to issue of fog and its impact on highway safety with connected maintain safe following distances under adverse weather vehicle technologies (Boyle and Mannering, 2004). Nowadays, conditions according to the World Health Organization the fog warning system using connected vehicle technology is (2016). Fog is likely to have played a role in 20,159 police- reported fatal crashes that occurred in China in 2017 by the Ministry of Public Security Traffic Management Bureau © Jia Li, Wenxiang Xu and Xiaohua Zhao. Published in Journal of (2017). Intelligent and Connected Vehicles. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial The current issue and full text archive of this journal is available on and non-commercial purposes), subject to full attribution to the original Emerald Insight at: www.emeraldinsight.com/2399-9802.htm publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode Journal of Intelligent and Connected Vehicles Received 28 December 2018 2/2 (2019) 33–40 Revised 26 May 2019 Emerald Publishing Limited [ISSN 2399-9802] [DOI 10.1108/JICV-12-2018-0016] Accepted 27 July 2019 33 On-board human machine interface Journal of Intelligent and Connected Vehicles Jia Li, Wenxiang Xu and Xiaohua Zhao Volume 2 · Number 2 · 2019 · 33–40 widely used. All countries in the world have taken measures to transmission between driver and vehicle, and the interaction of improve the driving safety of highway in foggy days by fog on-board systems with the outside world based on connected warning system. Connected vehicle used advanced wireless vehicle and big data technology will become more powerful (Li communication and a new generation of internet technology, et al., 2008). For complex human-machine interaction implemented vehicle and road dynamic real-time information scenarios, information needs to be intuitive, precise and clear, interaction all around, allowed vehicles to talk to one another, with the goal of assisting driving, ensuring driving safety and to transportation infrastructure, to pedestrians, cyclists and reducing the driving burden (Zhang, 2014). passengers in a cooperative manner, and carried out vehicle On-road DMS and on-board HMI are two types of warning active safety control and road collaborative management on the technologies for CV-VSL systems, however, drivers’ basis of the whole space-time dynamic traffic information acceptance of these two technologies are rarely analyzed. The collection and integration, fully realized the effective purpose of this study is to: coordination of human, vehicle and road to form a safe, 1 analyze the drivers’ acceptance of the CV-VSL systems efficient and environmentally friendly road traffic system including on-road DMS and on-board HMI; and (McGurrin et al.,2012). 2 explore factors affecting drivers’ acceptance of the Speed control is a primary method to change drivers’ warning technologies. behavior. This has been studied extensively and described in Among various methods, the technology acceptance model the literature, and various connected vehicle system approaches (TAM) has been frequently and widely used in information have been introduced to capture the underlying processes of technology adoption studies. TAM was based on the theory of drivers’ speed control (Khondaker and Kattan, 2015; Wu et al., reasoned action (TRA) (Fishbein and Ajzen, 1975) and theory 2018). Notably, variable speed limit (VSL) systems are speed of planned behavior (TPB) (Ajzen, 1991) which suggested that control system solutions that enable dynamic changes of posted social behavior was motivated by an individual’s attitude. speed limits in response to prevailing traffic, incidents, and/or According to Davis (1989), the TAM consists of four weather conditions. Connected vehicle-based variable speed determining factors to accept information technologies, namely limit (CV-VSL) systems use traffic speed, volume detection, perceived usefulness (PU) and perceived ease of use (PEOU), and road weather information systems to determine the attitude toward using (ATT) and behavioral intention to use appropriate speed at which drivers are expected to be traveling, (BIN) as shown in Figure 1. The impacts of both determining given the current traffic and road conditions. Changes in posted factors on attitude towards the information technology are speed limits are indicated by displays on overhead or roadside assumed to be positive. That is when users’ perceptions of variable message signs, or displays on a vehicle’s human– usefulness and ease of use to one information technology machine interface (HMI) (Chang et al.,2019; Zhao et al., increase, the users’ positive attitude towards adopting that 2019). In the connected vehicle condition, dynamic message information technology is more likely. Furthermore, perceived signs (DMS) on the road and on-board HMI displays are two ease of us is assumed to have a positive, direct effect on types of information transmission technologies (Louw et al., perceived usefulness while both attitude and perceived 2015; Louw et al., 2017). usefulness has positive, direct effects on behavioral intention. On-road DMS in fog area refers to the establishment of The TAM has been applied to predict and explain a variety of variable speed limit signs at certain intervals on the road information technologies and the hypothetical relationships section, instructing the drivers to achieve uniform speed have been widely supported (Chen and Chen, 2011; Rahman change, to avoid the sudden speed mutation of the vehicle at low visibility. It is an infrastructure for real-time display of et al.,2017; Scherer et al.,2019; Taherdoost, 2018). information sent by management center and usually located in The objective of this research was to quantify the drivers’ front of the fog zone and helps the drivers adjust to the driver acceptance of the CV-VSL system and its influencing factors performance as they enter the fog zone (Goodwin and Pisano, through driving simulator experiment and its post surveys. The 2003; Pisano and Goodwin, 2004; Xu, 2007). In the United CV-VSL systems including on-road DMS and on-board HMI States, UT (Goodwin and Pisano, 2003), WA (Pisano and were both realized through a connected vehicle testing platform Goodwin, 2004), Carolina (Xu, 2007) have the fog warning based on a driving simulator. According to the TAM, two systems, where DMS and speed limit signs are installed on the factors, namely perceived usefulness and PEOU associated road side to reduce the speed of vehicles under adverse weather with DMS and on-board HMI are assumed to affect conditions to reduce the accident rates. In Australia, DMS is consumers’ acceptance of the CV-VSL systems in this study. used to carry out the warning measures in foggy days on the The influence of driver characteristics (gender, age, driving freeway. Moreover, the reduction of vehicle speed and the difference of speed after the implementation of the measures is Figure 1 Technology acceptance model analyzed, which verifies the effectiveness of the facilities (Xu, 2007). In recent decades, some researchers put forward from HMI design research from the psychological point of view. Laboratory led by Negroponte did a large number of researches on the multi-channel user interface through the visual, auditory, tactile and other sensory organs of human–computer interaction to reduce the visual pressure of users (Shneiderman, 1992). On-board HMI is a key device for information 34 On-board human machine interface Journal of Intelligent and Connected Vehicles Jia Li, Wenxiang Xu and Xiaohua Zhao Volume 2 · Number 2 · 2019 · 33–40 year and driving personality), technical type (on-road DMS or Figure 2 Connected vehicle testing platform construction on-board HMI) and fog density (heavy or light fog) on driver’s acceptance of CV-VSL systems were considered. The research results are conducive to better popularizing the use of the CV- VSL systems and giving full play to its positive role in traffic safety. 