Electric bike (e-bike) riders’ inappropriate go-decision, yellow-light running (YLR), could lead to accidents at intersection during the signal change interval. Given the high YLR rate and casualties in accidents, this paper aims to investigate the factors influencing the e-bikers’ go-decision of running against the amber signal. Based on 297 cases who made stop-go decisions in the signal change interval, two analytical models, namely, a base logit model and a random parameter logit model, were established to estimate the effects of contributing factors associated with e-bikers’ YLR behaviours. Besides the well-known factors, we recommend adding approaching speed, critical crossing distance, and the number of acceleration rate changes as predictor factors for e-bikers’ YLR behaviours. The results illustrate that the e-bikers’ operational characteristics (i.e., approaching speed, critical crossing distance, and the number of acceleration rate change) and individuals’ characteristics (i.e., gender and age) are significant predictors for their YLR behaviours. Moreover, taking effects of unobserved heterogeneities associated with e-bikers into consideration, the proposed random parameter logit model outperforms the base logit model to predict e-bikers’ YLR behaviours. Providing remarkable perspectives on understanding e-bikers’ YLR behaviours, the predicting probability of e-bikers’ YLR violation could improve traffic safety under mixed traffic and fully autonomous driving condition in the future.
Can I Trust You? Estimation Models for e-Bikers Stop-Go Decision before Amber Light at Urban Intersection
2020
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Modeling for Driver Decision-Making Behavior during Amber Signal Time at Intersection
Tema Archive | 2012
|Sharing the Road with Bikers: The Nigerian Experience
Online Contents | 1993
|Sharing the Road with Bikers: The Nigerian Experience
British Library Conference Proceedings | 1993
|Analyzing Aggressive Cycling Behaviors of E-Bikers in Guangzhou through Structural Equation Models
Transportation Research Record | 2024
|