Analysis of Crossing Behavior and Violations of Electric Bikers at Signalized Intersections

Abstract

This paper focuses on investigating electric bikers’ (e-bikers) crossing behavior and violations based on survey data of 3,126 e-bikers collected at signalized intersections in Nantong, China. We first explore e-bikers’ characteristics of late crossing, incomplete crossing, and violating crossing behaviors by frequency analysis and duration distribution, and examine a few influential factors for e-bikers’ red-light running (RLR) behavior, including site type, crossing length and traffic signal countdown timers (TSCTs). E-bikers’ RLR behavior is further divided into three categories, namely GR near-violations, RR violations, and RG violations. Second, we use a binary logistic regression model to identify the relationship between e-bikers’ RLR behavior and potential influential factors, including demographic attributes, movement information, and infrastructure conditions. We not only make regression analysis for respective violation type, but also carry out an integrated regression of a census of all three types of violations. Some insightful findings are revealed: (i) the green signal time and site type are the most significant factors to GR near-violations, but with little impact on the other two violation types; (ii) the waiting time, waiting position, passing cars and crossing length exert considerable impact on RR violations; (iii) for RG violations, TSCTs, leading violators and gender are the most significant factors; (iv) it is also unveiled that site type, green signal time and TSCTs have negligible impact on the whole violations regardless of the violation types. Thus, it is more meaningful to investigate the impacts of these variables on e-bikers’ RLR behavior according to different violation types; otherwise, the potential relationship between some crucial factors and e-bikers’ RLR behavior might be ignored. These findings would help to improve intersection crossing safety for traffic management.

Can I Trust You? Estimation Models for e-Bikers Stop-Go Decision before Amber Light at Urban Intersection

“…Due to the low power output, the acceleration rate or deceleration rate, which also can be seen as the e-bike riders' stop-go decision, was the most critical factor affecting the speed of e-bike. With regard to the number of acceleration rate changes for e-bikers, consistent with the study proposed by Tang [31], the e-bikers who could know the remaining time of amber light from the countdown timer are more likely to accelerate to clear the intersection. e-bikers who decided not to change their initial acceleration rate (keep accelerating to pass the stop line) were found to have YLR violation with the probability of 271% higher than the riders who change their initial decision once time.…”

“…In our study, the natural observation results showed that 63% of e-bikers had a YLR violation when they arrived at intersection facing signal change interval, which is slightly lower than the result proposed by Bharat [13] (the proportion of YLR violation was 68.6% in motorized two wheel). However, the proportion of YLR e-bikers is relatively higher than the result reported by Tang et al [31] (the proportion of the GR near-violation, also called YLR violation, was 32%). e difference between these two studies may be caused by the different data collection process, which is that we only collected the e-bike riders' crossing behaviour who faced the signal change interval, while in Tang's study the authors recorded all e-bike riders' crossing behaviours during peak hours.…”

“…Dong et al focused on the effects of flash green signal on the YLR violation and they found that flashing green signals not just almost eliminate the DZ but also enlarge the OZ for e-bikers [30]. Tang et al concluded that the green-light time and intersection's form are the most significant factors to e-bikers' GR near-violation which is also called YLR violation [31].…”

“…A Hidden Markov Driving model proposed by Dong et al and Li et al [30,34,35] was used to explain the YLR behaviour of motor drivers and e-bikers. An integrated regression analysis developed by Tang et al was used to explain the probability of e-bikers' YLR behaviours [31]. Numerous researchers have tried to use many methods identifying the factors influencing e-biker's signal violation.…”

Understanding the Interaction between Cyclists’ Traffic Violations and Enforcement Strategies: An Evolutionary Game-Theoretic Analysis

“…Four types of factors have been identified in the literature that affect the probability of cyclists’ traffic violations, including sociodemographic (e.g., age and gender), psychological (e.g., attitude and subjective norms), riding condition (e.g., if they are carrying a passenger and using a phone), and ambient road environment (e.g., traffic volume and intersection types) [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. Despite that these studies provided valuable information for improving road safety, they cannot capture the interdependency between cyclists’ traffic violations and enforcement strategies (e.g., police patrol intensity and penalty severity for traffic violations).…”