2. Experiment design 2.1 Connected vehicle testing platform The research constructed a CV-VSL test platform based on driving simulator. The fixed-base driving simulator located in the Key Laboratory of Traffic Engineering of Beijing University of Technology consists of a real car, computers, videos, and audio equipment. The road scenario was projected onto three big screens, providing a 130-degree field of view. The screen resolution of the driving simulator was 1920  1080. The simulator recorded operating data (e.g. braking force, acceleration, speed, lateral placement, lane numbers, and turning angle of the steering wheel) 30 times per s. The simulator adopted an application programming interface is chosen as the color of DMS text since it means warning in (API), which allowed users to design driving tasks according to traffic sign. Traffic signs are set up in columns type (both single their needs. and double), cantilevered type, attached type and doorframe The virtual visibility sensor and distance sensor collect data type according to the Institute of highway science, ministry of by the roadside unit to driving simulator system (DSS) through communications (2009). The DMS on the freeway adopts the the API corresponding to the data collection in the actual system. When visibility is less than 10,000 m, the information doorframe type through communication with traffic will pass to the management center. The management center management department because the doorframe sign is more striking than the roadside sign. The size of DMS is shown in compares the visibility information with the threshold value of the classification standard related to fog level to determine the Figure 1 (Take two lanes for example, the lane width is level of fog: light fog and heavy fog. 3.75 m). We structure the interconnection between driving simulator 2.2.2 Human–machine interface design and DSS through interface. DSS and management center The on-board display uses a PAD that receives fog warning and synchronously transmits through the user datagram protocol variable speed limit information in real time at a rate of 5 times corresponding to visibility and distance perception in the actual per s through wireless as shown in Figure 4. The HMI was system. Management center send the final display information divided into four groups as shown in Figure 4: to the roadside or user terminal. The information interaction 1 Group 1 showed the distance between a vehicle and its process was shown in Figure 2. lead vehicle; This study chose the CV-VSL system in foggy conditions as a 2 Group 2 showed the speed warning to the drivers; case study based on the connected vehicle testing platform. “Speed” showed the current speed of the vehicle, and The results revealed that the majority of drivers agreed with the “Speed limit” showed the current speed limit of the road. validity of this platform. The validity of this platform has been The speed limit was 120 km/h in a no fog or light fog determined in our previous research using an assessment method (Shechtman et al.,2009). Figure 3 Design of DMS 2.2 Warning information interaction modes – on-road dynamic message sign and on-board human–machine interface Two types of terminals including on-road DMS and on-board HMI were designed in this study. The CV-VSL system warning appeared when the vehicle neared the 2 km range of the fog. There were 5 warning points in the clear zone, each at an interval of 500 m. The technical details of the implementation are described as follows. 2.2.1 Dynamic message sign design The DMS displayed the fog warning and variable speed limit information on the road infrastructure as shown in Figure 3.In order to enhance the effect of forecast, multiple DMS should be set up. The advance distance of DMS to the driver should be considered to ensure that the driver can see each DMS. Yellow 35 On-board human machine interface Journal of Intelligent and Connected Vehicles Jia Li, Wenxiang Xu and Xiaohua Zhao Volume 2 · Number 2 · 2019 · 33–40 1 a clear zone (3.5 km); Figure 4 Symbol display of HMI 2 a transition zone (0.5 km); and 3 a fog zone (2 km) (Figure 3). The length of the clear zone was determined to ensure sufficient distance for allocating multiple CV-VSL systems; the distance (1.5 km) before the warning was to ensure drivers could reach the normal speed. The transition zone was designed with gradually reduced visibility to avoid a sudden visibility change, and the visibility changes to the fog zone’s level when the drivers arrive to fog zone. It was assumed that drivers could get used to the reduced visibility within the 0.5 km distance. In addition, drivers were expected to drive in the fog zone for a 2 km distance. As shown in Figure 5, the situation, and 60 km/h in a heavy fog situation (30).A visibility in the different fog level scenarios was no fog, light fog continuous voice warning (you have been speeding, please (visibility = 725m) and heavy fog (visibility = 125 m), slow down) alerted drivers of danger whenever the driver’s respectively (Standardization Administration of the People’s speed exceeded the speed limit. Republic of China, 2012). Each driver would drive twice along 3 Group 3 showed imminent dangerous vehicle the freeway using on-road DMS and on-board HMI, surroundings. The red exclamation mark appeared with a respectively. continuous alarm to alert drivers of an imminent collision 2.3.2 Participants whenever time to collision (TTC) with the lead vehicle A total of 43 healthy participants (age: Mean = 35 years, was below a 2-s threshold (31). The fog symbol appeared Standard Deviation (SD) = 11.88), including 27 males and 16 with a voice warning (you are close to the fog area) when females, were recruited from universities and social the vehicle neared the 2-km range of the fog. The voice organizations to participate in the experiment. The participants played once every 500 meters, and the fog symbol was were required to have at least 20/20 (normal or corrected, self- continuous. reported) vision and no hearing problems (self-reported). A 4 Group 4 showed the traffic situation surrounding a self-administered questionnaire was designed to collect the vehicle. The arrow symbol was solid green when the empirical data for this study. All participants provided distance between a vehicle and its surrounding vehicles informed written consent and demographic data (Table I) was over 200 m, flashing yellow when the distance was less before joining the experiment. Driver’s basic information than the 200 meters, and flashing red whenever the TTC includes gender, age, driving year, driving personality, etc. To was below a 2-s threshold. clarify, the homogeneous sample of subjects was selected in order to minimize any bias attributable to sample 2.3 Experimental design and data collection heterogeneity. After excluding 5 invalid questionnaires, 38 Experiments were conducted to analyze the drivers’ acceptance sample data were used in this study. of the CV-VSL systems and its influencing factors. 