Red-light Running Behaviors of E-scooters and E-bikes at Signalized Intersections in China

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1 Red-light Running Behaviors of E-scooters and E-bikes at Signalized Intersections in China By Yanyong Guo, Ph.D. Candidate School of Transportation, Southeast University Si Pai Lou #, Nanjing, China, 0 guoyanyong@1.com Pan Liu, Ph.D., Professor School of Transportation, Southeast University Si Pai Lou #, Nanjing, China, 0 pan_liu@hotmail.com Lu Bai, Ph.D. Candidate School of Transportation, Southeast University Si Pai Lou #, Nanjing, China, 0 xinyuesther@1.com Chengcheng Xu, Ph.D. Candidate School of Transportation, Southeast University Si Pai Lou #, Nanjing, China, 0 seudarwin@gmail.com and Jun Chen, Ph.D. Professor School of Transportation, Southeast University Si Pai Lou #, Nanjing, China, 0 chenjun@seu.edu.com Total number of words = 1 + 0* January 1-1, 01 Paper submitted for Possible Presentation at the nd Annual Meeting of the Transportation Research Board 1

2 Red-light Running Behaviors of E-scooters and E-bikes at Signalized Intersections in China by Yanyong Guo, Pan Liu, Lu Bai, Chengcheng Xu, and Jun Chen ABSTRACT: A comparative analysis was conducted to compare the red-light running behaviors of the riders of e-bikes, e-scooters and bicycles as they were crossing signalized intersections. The crossing behaviors of, individuals, including the riders of 1, e-bikes,,1 e-scooters, and 1, bicycles were observed. The average red-light running rate for e-bikes, e-scooters and bicycles at the selected sites was found to be.%,.0%, and 1.%, respectively. The red-light running rates for e-bikes and e-scooters are significantly higher than those for bicycles. The difference in the red-light running rates for e-bikes and e-scooters is not statistically significant. A binary logit model was then developed to evaluate how various factors affect the red-light running rates of two-wheeled vehicles at signalized intersections. Comparing the traffic conflicts caused by various red-light running two-wheelers, it was found that bicycles were slightly more likely to be involved in traffic conflicts than e-bikes and e-scooters as they were crossing intersections during a red pedestrian phase. In addition, e-bikes are less likely to be involved in traffic conflicts in the middle stage of a red pedestrian phase than e-scooters and bicycles. Both e-scooters and e-bikes have higher crossing speeds than bicycles. Even though e-scooters have significantly higher speeds than e-bikes as they were crossing intersections during green phases, the difference in the crossing speeds between red-light running e-bikes and e-scooters is not statistically significant. Keywords: e-scooter; e-bike; bicycle; red-light running; signalized intersection

3 INTRODUCTION Over the past two decades, there has been a significant increase in the number of electric bicycles used in urban areas in China. The number of electric bicycles has increased from thousand in 1 to 10 million in 01 with an average annual growth rate of.%(1). As compared to man-powered bicycles whose performance mainly relies on riders physical ability, electric bicycles use lead acid batteries and motors to provide power assistance. With enhanced mobility, electric bicycles allow users to make trips of longer distances. They have also been considered an environmental friendly alternative to automobiles (-). Despite all the obvious advantages, the use of electric bicycles has also raised some issues and concerns regarding their safety impacts in China. The fatalities and injuries of the electric bicycles accounted for.% and.% of the non-motorized Chinese traffic fatalities and injuries in 0 (). One of the safety concerns is related to the risk-taking behaviors of the riders of electric bicycles at signalized intersections, in which running against red lights is most frequently cited. According to our previous study, red-light running accounts for.% of the traffic conflicts that were caused by the risk-taking behaviors of the riders of electric bicycles at signalized intersections (). Two major types of electric bicycles are currently being used in China, including bicycle and scooter style electric bicycles (see Figure 1). For convenience, the scooter style electric bicycles are termed as e-scooters, while the term e-bike is used to denote the bicycle style electric bicycles for the rest of the paper. E-bikes are similar in size and weight to bicycles. They are usually equipped with -V batteries and to 0-W motors. E-scooters are more similar to motorcycles. They are installed with -V batteries and 0 to 00-W motors, which allow them to travel at higher speeds (-). Both e-bikes and e-scooters are legally classified as bicycles in China (). However, the operational characteristics of e-bikes, e-scooters and bicycles are quite different. (a) E-scooter (b) E-bike Figure 1. E-scooters and E-bikes

