This study aimed to explore the factors influencing right-turning vehicles’ yielding behavior for crossing e-bikes at signalized intersections to improve safety for crossing e-bikes. Videos of different intersections were obtained through manual video recording and drone aerial photography. Spatiotemporal information data for right-turning vehicles and straight-through e-bikes were extracted through Tracker 6.0 software. Right-turning vehicle yielding decisions were categorized into three types: no yielding, decelerating to yield, and stopping to yield. Five potential variables influencing yielding decisions were selected: personal attributes of e-bike riders, traffic characteristics of e-bikes, traffic characteristics of right-turning vehicles, road characteristics, and right-turning vehicle–e-bike interaction influence characteristics. A multiple ordered logistic regression model was established to predict right-turn vehicle yielding decisions. Simultaneously calculating the OR (Odds Ratio) value reveals the likelihood of increased yielding probability under varying factors. For every one-unit increase in the number of crossing e-bikes, the yielding probability increases to 1.002 times the original value; for every one-unit increase in the average speed of right-turning vehicles, the yielding probability decreases to 0.406 times the original value; for every one-unit increase in the average crossing speed of e-bikes, the yielding probability increases to 1.737 times the original value. Compared with the straight + right-turn lane, a dedicated right-turning lane increases the yielding probability of right-turning vehicles to 4.2 times, and compared with not occupying a crosswalk, illegally occupying a crosswalk decreases the yielding probability of right-turning vehicles to 0.356 times. These findings offer valuable insights for enhancing the safety of e-bikes crossing signal-controlled intersections.
Ge et al. (Fri,) studied this question.