2.4 Technology acceptance model 2.3.1 Scenario design. The driver’s questionnaire on technology acceptance was based The experimental road in this study was based on the on the study of Son et al. (2015) and Venkatesh and Davis northbound sections on the Xingyan freeway (a freeway with a (2000) to make used of the TAM to study the degree of driver’s total width of 18.8 m (lane width = 3.75 m, median (green belt) acceptance of one technology. Besides of the basic information width = 0.8 m and shoulder width = 1.50 m) in the north of of the drivers, the questionnaire also included 3 questions Beijing. The selected segments were located in a relatively foggy area. The Xingyan freeway is a four-lane divided freeway measuring respondents’ perceptions about usefulness, ease of with 120 km/h speed limit, while in heavy fog area the speed use for the CV-VSL system. The average score for questions limit is 60 km/h. For each road segment, the total length was “rationality of the warning content”, “safety of the technology” about 6 km, consisting of three zones: was used to calculate the perceived usefulness (PU) and the Figure 5 Layout of the experimental road 36 On-board human machine interface Journal of Intelligent and Connected Vehicles Jia Li, Wenxiang Xu and Xiaohua Zhao Volume 2 · Number 2 · 2019 · 33–40 Table I Descriptive statistics The acceptance of DMS by young and middle-aged drivers was 71 per cent and 74 per cent respectively, and the Mean (SD) Data acceptance of the on-board HMI was 60 per cent and 67 per Variables statistics % statistics description cent respectively for young and middle-aged drivers. In heavy Gender 1.42 (0.49) 1:57 1: male; fog, the acceptance of DMS was 63 per cent for young and 71 2:43 2: female per cent for middle-aged; and they were 59 per cent and 69 per Age (years) 35 (18) 1:40 1: age < = 30; cent respectively. In heavy fog, the acceptance of HMI was 59 2:60 2: age > 30 per cent for young and 60 per cent for middle-aged; while in Driving years 14.9 (9.8) 1:47% 1: driving years light fog, the acceptance of HMI was 51 per cent for young and 2:53% <10; 63 per cent for middle-aged. The results showed that the 2: driving years middle-aged drivers’ acceptance is higher than young drivers. > =10 The acceptance of DMS was 72 per cent for drivers with Driving personality 1.39 (0.49) 1:60% 1: conservative; longer driving year (more than 10 years) and 73 per cent for 2:40% 2: aggressive drivers with shorter driving year (less than 10 years), and the Average driving 15450 (4372.15) –– acceptance of HMI was 62 per cent for drivers with more than mileage 10 driving years and 65 per cent for drivers with less than 10 (km/per year) driving years respectively. The acceptance of DMS in heavy fog was 65 per cent and 70 per cent for drivers with different driving years, and was 60 per cent and 67 per cent in light fog. score for question “negative interference of the technology” The acceptance of HMI in heavy fog was 61 per cent and 59 per was used to calculate the PEOU. cent for drivers with different driving years, and was 53 per cent All questions were measured with a five-point Likert-type and 64 per cent in light fog. The results showed that the scale ranging from “strongly disagree (=1)” to “strong agree experienced drivers’ acceptance is lower than inexperienced (=5)”. All the 3 questions have been asked for DMS, DMS in drivers in most cases. light fog, DMS in heavy fog, on-board HMI, on-board HMI in The acceptance of DMS for conservative and aggressive light fog, on-board HMI in heavy fog separately. TAM uses the drivers was 72 and 73 per cent and 60 and 67 per cent for HMI. drivers’ attitude that is sum of PU and PEOU to quantify the In heavy fog, the acceptance of DMS was 68 per cent for acceptance of computer-related technologies. The driver’s conservative and aggressive drivers, while in light fog, they were acceptance of the technology is calculated as follows (Davis, 64 per cent and 63 per cent respectively. In heavy fog, the 1989): acceptance of HMI was 56 per cent for conservative drivers and ðÞðÞ A ¼ PU1 PEOU = 2  C  100% (1) 63 per cent for aggressive drivers, while in light fog, the acceptance values were 61 and 57 per cent. The results showed In the formula: A is the driver’s acceptance of DMS or on- that there were no consistent rules for the drivers’ acceptance of board HMI; PU is the perceived usefulness, PEOU is PEOU, technologies between different driving personalities. C is the subjective scale rating, that is, 5 points. From another perspective, no matter what type the drivers are, the drivers’ acceptance of DMS is higher than that of on- board HMI. In most cases, the drivers’ acceptance of both of 3. Results the two types of technologies in light fog is lower than that in 3.1 Drivers’ acceptance of on-road dynamic message heavy fog. sign and on-board human–machine interface The driver’s acceptance of DMS and on-board HMI for CV- 3.2 The effect of technology types and fog density on VSL system can be defined as the reaction when they come into technology acceptance of fog waring system contact with the warning technology, as well as the intention to In order to study the effect of technology types and fog densities adopt the technology while driving (Rahman et al.,2017). on drivers’ acceptance, the variance analysis was carried out Table II shows the acceptance of DMS and on-board HMI by with technology types for CV-VSL and fog densities as factors. drivers of different gender, age, driving year and driving The results in Table III showed that the technology types had a personality, as well as the acceptance of the two technology significant effect on drivers’ acceptance, while the fog density types in light and heavy fog areas. had no significant effect on drivers’ acceptance. The results showed that the average acceptance of DMS was The results showed that the drivers’ acceptance of DMS (73 approximately equal for drivers of different gender, 74 per cent per cent) was obviously higher than that for on-board HMI (64 for male and 71 per cent for female. The average acceptance of per cent) which could be seen in Figure 6. The results showed on-board HMI is 65 per cent for male and 62 per cent for that drivers were significantly easier to accept DMS than on- females. In heavy fog, the acceptance of DMS was 70 per cent board HMI for the CV-VSL systems. It also indicated that the for male and 65 per cent for female; while in light fog, the more complex CV-VSL system such as on-board HMI design acceptance of DMS was 64 per cent for both male and female. did not mean the higher acceptance of drivers. The information In heavy fog, the acceptance of HMI was 61 per cent for male transmission to driver should pay more attention to the and 59 per cent for female; while in light fog, the acceptance of HMI was 63 per cent for male and 54 per cent for female. The simplicity and directness instead of the gorgeous design. results indicated that there was no larger difference for the Although the results in Table II indicated that for most cases the drivers’ technology acceptance between male and female. acceptance of the technologies in light fog was lower than that in 37 On-board human machine interface Journal of Intelligent and Connected Vehicles Jia Li, Wenxiang Xu and Xiaohua Zhao Volume 2 · Number 2 · 2019 · 33–40 Table II Descriptive statistics results of driver’s acceptance DMS DMS HMI HMI Variables DMS HMI light fog heavy fog light fog heavy fog Gender Male Mean/(%) 74 65 64 70 63 61 SD 0.11 0.111 0.155 0.1 0.184 0.176 Female Mean/(%) 71 62 64 65 54 59 SD 0.094 0.099 0.145 0.15 0.117 0.132 Age Youth Mean/(%) 71 60 59 63 51 59 SD 0.089 0.084 0.166 0.141 0.15 0.119 Middle age Mean/(%) 74 67 