4 In past, numerous studies have investigated the behavioral characteristics of the riders of electric bicycles as they are crossing cross signalized intersections (-1). Currently the market price of an e-scooter in China varies from 000 to 000 RMB ( to US Dollars), which is significantly higher than that of an e-bike, of which the price varies from 100 to 00 RMB ( to 0 US Dollars). It is reasonable to assume that the riders of e-bikes and e-scooters have distinct social-economical characteristics and may behave differently at signalized intersections. The lack of information on the behavioral characteristics of e-bikes and e-scooters has also raised controversies regarding if different policies should be applied for the regulation of these two types of electric bicycles. Some believe that with higher operating speeds the riders of e-scooters are more crash prone, and accordingly, should be regulated more strictly as compared to the riders of e-bikes. However, such assumption is yet to be justified by field observations. In this study, comparative analyses were conducted to investigate the difference in the behavioral characteristics of the riders of e-bikes, e-scooters and bicycles as they were crossing signalized intersections. The focus was on the red-light running behaviors which, according to our previous study, constituted a major safety concern (). It is expected that the research results will help transportation professionals or policy makers develop effective guidelines or policies to reduce red-light running rates of e-bikes and e-scooters at signalized intersections in China. FIELD DATA COLLECTION Field data collection was conducted at nineteen approaches at fourteen signalized intersections in two cities (Nanjing and Kunming) in China. The characteristics of the selected sites are given in Table 1. The sites were carefully selected such that their geometric design and traffic control feathers represent the most common situations in major cities in China. More specifically, all selected sites are installed with pedestrian signals, of which seven sites are installed with flashing pedestrian signals that uses flashing green lights to pre-empt the termination of the green phase; while the other twelve sites are installed with countdown timers that display the remaining time of the red and green phases. The number of lanes on crossing streets varies from three to eight in both directions. The cross sections of the crossing streets at the selected sites can be divided into three types. For convenience they were designated as type 1,, and cross sections for the rest of the paper (see Figure ). Two video cameras were used to record data in the field (see Figure ). One camera was set up on top of a roadside building to cover the entire functional area of intersections, while the other camera was placed in vicinity of the target crosswalk to film the crossing behaviors of riders. The specific locations of cameras were carefully selected to ensure that their presence would not affect the behaviors of riders. Field data collection was only conducted during weekday peak periods, under fine weather conditions, and when live traffic enforcement was not present. The purpose of doing so was to minimize the impacts of confounding factors, such that the difference in the observed crossing behaviors can be attributed to

