Citation

Tang K, Dong S, Wang F, Ni Y, Sun J. Transp. Res. Rec. 2012; 2317: 85-96.

Copyright

(Copyright © 2012, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.3141/2317-11

PMID

unavailable

Abstract

In most Chinese cities, electric bicycles and electrically assisted bicycles (e-bikes) have drastically increased in recent years and currently constitute the largest proportion of the nonmotorized traffic at signalized intersections. Proper treatment of e-bikes has become a vitally important issue in improving the operational efficiency and safety performance of signalized intersections. However, fundamental knowledge of the unique operating characteristics and behavior of riders of e-bikes under various conditions is insufficient. This study statistically analyzed critical behavioral parameters of e-bike riders and empirically modeled their start-up behavior at the green onset following a 3-s red-and-yellow signal and their stop-pass decision behavior at the yellow onset following a 3-s flashing green. Distribution types and parameters of desired speed, start-up time, acceleration rate, perception-reaction time, and deceleration rate were investigated with the use of highly accurate trajectory data. A temporal-spatial model was developed to interpret the start-up curve, and three binary logistic regression models were built to predict the stop-pass decisions for different rider groups. It was found that the start-up curve of e-bikes could be well described by a quadratic function and that the red-and-yellow signal significantly induced a hurried start. The potential time to the stop line at the decision point was found to be the dominant independent factor explaining the stop-pass decision of e-bike riders; the flashing green signal seemed to enlarge the option zone, bring the indecision zone earlier, and result in more aggressive passing behavior.