69 71 63 60 SD 0.115 0.112 0.115 0.1 0.167 0.185 Driving years Experienced Mean/(%) 72 62 60 65 53 61 SD 0.094 0.105 0.178 0.145 0.14 0.126 Inexperienced Mean/(%) 73 65 67 70 64 59 SD 0.115 0.099 0.112 0.099 0.18 0.189 Driving personality Conservative Mean/(%) 72 60 64 68 61 56 SD 0.12 0.093 0.131 0.122 0.154 0.182 Aggressive Mean/(%) 73 67 63 68 57 63 SD 0.092 0.108 0.163 0.128 0.17 0.136 Total Mean 73 64 64 68 58 60 SD 0.104 0.101 0.146 0.123 0.158 0.155 Note: Total means all the 43 participants Figure 6 Acceptance of different technology types for CV-VSL system Table III Analysis results of the effect of technology types and fog density on drivers’ acceptance of CV-VSL systems Mean(SD) F Df p DMS VS on-board HMI 12.623 1 0.001 DMS 73% (0.10) On-board HMI 64% (0.10) DMS Heavy fog VS light fog 1.694 1 0.197 Light fog 64% (0.15) Heavy fog 68% (0.12) HMI Heavy fog VS light fog 0.078 1 0.781 Light fog 59% (0.17) Heavy fog 60% (0.16) 38 On-board human machine interface Journal of Intelligent and Connected Vehicles Jia Li, Wenxiang Xu and Xiaohua Zhao Volume 2 · Number 2 · 2019 · 33–40 heavy fog, the variance analysis in Table III showed that the fog study were collected by the questionnaires after the drivers’ density was not associated with the drivers’ acceptance significantly. experiment on a driving simulator. The analysis results showed that: The technology type (DMS or on-board HMI) of CV-VSL 3.3 The effect of gender, age, driving year and driving systems in fog area has a significant impact on drivers’ personality on technology acceptance acceptance. This suggested that drivers are easier to accept In order to further study the influence of driver’s gender, age, driving year, and driving personality on technology acceptance DMS than on-board HMI for the fog warning system, which of DMS and on-board HMI for CV-VSL, variance analysis was can be understood as drivers prefer to get message head up rather than looking at the display on the screen in the cab. carried out with the driver’s gender, age, driving year and The fog density has no significant effect on drivers’ driving personality as factors, as shown in Table IV. The variance analysis results in Table IV showed gender was acceptance of the CV-VSL system. However, in practice, not associated with the acceptance of any technology for the drivers’ acceptance of the CV-VSL system is expected to be CV-VSL system. Driver’s age was associated with the higher in heavy fog than no fog area because of the higher risk in heavy fog. Therefore, better eye-catching signs need acceptance of HMI, DMS in heavy fog, and DMS in light fog significantly. It could be seen from Table II that the acceptance to be further designed for the DMS and on-board HMI of drivers above 30 years old is higher than that for younger under low visibility conditions. drivers. Driving year was significantly associated with the Drivers above 30 years old have significantly higher acceptance of on-board HMI, DMS in light fog, as well as acceptance of HMI in light fog. It could be seen from Table II that the acceptance of HMI in light fog for drivers with more DMS in heavy fog than younger drivers. Experienced drivers’ than 10 driving years is lower than that for drivers with less than acceptance of the on-board HMI in light fog is lower than that 10 years, which could be understood as fresh drivers are more for fresh drivers. Aggressive drivers’ acceptance of the on- board HMI is higher than that for conservative drivers. This able to accept new technologies while experienced drivers get used to the traditional driving environment. Driving personality indicated that the age, driving year, and driving personality is also significantly associated with the acceptance of HMI. It influence drivers’ acceptance of the technology differently. could be seen from Table II that the acceptance of HMI for The findings of this study also provide some important practical aggressive drivers is higher than that for conservative drivers. implication for marketers. For the location of the on-board display, automobile manufacturers should assign the on-board display at 4. Discussion and conclusion the up-head position to increase the access to warning information. For the warning message, better eye-catching signs need to be Driving in fog area is a potentially dangerous activity, especially when fog appears suddenly. CV-VSL systems can deliver further designed under low visibility conditions. For different warning messages to drivers and help them improve their drivers, more personalized designs should be considered. For decisions in conditions of reduced visibility. Previous studies have example, aggressive drivers are more acceptable to the new technology, therefore, they can be provided with more multi- been conducted to evaluate the effectiveness of CV-VSL systems. However, there were rare studies focused on the drivers’ attitude source information; while conservative drivers are less acceptable toward using which was also called as acceptance of the CV-VSL to the new technology, therefore, the existing information should systems. Therefore, this research calculated the drivers’ be further optimized for them. Thus, the marketers can design acceptance of two normally used CV-VSL systems including their marketing strategies for the on-board HMI. DMS and on-board HMI using TAM. Furthermore, variance The acceptance in this study was calculated by only three analysis was conducted to explore whether the factors such as questions, which is relatively less than the proposed questions drivers’ characteristics (gender, age, driving year, and driving by previous studies (Davis, 1989). In the future study, personality), technology types, and fog density affected the questions about drivers’ perceived usefulness, PEOU, attitude drivers’ acceptance of the CV-VSL systems. The data in this towards use, and behavior intention to use are all needed to be Table IV Analysis results of the effect of driver’s gender, age, driving experience, and driving personality on CV-VSL acceptance DMS DMS HMI HMI Pr > F DMS HMI light fog heavy fog light fog heavy fog Gender 0.380 0.297 0.982 0.238 0.103 0.689 Age 0.291 0.078 0.027 0.057 0.11 1 Driving year 0.808 0.342 0.186 0.169 0.045 0.691 Driving personality 0.620 0.048 0.920 0.968 0.485 0.221 Gender Age –– Gender Driving year 0.786 0.310 0.079 0.175 0.335 0.976 Gender Driving personality – – –––– Age Driving year – – –––– Age Driving personality – – –––– Driving year Driving personality 0.271 0.108 0.922 0.967 0.751 0.610 Notes: Significant at 95% level; significant at 90% level 39 On-board human machine interface Journal of Intelligent and Connected Vehicles Jia Li, Wenxiang Xu and Xiaohua Zhao Volume 2 · Number 2 · 2019 · 33–40 Research Record: Journal of the Transportation Research Board, designed in the questionnaire to develop the TAM. 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(2004), “Research needs for weather-responsive traffic management”, Transportation Xiaohua Zhao can be contacted at: zhaoxiaohua@bjut.edu.cn For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: permissions@emeraldinsight.com http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Intelligent and Connected Vehicles Emerald Publishing

Technology acceptance comparison between on-road dynamic message sign and on-board human machine interface for connected vehicle-based variable speed limit in fog area

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Emerald Publishing
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© Jia Li, Wenxiang Xu and Xiaohua Zhao.