5 the behavioral characteristics of the riders of e-bikes, e-scooters, and bicycles. Before start recording, the two cameras were synchronized such that the data extracted from different videos can be matched (see Figure ). For each selected site the research team recorded three hours of data. In total, hours of data were recorded at the selected sites. Table 1. Selected signalized intersections for field data collection No. Intersection Approach Type Signal Cycle # Filming phases length (s) Lanes a Hours 1 Jinxianghe Rd. & Xuefu Rd. NB -leg Zhongshan Rd. & Zhujiang Rd. NB -leg 10 Zhongshan Rd. & Zhujiang Rd. EB -leg 10 Zhongshan Rd. & Dashiqiao Rd. NB -leg 10 Zhongshan Rd. & Dashiqiao Rd EB -leg 10 Changjiang Rd. & Zhongshan Rd. EB -leg 10 Jinxianghe Rd. & Beijing Rd. WB T-leg 10 Jinxianghe Rd. & Beijing Rd. NB T-leg 10 Hongwu Rd. & Changjiang Rd. NB -leg 1 Hongwu Rd. & Changjiang Rd EB -leg 1 EastbeijingRd. & Danfeng Rd. NB -leg 10 1 Jinxianghe Rd. & Zhujiang Rd. NB -leg 1 1 Jinxianghe Rd. & Zhujiang Rd. WB -leg 1 1 Baita Rd & Bailong Rd WB -leg 0 1 Beijin Rd & Koto Garden Rd EB -leg 1 Dongsi Rd & Gaodi Rd WB -leg 0 1 Huancheng Rd & Wujing Rd NB -leg 1 Ruikun Rd & Hongshan Rd SB -leg 10 1 Huancheng Rd & Xinweng Rd EB -leg 10 a Number of lanes on the crossing streets in both directions The recorded videos were reviewed in the laboratory for data reduction. For each individual, the research team recorded the gender, the estimated age, the type of the vehicle, and the time at which the rider crossed the stop lines in both directions of the intersection (see Figure ). The crossing speed for each rider was then estimated as the width of the crossing street divided by the crossing time. Note that the above information was only collected for the riders who arrived at an intersection during the red phase. The group size associated with each rider was recorded to identify the differences in the crossing behaviors of individuals and groups. The group size was defined as the number of riders who ride side-by-side within a distance of approximately two times the length of two-wheeled vehicles (). The volume of the traffic flow that is in conflict with the bicycle flow was counted in -min time intervals.

6 1 1 1 The conflicting flow includes the through traffic in both directions on the crossing street, the opposing left-turning traffic, and the right-turning traffic from the same approach. By reviewing videos the traffic conflicts that were caused by red-light running two-wheelers were also collected. The procedure proposed by (1) was followed for identifying traffic conflicts. A trained graduate student was designated to review all the videos to ensure that the same criteria were applied for identifying conflicts. The identification of traffic conflicts was based on the observed evasive actions between conflicting vehicles. The time to collision (TTC) was measured to help identify traffic conflicts. The measurement of TTC follows the procedure described in (). A total of conflicts that were caused by red-light running two-wheelers were observed at the selected sites. The TTC of each conflict varied from 0. to. sec with a mean of 1.0 sec. Only the conflicts with TTC less than sec were considered for further data analysis because a conflict with TTC greater than sec was considered of low risk according to our previous study (). Type 1 cross section Sideway Motor and bicycle lanes Sideway Type cross section Sideway Bicycle lane Motor lane Raised median Motor lane Bicycle lane Sideway Type cross section Sideway Bicycle lanebarrier Motor lane Barrier Bicycle lane Figure. Three types of cross sections at the selected sites Sideway

7 Figure. The crosswalk at the intersection between Jinxianghe Rd. & Zhujiang Rd. (site 1) RESULTS OF COMPARATIVE ANALYSES Red-light running rates The crossing behaviors of, individuals, including the riders of 1, e-bikes,,1 e-scooters, and 1, bicycles were observed at the selected sites. Of the observed individuals,.% crossed intersections against red lights. The overall non-compliance rate is lower than those reported by (-1), but comparable to those reported by (1). The average red-light running rate for e-bikes, e-scooters and bicycles was found to be.%,.0%, and 1.%, respectively. The results of proportionality tests suggested that the red-light running rates for e-bikes and e-scooters are significantly higher than those for bicycles. The finding is consistent with those of previous studies (-1). However, the difference in the red-light running rates for e-bikes and e-scooters was not found to be statistically significant. The red-light running rate for various two-wheeled vehicles was also compared for different rider groups and traffic conditions in Table. In most scenarios the red-light running rates for e-bikes and e-scooters are actually quite comparable. The only exception is with young riders - it was found that the young riders of e-scooters are less likely to run against red lights as compared to the young riders of e-bikes. At signalized intersections, a rider needs to make a binary decision when the pedestrian traffic signal turns red to stop or to cross the intersection against the red light. Theoretically, a number of factors may affect riders decision. The factors include, but are not limited to the geometric characteristics and the traffic control features of the signalized intersection, the pedestrian facilities, the traffic volume that is in conflict with two-wheeled vehicles, the type of the two-wheeled vehicles, and the social-economical characteristics of the rider, etc. In this study, a binary logit model was developed to evaluate how various variables affect the red-light running rates of two-wheeled vehicles at signalized

