Feasibility analysis of phase transition signals based on e-bike rider behavior

Abstract

This article evaluates the feasibility of two scenarios of phase transition signals, that is, the flashing green together with red-yellow light and the green countdown together with red countdown, at signalized intersections in terms of e-bike rider behavior. An evaluation framework is first proposed. During the phase transition, the stop-go and start-up behavioral parameters are collected at four intersections in Shanghai, China. Sensitivity analysis is then performed to identify the most significant factors that influence the occurrence of traffic conflicts during the phase transition. Based on the above analysis results, case studies were finally done to look into safety performance of the two scenarios of phase transition signals, indicated by the distributions of post encroachment time at the conflict point and the occurring probability of extremely small post encroachment times. Research result shows the transition signal combination of green countdown + red countdown tends to cause traffic accidents more easily and thus less safe compared to the transition signal combination of flashing green + red-yellow. Unlike the conventional method generally based on the deterministic traffic flow theory, the proposed methodology has a wide application. With the aid of it, traffic engineers are capable of designing transition signals in a more scientific manner.

Determining E-Bike Drivers’ Decision-Making Mechanisms during Signal Change Interval Using the Hidden Markov Driving Model

“…These studies are consistent in showing that flashing green reduces red-light violations that may result in right-angle collisions but increases conflicts during approach that may result in rear-end collisions that call for immediate action rather than preparatory warnings. In addition, Tang et al [26] and Dong et al [27,28] studied the impact of flashing green on e-bike driving behavior and found that potential time is the dominant independent factor explaining the stop/pass decision of e-bike drivers. In these cases, flashing green seemed to enlarge the option zone, bringing the indecision zone earlier and resulting in more aggressive driving behavior with regard to passing through intersections.…”

A Comparative Study on Drivers’ Stop/Go Behavior at Signalized Intersections Based on Decision Tree Classification Model

“…ere are both positive and negative conclusions about the effect of FG in these kinds of literature studies. e positive results show that FG can warn the driver that the phase of green light is coming to an end, and the driver can reduce the incidence of DZ by reducing the driving speed, to avoid red light violations [1,2,9]. FG signal essentially plays a role in prolonging the duration of yellow light.…”

“…At signalized intersections in most cities of China, a 3 s green flashlight (FG) indicator and a 3 s yellow light (Y) indicator are the most common form of transition signal setting [1][2][3].…”

Video‐based method for detecting potential risks among multiple electric bikes

“…Oh and Kim [28] analysed the detailed vehicular movement and trajectories at intersections from traffic surveillance systems and estimated the crash potential with a probabilistic measurement. Dong et al [8] extracted the overall trajectories and investigated the distribution of the encroachment time and position to find significant factors influencing the interactions between cars and electric bikes. The above-mentioned methods try to define the potential conflict zone as a box of joint trajectories and describe the severity with the frequency numbers and distributions.…”

“…1). Shockingly, it is shown that more than half of the accidents at intersections are related to electric bikes in China [8], let alone the cyclists are much more vulnerable than the drivers in cars [9]. However, despite the plentiful experiments and analyses carried out for car flow and pedestrians [10][11][12][13][14], less attention is paid to electric bikes in the analysis of interactions and potential risks.…”