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2399-9802
DOI
10.1108/jicv-12-2018-0016
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Abstract

Purpose – Connected vehicle-based variable speed limit (CV-VSL) systems in fog area use multi-source detection data to indicate drivers to make uniform change in speed when low visibility conditions suddenly occur. The purpose of the speed limit is to make the driver’s driving behavior more consistent, so as to improve traffic safety and relieve traffic congestion. The on-road dynamic message sign (DMS) and on-board human–machine interface (HMI) are two types of warning technologies for CV-VSL systems. This study aims to analyze drivers’ acceptance of the two types of warning technologies in fog area and its influencing factors. Design/methodology/approach – This study developed DMS and on-board HMI for the CV-VSL system in fog area on a driving simulator. The DMS and on-board HMI provided the driver with weather and speed limit information. In all, 38 participants participated in the experiment and completed questionnaires on drivers’ basic information, perceived usefulness and ease of use of the CV-VSL systems. Technology acceptance model (TAM) was developed to evaluate the drivers’ acceptance of CV-VSL systems. A variance analysis method was used to study the influencing factors of drivers’ acceptance including drivers’ characteristics, technology types and fog density. Findings – The results showed that drivers’ acceptance of on-road DMS was significantly higher than that of on-board HMI. The fog density had no significant effect on drivers’ acceptance of on-road DMS or on-board HMI. Drivers’ gender, age, driving year and driving personality were associated with the acceptance of the two CV-VSL technologies differently. This study is beneficial to the functional improvement of on-road DMS, on-board HMI and their market prospects. Originality/value – Previous studies have been conducted to evaluate the effectiveness of CV-VSL systems. However, there were rare studies focused on the drivers’ attitude toward using which was also called as acceptance of the CV-VSL systems. Therefore, this research calculated the drivers’ acceptance of two normally used CV-VSL systems including on-road DMS and on-board HMI using TAM. Furthermore, variance analysis was conducted to explore whether the factors such as drivers’ characteristics (gender, age, driving year and driving personality), technology types and fog density affected the drivers’ acceptance of the CV-VSL systems. Keywords Technology acceptance model (TAM), Connected vehicle (CV), Dynamic message sign (DMS), Human machine interface (HMI), Variable speed limit (VSL) Paper type Research paper Several measures have been made to reduce traffic crash 1. Introduction frequency in fog area such as fog detection and warning Fog reduces visibility on roads which is a critical factor in systems, low visibility driving safety campaigns, and driver drivers’ perceptions of the driving environment. Crashes are training. Some studies have further attempted to address the always possible in fog area because of drivers’ failure to issue of fog and its impact on highway safety with connected maintain safe following distances under adverse weather vehicle technologies (Boyle and Mannering, 2004). Nowadays, conditions according to the World Health Organization the fog warning system using connected vehicle technology is (2016). Fog is likely to have played a role in 20,159 police- reported fatal crashes that occurred in China in 2017 by the Ministry of Public Security Traffic Management Bureau © Jia Li, Wenxiang Xu and Xiaohua Zhao. Published in Journal of (2017). Intelligent and Connected Vehicles. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial The current issue and full text archive of this journal is available on and non-commercial purposes), subject to full attribution to the original Emerald Insight at: www.emeraldinsight.com/2399-9802.htm publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode Journal of Intelligent and Connected Vehicles Received 28 December 2018 2/2 (2019) 33–40 Revised 26 May 2019 Emerald Publishing Limited [ISSN 2399-9802] [DOI 10.1108/JICV-12-2018-0016] Accepted 27 July 2019 33 On-board human machine interface Journal of Intelligent and Connected Vehicles Jia Li, Wenxiang Xu and Xiaohua Zhao Volume 2 · Number 2 · 2019 · 33–40 widely used. All countries in the world have taken measures to transmission between driver and vehicle, and the interaction of improve the driving safety of highway in foggy days by fog on-board systems with the outside world based on connected warning system. Connected vehicle used advanced wireless vehicle and big data technology will become more powerful (Li communication and a new generation of internet technology, et al., 2008). For complex human-machine interaction implemented vehicle and road dynamic real-time information scenarios, information needs to be intuitive, precise and clear, interaction all around, allowed vehicles to talk to one another, with the goal of assisting driving, ensuring driving safety and to transportation infrastructure, to pedestrians, cyclists and reducing the driving burden (Zhang, 2014). passengers in a cooperative manner, and carried out vehicle On-road DMS and on-board HMI are two types of warning active safety control and road collaborative management on the technologies for CV-VSL systems, however, drivers’ basis of the whole space-time dynamic traffic information acceptance of these two technologies are rarely analyzed. The collection and integration, fully realized the effective purpose of this study is to: coordination of human, vehicle and road to form a safe, 1 analyze the drivers’ acceptance of the CV-VSL systems efficient and environmentally friendly road traffic system including on-road DMS and on-board HMI; and (McGurrin et al.,2012). 2 explore factors affecting drivers’ acceptance of the Speed control is a primary method to change drivers’ warning technologies. behavior. This has been studied extensively and described in Among various methods, the technology acceptance model the literature, and various connected vehicle system approaches (TAM) has been frequently and widely used in information have been introduced to capture the underlying processes of technology adoption studies. TAM was based on the theory of drivers’ speed control (Khondaker and Kattan, 2015; Wu et al., reasoned action (TRA) (Fishbein and Ajzen, 1975) and theory 2018). Notably, variable speed limit (VSL) systems are speed of planned behavior (TPB) (Ajzen, 1991) which suggested that control system solutions that enable dynamic changes of posted social behavior was motivated by an individual’s attitude. speed limits in response to prevailing traffic, incidents, and/or According to Davis (1989), the TAM consists of four weather conditions. Connected vehicle-based variable speed determining factors to accept information technologies, namely limit (CV-VSL) systems use traffic speed, volume detection, perceived usefulness (PU) and perceived ease of use (PEOU), and road weather information systems to determine the attitude toward using (ATT) and behavioral intention to use appropriate speed at which drivers are expected to be traveling, (BIN) as shown in Figure 1. The impacts of both determining given the current traffic and road conditions. Changes in posted factors on attitude towards the information technology are speed limits are indicated by displays on overhead or roadside assumed to be positive. That is when users’ perceptions of variable message signs, or displays on a vehicle’s human– usefulness and ease of use to one information technology machine interface (HMI) (Chang et al.,2019; Zhao et al., increase, the users’ positive attitude towards adopting that 2019). In the connected vehicle condition, dynamic message information technology is more likely. Furthermore, perceived signs (DMS) on the road and on-board HMI displays are two ease of us is assumed to have a positive, direct effect on types of information transmission technologies (Louw et al., perceived usefulness while both attitude and perceived 2015; Louw et al., 2017). usefulness has positive, direct effects on behavioral intention. On-road