8 intersections. The binary logit model has been widely used for predicting a binary dependent variable as a function of explanatory variables (0-). The method has also been used by previous researchers for analyzing the influential factors that contribute to the red light violations of pedestrians and cyclists at signalized intersections (-1, -). Descriptive statistically of the initially considered explanatory variables are included in Table. Model specification started from calculating the Pearson correlation and the Kendall s tau-b correlation coefficients to identify possible correlations between various candidate variables. Stepwise variable selection was conducted to select the explanatory variables that should be included in the binary logit model. The log likelihood at the convergence of various models was compared. The model with the highest log likelihood at convergence was considered the best. The specification results of the best model are given in Table. The best model has fifteen explanatory variables, including eleven binary variables. All variables in the best model are statistically significant with a 0% level of confidence. The odds ratio (OR) was estimated to quantitatively evaluate the impacts of various explanatory variables on the chance of a rider crossing against red lights (see Table ). The odds ratio of an explanatory variable represents the increase in the odds of the outcome if the value of the variable increases by one unit (). The OR for the binary variables e-bike and e-scooter are 1. and 1.1, respectively, indicating that the riders of e-bikes and e-scooters are 0. and 0.1 times more likely to run against red lights than the riders of bicycles. The finding is quite intuitive and consistent with those of previous studies (-1). Field observation revealed that with higher operating speeds the riders of e-bikes and e-scooters feel more confident that they can cross intersections more quickly, resulting in increased inclination to cross against red lights. Another finding is that the OR for the binary variables e-bike and e-scooter are actually quite similar, which confirms the finding that the difference in the red-light running rates for e-bikes and e-scooters is not statistically significant. The gender and age of riders significantly affect their red-light running rates. Male riders are 0.1 times more likely to run against red lights than female riders. Young and middle-aged riders are 1. and 0. times more likely to run against red lights than older riders. The odds ratios for the two binary variables for group size are. and. respectively, indicating that a rider is more likely to cross against a red light when there are fewer people waiting at the intersection. The finding is consistent with those of previous studies and the possible explanation for this phenomenon can be related to the theory of social control (, ). The OR for the binary variable peak period is 1.1, indicating that riders are 0.1 times more likely to run against red lights during the morning peak periods than during the afternoon peak periods. The finding is intuitive because during morning peak periods riders are usually travelling under pressure with the motivation of not being late for work.

9 Table. Comparison of the red-light running rates for e-bikes, e-scooters and bicycles Category Type E-bike E-scooter Bicycle Overall P-val. a P-val. b P-va Gender Male.% (/).% (0/) 1.% (1/).0% (/0) Female.% (1/) 1.% (/0) 1.% (1/) 1.0% (0/1) Young.0% (/1).% (/1).% (/).% (1/) ge group Middle.% (0/).% (/) 1.% (1/).% (/) Older 1.1% (0/1) 1.% (0/10) 1.% (/) 1.% (/) edestrian Flashing 1.% (0/1) 1.% (0/).% (/) 1.% (1/1) nals type Countdown.% (0/).0% (/1) 0.% (/).% (/) nflicting volume Low 0.% (1/) 0.% (/) 0.% (1/).% (/1) Medium.% (/).0% (1/10) 1.% (/01).% (/1) High 0.% (/) 1.% (/) 1.% (/) 1.% (/1) Kunming.% (10/0).% (/) 0.% (/).% (/0) City Nanjing.% (0/).% (/1) 1.% (1/1).1% (1/0) Overall.% (0/1).0% (/1) 1.% (/1).% (/) a p-value for the proportionality tests between e-bikes and e-scooters b p-value for the proportionality tests between e-bikes and bicycles c p-value for the proportionality tests between e-scooters and bicycle

