Abstract
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.
Highlights
In recent years, the transportation system has been undergoing huge change
A higher proportion of normal yellow-light running (NYLR) e-bikers and a lower proportion of aggressive yellow-light running (AYLR) e-bikers were observed in the old-age group than the young- and middle-age riders (100% vs. 93.9% and 87.7%, 0% vs. 6.1% and 12.3%); the difference cannot be observed in the number of different yellow-light running (YLR) types for different age groups from the results of chisquare test (p 0.179)
To improve the intersection safety under mixed traffic condition, especially under fully autonomous driving condition in the near future, we have found out factors contributing to e-bikers’ stop-go decision to cross through stop mark during the signal change interval
Summary
The transportation system has been undergoing huge change. In China, the transportation system has been undergoing huge change in recent years. With the growing popularity of e-bike, many countries have experienced a tremendous growth in traffic crashes involving e-bike. Because e-bike is defined as nonmotor vehicle by most countries in the world, e-bikers are not required to have driving license which may cause them to overestimate their cycling technique. Due to the unskilled cycling performance and being not protected by any metal structures, the casualties of e-bikers were about 32579 in traffic accident from 2016 to 2017 [4, 5]. E-bike crashes were composed of 70% Chinese nonmotor traffic accidents in 2015 [4]. Intersection is one of the most dangerous parts in road
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