Objective: The primary objective of this study was to compare the risky behaviors of e-bike, e-scooter, and bicycle riders as they were crossing signalized intersections. Methods: Pearson's chi-square test was used to identify whether there were significant differences in the risky behaviors among e-bike, e-scooter, and bicycle riders. Binary logit models were developed to evaluate how various variables affected the behaviors of 2-wheeled vehicle riders at signalized intersections. Field data collection was conducted at 13 signalized intersections in 2 cities (Nanjing and Kunming) in China. Results: Three different types of risky behaviors were identified, including stop beyond the stop line, riding in motorized lanes, and riding against traffic. Two-wheeled vehicle riders’ gender and age and traffic conditions were significantly associated with the behaviors of 2-wheeled vehicle riders at the selected signalized intersections. Conclusions: Compared to e-bike and bicycle riders, e-scooter riders are more likely to take risky behaviors. More specifically, they are more likely to ride in motorized lanes and ride against traffic.

A common cause of vehicle crashes on urban expressways lies in the unsafe behaviors of drivers. This study focused on analyzing the influence of various unsafe behaviors on crash duration. Based on actual video image of vehicle crashes, 14 unsafe behaviors were identified for the analysis of crashes on urban expressways. Using the correspondence analysis method, the correlation among unsafe behaviors and collision types was obtained. Nonparametric survival analysis was then presented to obtain the survival rate curves of sideswipe crashes and rear-end crashes. Finally, parametric survival analysis method can get the influence of unsafe behaviors on crash duration. The survival rate of any time was quantified through the reasoning of key unsafe behaviors for different types of crashes. The results show that there were striking differences in the duration among different types of crashes. The unsafe behaviors had a significant impact on duration for different types of crashes. This study focused on the duration under the influence of unsafe behaviors before the crash, and the results provide valuable information to prevent crashes, which can improve traffic safety.