10 Table. Descriptive statistics of candidate explanatory variables of the red-light running rate model Variables Min Max Mean Std. Category frequency Gender Male (0), female (1) Age Young (), middle-aged (), older () Vehicle type E-bike (1), e-scooter (1), bicycle (1) Group size Vehicles number < (), vehicles number ~ (), vehicles number > (1) Cross section Type 1 (1), type (1), type () Peak period Morning (0), afternoon (1) Pedestrian signals type Flashing (1), countdown () City Kunming (0) Nanjing (0) Roadway width (m).. -- Green ratio Volume of two-wheelers in min Conflicting traffic volume in min The geometric characteristics, traffic control features, and traffic conditions also significantly affect the rate of red light violations of two-wheelers at the selected signalized intersections. The red-light running rate decreases with an increase in the width of the crossing street, the conflicting traffic volume, and the green ratio; while slightly increases as the volume of the two-wheeled vehicles increases. Note that the green ratio is defined as the ratio of the duration of pedestrian green phase to the cycle length. Results of the OR analyses show that riders at the intersections with type cross sections are more likely to run against red lights as compared to those at the intersections with other types of cross sections. As shown in Figure, the sites with type cross sections are designed with raised medians in the middle of the crossing streets. The presence of raised medians provides refuge areas to pedestrians and cyclists as they are crossing intersections. Theoretically, it will result in improved safety to pedestrians and cyclists. However, the findings of the study suggest that the presence of raised medians also encourage the riders of e-bikes and e-scooters to cross against red lights. The results of OR analyses also suggest that at the sites designed with countdown pedestrian signals, riders are.1 times more likely to cross intersections against red lights as compared to those at the sites where flashing pedestrian signals are installed. As mentioned before, the countdown timers display the remaining time of the red and green phases of pedestrian signals. Field observation revealed that with exact timing information, riders are actually more inclined to cross against red lights, especially when a

11 pedestrian red light has just turned on- in this condition the riders may lose patience with the perception that they need to wait for the whole cycle, or at the end of a red phase- riders in this condition may choose to enter intersections before green onset. Table. Specification results of the red-light running rate model Variables Coefficient S.E. P value OR % C.I. for OR Intercept Gender Male vs female ~1. Age Young vs older ~.1 Middle-aged vs older ~1. Vehicle type E-bike vs bicycle ~1.1 E-scooter vs bicycle ~1.01 Group size Group size 1 vs Group size ~1. Group size vs Group size ~.0 Cross section Type 1 vs type ~.0 Type vs type ~.0 Roadway width ~0. Peak Period Morning vs Afternoon ~1.0 Pedestrian signals type Countdown vs flashing ~. Green ratio ~1. Volume of two-wheelers in min ~1.00 Conflicting traffic volume in min ~0. Number of observations: L(c) =.; L(β) =.1 [L(c) L(β)] = 1.1 McFadden R = 0.1, Corrected McFadden R = 0. Traffic conflicts caused by red-light running behaviors Of the observed 1,1 red-light running two-wheelers,.% have been involved in traffic conflicts. It was found that bicycles were slightly more likely to be involved in traffic conflicts than e-bikes and e-scooters as their riders were crossing intersections during a red pedestrian phase. A possible