DMS in fog area refers to the establishment of The TAM has been applied to predict and explain a variety of variable speed limit signs at certain intervals on the road information technologies and the hypothetical relationships section, instructing the drivers to achieve uniform speed have been widely supported (Chen and Chen, 2011; Rahman change, to avoid the sudden speed mutation of the vehicle at low visibility. It is an infrastructure for real-time display of et al.,2017; Scherer et al.,2019; Taherdoost, 2018). information sent by management center and usually located in The objective of this research was to quantify the drivers’ front of the fog zone and helps the drivers adjust to the driver acceptance of the CV-VSL system and its influencing factors performance as they enter the fog zone (Goodwin and Pisano, through driving simulator experiment and its post surveys. The 2003; Pisano and Goodwin, 2004; Xu, 2007). In the United CV-VSL systems including on-road DMS and on-board HMI States, UT (Goodwin and Pisano, 2003), WA (Pisano and were both realized through a connected vehicle testing platform Goodwin, 2004), Carolina (Xu, 2007) have the fog warning based on a driving simulator. According to the TAM, two systems, where DMS and speed limit signs are installed on the factors, namely perceived usefulness and PEOU associated road side to reduce the speed of vehicles under adverse weather with DMS and on-board HMI are assumed to affect conditions to reduce the accident rates. In Australia, DMS is consumers’ acceptance of the CV-VSL systems in this study. used to carry out the warning measures in foggy days on the The influence of driver characteristics (gender, age, driving freeway. Moreover, the reduction of vehicle speed and the difference of speed after the implementation of the measures is Figure 1 Technology acceptance model analyzed, which verifies the effectiveness of the facilities (Xu, 2007). In recent decades, some researchers put forward from HMI design research from the psychological point of view. Laboratory led by Negroponte did a large number of researches on the multi-channel user interface through the visual, auditory, tactile and other sensory organs of human–computer interaction to reduce the visual pressure of users (Shneiderman, 1992). On-board HMI is a key device for information 34 On-board human machine interface Journal of Intelligent and Connected Vehicles Jia Li, Wenxiang Xu and Xiaohua Zhao Volume 2 · Number 2 · 2019 · 33–40 year and driving personality), technical type (on-road DMS or Figure 2 Connected vehicle testing platform construction on-board HMI) and fog density (heavy or light fog) on driver’s acceptance of CV-VSL systems were considered. The research results are conducive to better popularizing the use of the CV- VSL systems and giving full play to its positive role in traffic safety. 2. Experiment design 2.1 Connected vehicle testing platform The research constructed a CV-VSL test platform based on driving simulator. The fixed-base driving simulator located in the Key Laboratory of Traffic Engineering of Beijing University of Technology consists of a real car, computers, videos, and audio equipment. The road scenario was projected onto three big screens, providing a 130-degree field of view. The screen resolution of the driving simulator was 1920  1080. The simulator recorded operating data (e.g. braking force, acceleration, speed, lateral placement, lane numbers, and turning angle of the steering wheel) 30 times per s. The simulator adopted an application programming interface is chosen as the color of DMS text since it means warning in (API), which allowed users to design driving tasks according to traffic sign. Traffic signs are set up in columns type (both single their needs. and double), cantilevered type, attached type and doorframe The virtual visibility sensor and distance sensor collect data type according to the Institute of highway science, ministry of by the roadside unit to driving simulator system (DSS) through communications (2009). The DMS on the freeway adopts the the API corresponding to the data collection in the actual system. When visibility is less than 10,000 m, the information doorframe type through communication with traffic will pass to the management center. The management center management department because the doorframe sign is more striking than the roadside sign. The size of DMS is shown in compares the visibility information with the threshold value of the classification standard related to fog level to determine the Figure 1 (Take two lanes for example, the lane width is level of fog: light fog and heavy fog. 3.75 m). We structure the interconnection between driving simulator 2.2.2 Human–machine interface design and DSS through interface. DSS and management center The on-board display uses a PAD that receives fog warning and synchronously transmits through the user datagram protocol variable speed limit information in real time at a rate of 5 times corresponding to visibility and distance perception in the actual per s through wireless as shown in Figure 4. The HMI was system. Management center send the final display information divided into four groups as shown in Figure 4: to the roadside or user terminal. The information interaction 1 Group 1 showed the distance between a vehicle and its process was shown in Figure 2. lead vehicle; This study chose the CV-VSL system in foggy conditions as a 2 Group 2 showed the speed warning to the drivers; case study based on the connected vehicle testing platform. “Speed” showed the current speed of the vehicle, and The results revealed that the majority of drivers agreed with the “Speed limit” showed the current speed limit of the road. validity of this platform. The validity of this platform has been The speed limit was 120 km/h in a no fog or light fog determined in our previous research using an assessment method (Shechtman et al.,2009). Figure 3 Design of DMS 2.2 Warning information interaction modes – on-road dynamic message sign and on-board human–machine interface Two types of terminals including on-road DMS and on-board HMI were designed in this study. The CV-VSL system warning appeared when the vehicle neared the 2 km range of the fog. There were 5 warning points in the clear zone, each at an interval of 500 m. The technical details of the implementation are described as follows. 2.2.1 Dynamic message sign design The DMS displayed the fog warning and variable speed limit information on the road infrastructure as shown in Figure 3.In order to enhance the effect of forecast, multiple DMS should be set up. The advance distance of DMS to the driver should be considered to ensure that the driver can see each DMS. Yellow 35 On-board human machine interface Journal of Intelligent and Connected Vehicles Jia Li, Wenxiang Xu and Xiaohua Zhao Volume 2 · Number 2 · 2019 · 33–40 1 a clear zone (3.5 km); Figure 4 Symbol display of HMI 2 a transition zone (0.5 km); and 3 a fog zone (2 km) (Figure 3). The length of the clear zone was determined to ensure sufficient distance for allocating multiple CV-VSL systems; the distance (1.5 km) before the warning was to ensure drivers could reach the normal speed. The transition zone was designed with gradually reduced visibility to avoid a sudden visibility change, and the visibility changes to the fog zone’s level when the drivers arrive to fog zone. It was assumed that drivers could get used to the reduced visibility within the 0.5 km distance. In addition, drivers were expected to drive in the fog zone for a 2 km distance. As shown in Figure 5, the situation, and 60 km/h in a heavy fog situation (30).A visibility in the different fog level scenarios was no fog, light fog continuous voice warning (you have been speeding, please (visibility = 725m) and heavy fog (visibility = 125 m), slow down) alerted drivers of danger whenever the driver’s respectively (Standardization Administration of the People’s speed exceeded the speed limit. Republic of China, 2012). Each driver would drive twice along 3 Group 3 showed imminent dangerous vehicle the freeway using on-road DMS and on-board HMI, surroundings. The red exclamation mark appeared with a respectively. continuous alarm to alert drivers of an imminent collision 2.3.2 