Exploring Factors Affecting the Yellow-Light Running Behavior of Electric Bike Riders at Urban Intersections in China

Abstract

Electric bikes play an important role in the urban transportation system in China. Yellow-light running behavior of riders is one of the most critical factors for e-bike riders involved in traffic crashes at intersection. The main purpose of this study is to explore how a variety of factors affect e-bike riders’ yellow-light running behaviors at intersection by a field observation conducted in Xi’an, China. Based on 396 e-bike riders who faced yellow-light samples, two analytical methods, the principle component analysis logistics model and a base logistics model, were employed to evaluate the impacts of contributing factors on e-bike riders’ yellow-light running behavior. The modeling results showed that seven variables significantly affect the e-bike riders’ yellow-light running behavior, which were the approaching speed of e-bike, the distance to stop line, riders’ age and gender attributes, type of e-bike, and the characteristics of intersection including the width of intersection and the existence of physical barriers. This study can provide valuable insights into understanding e-bike riders’ yellow-light running behavior and may also help decision makers propose countermeasures to reduce e-bike rider-related crashes at intersection.

Can I Trust You? Estimation Models for e-Bikers Stop-Go Decision before Amber Light at Urban Intersection

“…Logistics regression has been widely applied to analyze the risk behaviour of road users in previous researches, especially to examine the factors contributing to RLR and YLR illegal behaviour [4,20,28,32,33,45]. In this study, we developed two logit models, namely, a base logit model and a random parameter logit model, to analyze the e-bikers' behaviour with respect to the stop-go decision when they were facing the signal change from green to yellow.…”

“…It has been proved that direct observation can provide quite useful information to study road users' behavior by previous researches [11,29,30,36]. In this study, a natural observation was conducted in Xi'an, China, which has 10 million residents, and the ownership of e-bike has been over 3 million [4]. A typical four-leg intersection located in Huancheng South Road, which is the major corridor connecting east and west of Beilin district, and Wenyi North Road (H-W intersection) was selected for natural observation.…”

“…In China, the transportation system has been undergoing huge change in recent years. Electric bicycle (e-bike) has increased dramatically and constituted about 34% of all transportation modes in China, and the number of e-bikes has been in excess of 250 million in 2018 [4]. Without a doubt, 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]. Moreover, e-bike crashes were composed of 70% Chinese nonmotor traffic accidents in 2015 [4].…”

“…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]. Moreover, e-bike crashes were composed of 70% Chinese nonmotor traffic accidents in 2015 [4]. Intersection is one of the most dangerous parts in road network.…”