12 explanation is that the lower speeds of bicycles may pose their riders to increased risks of conflicts with other vehicles as they were crossing intersections. For the riders who entered intersections during different stages of a red pedestrian phase, the chance of traffic conflicts differs dramatically. Around 0% of the red-light running behaviors occurred during the first and the last three seconds of the red pedestrian phase (see Figure (a)). However, for the riders who entered intersections during the initial and late stages of the red phase the chance of traffic conflicts is much smaller than those who entered intersections during the middle stage (see Figure (b)). The finding is intuitive because the initial stage of the red pedestrian phase corresponds to the initial stage of the green interval in the conflicting approach (see Figure (c)). Considering the start-up lost time, it usually takes two to three seconds before the conflicting vehicles arrive at the conflict points. The riders of two-wheelers may take this time gap to cross intersections without traffic conflicts. During the last three seconds of the red pedestrian phase, traffic light in the conflicting approach turns amber (see Figure (c)). Many drivers in the conflicting approach may decelerate vehicle speeds to avoid red-light violations. The riders of two-wheelers may take this opportunity to cross intersections without bringing significant disturbances to the conflicting traffic. Another interesting finding is that e-bikes are less likely to be involved in traffic conflicts in the middle stage than e-scooters and bicycles. Field observation suggests that e-bikes have comparable speeds with e-scooters, but are generally more flexible, resulting in maneuverability to avoid traffic conflicts. The research team also compared the TTC associated with the traffic conflicts caused by different types of two-wheelers during different stages of red pedestrian phases. It was found that the conflicts occurred during the initial and late stages have significantly smaller TTC than those occurred in the middle of the red pedestrian phase (see Figure (d)). Note that TTC is an indicator for the severity of traffic conflicts. The finding suggests that even though the chance of traffic conflicts is relatively small, running against red lights during the initial and late stages of red pedestrian phases may results in severe traffic conflicts. As a result, red-light running during the initial and late stages of red pedestrian phases should not be ignored while considering the safety impacts of two-wheelers at signalized intersections. Crossing speeds Crossing speed can be considered a safety measure that is related to not only the risks, but also the severity of crashes. The frequency histogram and cumulative distribution curves for the crossing speeds of various two-wheeled vehicles are displayed and compared in Figure. The average crossing speed of e-bikes, e-scooters, and bicycles is 1., 1.0 and 1.1 km/h, respectively. The cumulative curve for bicycles is always to the left, while the curve for e-scooters is always to the right of the other two curves (see Figure (a)), indicating that e-scooters have the highest crossing speeds, followed by e-bikes, and then bicycles. The results of t-tests suggest that the differences in crossing speeds between various two-wheeled vehicles are all significant with a % level of confidence. 1