Participants whenever time to collision (TTC) with the lead vehicle A total of 43 healthy participants (age: Mean = 35 years, was below a 2-s threshold (31). The fog symbol appeared Standard Deviation (SD) = 11.88), including 27 males and 16 with a voice warning (you are close to the fog area) when females, were recruited from universities and social the vehicle neared the 2-km range of the fog. The voice organizations to participate in the experiment. The participants played once every 500 meters, and the fog symbol was were required to have at least 20/20 (normal or corrected, self- continuous. reported) vision and no hearing problems (self-reported). A 4 Group 4 showed the traffic situation surrounding a self-administered questionnaire was designed to collect the vehicle. The arrow symbol was solid green when the empirical data for this study. All participants provided distance between a vehicle and its surrounding vehicles informed written consent and demographic data (Table I) was over 200 m, flashing yellow when the distance was less before joining the experiment. Driver’s basic information than the 200 meters, and flashing red whenever the TTC includes gender, age, driving year, driving personality, etc. To was below a 2-s threshold. clarify, the homogeneous sample of subjects was selected in order to minimize any bias attributable to sample 2.3 Experimental design and data collection heterogeneity. After excluding 5 invalid questionnaires, 38 Experiments were conducted to analyze the drivers’ acceptance sample data were used in this study. of the CV-VSL systems and its influencing factors. 2.4 Technology acceptance model 2.3.1 Scenario design. The driver’s questionnaire on technology acceptance was based The experimental road in this study was based on the on the study of Son et al. (2015) and Venkatesh and Davis northbound sections on the Xingyan freeway (a freeway with a (2000) to make used of the TAM to study the degree of driver’s total width of 18.8 m (lane width = 3.75 m, median (green belt) acceptance of one technology. Besides of the basic information width = 0.8 m and shoulder width = 1.50 m) in the north of of the drivers, the questionnaire also included 3 questions Beijing. The selected segments were located in a relatively foggy area. The Xingyan freeway is a four-lane divided freeway measuring respondents’ perceptions about usefulness, ease of with 120 km/h speed limit, while in heavy fog area the speed use for the CV-VSL system. The average score for questions limit is 60 km/h. For each road segment, the total length was “rationality of the warning content”, “safety of the technology” about 6 km, consisting of three zones: was used to calculate the perceived usefulness (PU) and the Figure 5 Layout of the experimental road 36 On-board human machine interface Journal of Intelligent and Connected Vehicles Jia Li, Wenxiang Xu and Xiaohua Zhao Volume 2 · Number 2 · 2019 · 33–40 Table I Descriptive statistics The acceptance of DMS by young and middle-aged drivers was 71 per cent and 74 per cent respectively, and the Mean (SD) Data acceptance of the on-board HMI was 60 per cent and 67 per Variables statistics % statistics description cent respectively for young and middle-aged drivers. In heavy Gender 1.42 (0.49) 1:57 1: male; fog, the acceptance of DMS was 63 per cent for young and 71 2:43 2: female per cent for middle-aged; and they were 59 per cent and 69 per Age (years) 35 (18) 1:40 1: age < = 30; cent respectively. In heavy fog, the acceptance of HMI was 59 2:60 2: age > 30 per cent for young and 60 per cent for middle-aged; while in Driving years 14.9 (9.8) 1:47% 1: driving years light fog, the acceptance of HMI was 51 per cent for young and 2:53% <10; 63 per cent for middle-aged. The results showed that the 2: driving years middle-aged drivers’ acceptance is higher than young drivers. > =10 The acceptance of DMS was 72 per cent for drivers with Driving personality 1.39 (0.49) 1:60% 1: conservative; longer driving year (more than 10 years) and 73 per cent for 2:40% 2: aggressive drivers with shorter driving year (less than 10 years), and the Average driving 15450 (4372.15) –– acceptance of HMI was 62 per cent for drivers with more than mileage 10 driving years and 65 per cent for drivers with less than 10 (km/per year) driving years respectively. The acceptance of DMS in heavy fog was 65 per cent and 70 per cent for drivers with different driving years, and was 60 per cent and 67 per cent in light fog. score for question “negative interference of the technology” The acceptance of HMI in heavy fog was 61 per cent and 59 per was used to calculate the PEOU. cent for drivers with different driving years, and was 53 per cent All questions were measured with a five-point Likert-type and 64 per cent in light fog. The results showed that the scale ranging from “strongly disagree (=1)” to “strong agree experienced drivers’ acceptance is lower than inexperienced (=5)”. All the 3 questions have been asked for DMS, DMS in drivers in most cases. light fog, DMS in heavy fog, on-board HMI, on-board HMI in The acceptance of DMS for conservative and aggressive light fog, on-board HMI in heavy fog separately. TAM uses the drivers was 72 and 73 per cent and 60 and 67 per cent for HMI. drivers’ attitude that is sum of PU and PEOU to quantify the In heavy fog, the acceptance of DMS was 68 per cent for acceptance of computer-related technologies. The driver’s conservative and aggressive drivers, while in light fog, they were acceptance of the technology is calculated as follows (Davis, 64 per cent and 63 per cent respectively. In heavy fog, the 1989): acceptance of HMI was 56 per cent for conservative drivers and ðÞðÞ A ¼ PU1 PEOU = 2  C  100% (1) 63 per cent for aggressive drivers, while in light fog, the acceptance values were 61 and 57 per cent. The results showed In the formula: A is the driver’s acceptance of DMS or on- that there were no consistent rules for the drivers’ acceptance of board HMI; PU is the perceived usefulness, PEOU is PEOU, technologies between different driving personalities. C is the subjective scale rating, that is, 5 points. From another perspective, no matter what type the drivers are, the drivers’ acceptance of DMS is higher than that of on- board HMI. In most cases, the drivers’ acceptance of both of 3. Results the two types of technologies in light fog is lower than that in 3.1 Drivers’ acceptance of on-road dynamic message heavy fog. sign and on-board human–machine interface The driver’s acceptance of DMS and on-board HMI for CV- 3.2 The effect of technology types and fog density on VSL system can be defined as the reaction when they come into technology acceptance of fog waring system contact with the warning technology, as well as the intention to In order to study the effect of technology types and fog densities adopt the technology while driving (Rahman et al.,2017). on drivers’ acceptance, the variance analysis was carried out Table II shows the acceptance of DMS and on-board HMI by with technology types for CV-VSL and fog densities as factors. drivers of different gender, age, driving year and driving The results in Table III showed that the technology types had a personality, as well as the acceptance of the two technology significant effect on drivers’ acceptance, while the fog density types in light and heavy fog areas. had no significant effect on drivers’ acceptance. The results showed that the average acceptance of DMS was The results showed that the drivers’ acceptance of DMS (73 approximately equal for drivers of different gender, 74 per cent per cent) was obviously higher than that for on-board HMI (64 for male and 71 per cent for female. The average acceptance of per cent) which could be seen in Figure 6. The results showed on-board HMI is 65 per cent for male and 62 per cent for that drivers were significantly easier to accept DMS than on- females. In heavy fog, the acceptance of DMS was 70 per cent board HMI for the CV-VSL systems. It also indicated that the for male and 65 per cent for female; while in light fog, the more complex CV-VSL system such as on-board HMI design acceptance of DMS was 64 per cent for both male and female. did not mean the higher acceptance of drivers. The information In heavy fog, the acceptance