Citation

Gao Y, Qu Z, Jiang J, Song X, Xia Y. J. Transp. Eng. A: Systems 2021; 147(2): e04020155.

Copyright

(Copyright © 2021, American Society of Civil Engineers)

DOI

10.1061/JTEPBS.0000478

PMID

unavailable

Abstract

The increase in e-bikes has aggravated traffic conflicts and casualties at intersections. However, existing signal control methods focus on motor vehicles, ignoring the influence of expansion behavior of e-bikes on motor vehicles. In order to better balance the traffic benefits between them, this paper proposes a new release mode named e-bike early green and establishes a signal timing model of mixed traffic flow considering e-bike early green (MTEG). In particular, an effect strength indictor that reflects the degree of influence of e-bikes on motor vehicles is put forward. Then, based on this indicator and e-bike ratio, the calculation model for e-bike early green time is built. The Nondominated Sorting Genetic Algorithm II is applied to solve the MTEG model that considers multi-objective optimization coordination. The results show that, when the e-bike ratio is in the range of 0.4-0.6, compared with the method proposed by the Transport and Road Research Laboratory (TRRL), the method proposed by the Australian Road Research Board (ARRB), and Improved-Webster method, the maximum improvement values of the MTEG method in cycle length, average vehicle delay, intersection capacity, and average stops per vehicle are −5.56%−5.56%$-5.56\%$, −22.04%−22.04%$-22.04\%$, +2.30%+2.30%$+2.30\%$, and −8.00%−8.00%$-8.00\%$, respectively. The outcomes provide a signal timing basis and technical support for a mixed traffic flow environment containing numerous e-bikes.

Language: en