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.
Highlights
Electric bike as one of the flexible transportation modes is popular in China and other Asian countries, which constitutes about 34% proportion among all travel modes in China [1]
The growing violation in e-bikes has attracted widespread interest of researchers to model the signal violation behavior, few studies have investigated e-bike riders yellow-light running behavior. erefore, this study mainly reviewed the red-light running behavior of e-bike riders and the yellow-light running behaviors of motor vehicle drivers
Stepwise Regression Selection Model. e stepwise regression has been commonly used to deal with multicollinearity in logistics regression process. e stepwise regression selection method was used to extract possible combination of explanatory variables affecting the rider behavior at the yellow-light onset, and the final combination of variables was decided based on whether those were significant at a 95% confidence level using a T-test in the SPSS 22.0 software
Summary
Electric bike as one of the flexible transportation modes is popular in China and other Asian countries, which constitutes about 34% proportion among all travel modes in China [1]. Due to the convenience in congestion traffic, energy efficiency, and high manoeuvrability [2], the electric bike (e-bike) has experienced a tremendous growth in China and its total number was more than 250 million according to the China Bicycle Industry Information Center in 2018 [3]. Riders are considered as vulnerable road users since they are not protected by any metal structures of vehicles in traffic crashes [4]. E total number of road e-bike traffic accidents from 2016 to 2017 was 25990, which resulted in 4070 deaths and 28509 injuries [6]. According to the Chinese road rules, nonmotorized traffic including regular bicycles and electric bikes should obey the same signal as motor vehicles at signalized intersections. Due to the insufficient clearance time, riders may meet the opposite vehicles, causing a right-angle collision when doing a godecision at the onset of yellow-light. erefore, a study focusing on e-bike riders’ crossing behavior at intersection in yellow-light interval is imperative
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