of HMI was 61 per cent for male transmission to driver should pay more attention to the and 59 per cent for female; while in light fog, the acceptance of HMI was 63 per cent for male and 54 per cent for female. The simplicity and directness instead of the gorgeous design. results indicated that there was no larger difference for the Although the results in Table II indicated that for most cases the drivers’ technology acceptance between male and female. acceptance of the technologies in light fog was lower than that in 37 On-board human machine interface Journal of Intelligent and Connected Vehicles Jia Li, Wenxiang Xu and Xiaohua Zhao Volume 2 · Number 2 · 2019 · 33–40 Table II Descriptive statistics results of driver’s acceptance DMS DMS HMI HMI Variables DMS HMI light fog heavy fog light fog heavy fog Gender Male Mean/(%) 74 65 64 70 63 61 SD 0.11 0.111 0.155 0.1 0.184 0.176 Female Mean/(%) 71 62 64 65 54 59 SD 0.094 0.099 0.145 0.15 0.117 0.132 Age Youth Mean/(%) 71 60 59 63 51 59 SD 0.089 0.084 0.166 0.141 0.15 0.119 Middle age Mean/(%) 74 67 69 71 63 60 SD 0.115 0.112 0.115 0.1 0.167 0.185 Driving years Experienced Mean/(%) 72 62 60 65 53 61 SD 0.094 0.105 0.178 0.145 0.14 0.126 Inexperienced Mean/(%) 73 65 67 70 64 59 SD 0.115 0.099 0.112 0.099 0.18 0.189 Driving personality Conservative Mean/(%) 72 60 64 68 61 56 SD 0.12 0.093 0.131 0.122 0.154 0.182 Aggressive Mean/(%) 73 67 63 68 57 63 SD 0.092 0.108 0.163 0.128 0.17 0.136 Total Mean 73 64 64 68 58 60 SD 0.104 0.101 0.146 0.123 0.158 0.155 Note: Total means all the 43 participants Figure 6 Acceptance of different technology types for CV-VSL system Table III Analysis results of the effect of technology types and fog density on drivers’ acceptance of CV-VSL systems Mean(SD) F Df p DMS VS on-board HMI 12.623 1 0.001 DMS 73% (0.10) On-board HMI 64% (0.10) DMS Heavy fog VS light fog 1.694 1 0.197 Light fog 64% (0.15) Heavy fog 68% (0.12) HMI Heavy fog VS light fog 0.078 1 0.781 Light fog 59% (0.17) Heavy fog 60% (0.16) 38 On-board human machine interface Journal of Intelligent and Connected Vehicles Jia Li, Wenxiang Xu and Xiaohua Zhao Volume 2 · Number 2 · 2019 · 33–40 heavy fog, the variance analysis in Table III showed that the fog study were collected by the questionnaires after the drivers’ density was not associated with the drivers’ acceptance significantly. experiment on a driving simulator. The analysis results showed that: The technology type (DMS or on-board HMI) of CV-VSL 3.3 The effect of gender, age, driving year and driving systems in fog area has a significant impact on drivers’ personality on technology acceptance acceptance. This suggested that drivers are easier to accept In order to further study the influence of driver’s gender, age, driving year, and driving personality on technology acceptance DMS than on-board HMI for the fog warning system, which of DMS and on-board HMI for CV-VSL, variance analysis was can be understood as drivers prefer to get message head up rather than looking at the display on the screen in the cab. carried out with the driver’s gender, age, driving year and The fog density has no significant effect on drivers’ driving personality as factors, as shown in Table IV. The variance analysis results in Table IV showed gender was acceptance of the CV-VSL system. However, in practice, not associated with the acceptance of any technology for the drivers’ acceptance of the CV-VSL system is expected to be CV-VSL system. Driver’s age was associated with the higher in heavy fog than no fog area because of the higher risk in heavy fog. Therefore, better eye-catching signs need acceptance of HMI, DMS in heavy fog, and DMS in light fog significantly. It could be seen from Table II that the acceptance to be further designed for the DMS and on-board HMI of drivers above 30 years old is higher than that for younger under low visibility conditions. drivers. Driving year was significantly associated with the Drivers above 30 years old have significantly higher acceptance of on-board HMI, DMS in light fog, as well as acceptance of HMI in light fog. It could be seen from Table II that the acceptance of HMI in light fog for drivers with more DMS in heavy fog than younger drivers. Experienced drivers’ than 10 driving years is lower than that for drivers with less than acceptance of the on-board HMI in light fog is lower than that 10 years, which could be understood as fresh drivers are more for fresh drivers. Aggressive drivers’ acceptance of the on- board HMI is higher than that for conservative drivers. This able to accept new technologies while experienced drivers get used to the traditional driving environment. Driving personality indicated that the age, driving year, and driving personality is also significantly associated with the acceptance of HMI. It influence drivers’ acceptance of the technology differently. could be seen from Table II that the acceptance of HMI for The findings of this study also provide some important practical aggressive drivers is higher than that for conservative drivers. implication for marketers. For the location of the on-board display, automobile manufacturers should assign the on-board display at 4. Discussion and conclusion the up-head position to increase the access to warning information. For the warning message, better eye-catching signs need to be Driving in fog area is a potentially dangerous activity, especially when fog appears suddenly. CV-VSL systems can deliver further designed under low visibility conditions. For different warning messages to drivers and help them improve their drivers, more personalized designs should be considered. For decisions in conditions of reduced visibility. Previous studies have example, aggressive drivers are more acceptable to the new technology, therefore, they can be provided with more multi- been conducted to evaluate the effectiveness of CV-VSL systems. However, there were rare studies focused on the drivers’ attitude source information; while conservative drivers are less acceptable toward using which was also called as acceptance of the CV-VSL to the new technology, therefore, the existing information should systems. Therefore, this research calculated the drivers’ be further optimized for them. Thus, the marketers can design acceptance of two normally used CV-VSL systems including their marketing strategies for the on-board HMI. DMS and on-board HMI using TAM. Furthermore, variance The acceptance in this study was calculated by only three analysis was conducted to explore whether the factors such as questions, which is relatively less than the proposed questions drivers’ characteristics (gender, age, driving year, and driving by previous studies (Davis, 1989). In the future study, personality), technology types, and fog density affected the questions about drivers’ perceived usefulness, PEOU, attitude drivers’ acceptance of the CV-VSL systems. The data in this towards use, and behavior intention to use are all needed to be Table IV Analysis results of the effect of driver’s gender, age, driving experience, and driving personality on CV-VSL acceptance DMS DMS HMI HMI Pr > F DMS HMI light fog heavy fog light fog heavy fog Gender 0.380 0.297 0.982 0.238 0.103 0.689 Age 0.291 0.078 0.027 0.057 0.11 1 Driving year 0.808 0.342 0.186 0.169 0.045 0.691 Driving personality 0.620 0.048 0.920 0.968 0.485 0.221 Gender Age –– Gender Driving year 0.786 0.310 0.079 0.175 0.335 0.976 Gender Driving personality – – –––– Age Driving year – – –––– Age Driving personality – – –––– Driving year Driving personality 0.271 0.108 0.922 0.967 0.751 0.610 Notes: Significant at 95% level; significant at 90% level 39 On-board human machine interface Journal of Intelligent and Connected Vehicles Jia Li, Wenxiang Xu and Xiaohua Zhao Volume 2 · Number 2 · 2019 · 33–40 Research Record: Journal of the Transportation Research Board, designed in the questionnaire to develop the TAM. 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Journal

Journal of Intelligent and Connected Vehicles – Emerald Publishing

Published: Dec 17, 2019

Keywords: Technology acceptance model (TAM); Connected vehicle (CV); Dynamic message sign (DMS); Human machine interface (HMI); Variable speed limit (VSL)

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