TY  - JOUR
PY  - 2020//
TI  - Can i trust you? Estimation models for e-bikers stop-go decision before amber light at urban intersection
JO  - Journal of advanced transportation
A1  - Cai, Jing
A1  - Zhao, Jianyou
A1  - Xiang, Yusheng
A1  - Liu, Jing
A1  - Chen, Gang
A1  - Hu, Yueqi
A1  - Chen, Jianhua
A1  - Chen, Feng
SP  - e6678996
EP  - e6678996
VL  - 2020
IS  - 
N2  - 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<p />  <p>Language: en</p>
LA  - en
SN  - 0197-6729
UR  - http://dx.doi.org/10.1155/2020/6678996
ID  - ref1
ER  -