13 Figure. Characteristics of red-light running behaviors during different stages of red pedestrian phase 1

14 Figure. Comparison of the crossing speeds of various red-light running two-wheeled vehicles 1

15 The speed data were then divided into two groups to identify the difference in the crossing speeds between the riders who crossed against red lights and those who did not. The average crossing speed of the red-light running e-bikes, e-scooters, and bicycles is 1.1, 1.1 and. km/h, respectively. The results of t-tests suggest that the two-wheeled vehicles that crossed intersections against red lights have significantly lower crossing speeds than those who crossed intersections during green phases. The cumulative distribution curves for the red-light running e-bikes and e-scooters are mixed with each other, implying that they have comparable crossing speeds. The results of t-tests suggest that the difference in the crossing speeds between the red-light running e-bikes and e-scooters is not statistically significant. SUMMARY AND DISCUSSIONS A comparative analysis was conducted to compare the red-light running behaviors of the riders of e-bikes, e-scooters and bicycles as they were crossing signalized intersections. It was found that the red-light running rates for e-bikes and e-scooters are significantly higher than those for bicycles. The difference in the red-light running rates for e-bikes and e-scooters is not statistically significant. The binary logit model developed in this study shows that the red-light running rate of two-wheelers decreases with an increase in the width of the crossing street, the conflicting traffic volume, and the green ratio of the pedestrian phase; while slightly increases as the volume of the two-wheeled vehicles increases. Male riders are more likely to run against red lights than female riders. Young riders are the most likely to run against red lights, followed by the middle-aged riders, and older riders. A rider is more likely to cross against a red light when there are fewer people waiting at the intersection. Pedestrian facilities also significantly affect the red-light running rates of two-wheeled vehicles. The riders of two-wheeled vehicles at the intersections designed with raised medians on the crossing streets and countdown pedestrian signals are more likely to run against red lights. Traffic conflicts were used as surrogate measures for comparing the safety impacts of the red-light running behaviors of various two-wheelers. It was found that bicycles were slightly more likely to be involved in traffic conflicts than e-bikes and e-scooters as they were crossing intersections during a red pedestrian phase. In addition, e-bikes are less likely to be involved in traffic conflicts in the middle stage of a red pedestrian phase than e-scooters and bicycles. Comparing the speeds at which two-wheeled vehicles crossing the selected intersections we found that both e-scooters and e-bikes have higher crossing speeds than bicycles. Even though e-scooters have significantly higher speeds than e-bikes as they were crossing intersections during green phases, the difference in the crossing speeds between red-light running e-bikes and e-scooters is not statistically significant. The findings of this study provided insights into the factors associated with the red-light running behaviors of various types of two-wheeled vehicles. In China, both e-bikes and e-scooters are legally classified as bicycles - they need to be operated in bicycle lanes and the riders do not need to hold driver licenses and wear helmets. The research results show that with higher operating speeds the riders of 1

16 e-bikes and e-scooters are more likely to run against red lights than cyclists. Additional research is needed to test for the transferability of the research findings to other locations with heterogeneous traffic control features and human behaviors. In addition, considering the difficulties in field data collection, some of the social and economic characteristics of riders, such as their income, educational background, and profession, ect., were not included in the red-light running rate model. The age of riders was loosely classified as young, middle, and old; and estimated approximately based on riders facial appearance. The model can be improved by incorporating more detailed social-economic characteristics of riders. The authors recommend that future studies may focus on this issue. ACKNOWLEDGEMENT This research was sponsored by the National Natural Science Foundation of China (Grant No. 100 & 1). The authors also would like to thank the graduate research assistant at the School of Transportation at the Southeast University for their assistance in field data collection. REFERENCES 1. Chinese Statistical Yearbook, Chinese Statistical Press, Cherry, C., Weinert, J., and Yang, X., Comparative environmental impacts of electric bikes in Transportation Research Part D, 00, Vol. 1, No., Cherry, C. and He, M., Alternative methods of measuring operating speed of electric and traditional bikes in China-implications for travel demand. Journal of the Eastern Asia for Transportation Studies, 0, Vol., pp Parker, A., Electric power-assisted bicycles reduce oil dependence and enhance the mobility of the elderly. Australasian Transport Research Forum (ATRF), th, 00, Gold Coast, Queensland, Australia, Queensland Transport, viewed 1 August 00.. Parker, A., Green products to help move the world beyond oil: power assisted bicycles. Proceedings of Solar, th annual conference of the Australia and New Zealand Solar Energy Society, Deakin University, Geelong, 1.. CRTASR, China Road Traffic Accidents Statistics Report, Traffic Administration Bureau of China State Security Ministry, Beijing, China, 0.. Bai, L., Liu, P., Chen, Y. G., Zhang, X., and Wang, W., Comparative analysis of the safety effects of e-bikes at signalized intersections, Transportation Research Part D: Transport and Environment, 01,Vol. 0, pp. -.. Lin, S., He, M. Tan, Y., and He, M., Comparison study on operating speeds of e-bikes and bicycles: experience from field investigation in Kunming, China. Transportation Research Record: Journal of the Transportation Research Board, 00, Vol.0, pp. -.. Cherry, C and Cervero, R., Use characteristics and mode choice behavior of electric bike users in China. Transport Policy, 00, Vol.1, No., pp.-. 1

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