Citation

Deng J, Gao L, Chen X, Yuan Q. Transportation (Amst) 2024; 51(4): 1547-1573.

Copyright

(Copyright © 2024, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s11116-023-10377-1

PMID

unavailable

Abstract

Traffic congestion has caused great concern among policymakers in large cities around the world. In contrast with constantly increasing transport supply, individual-based active travel demand management (ATDM) has been proposed as a more efficient policy alternative for combating congestion. However, how individuals make routing choices during commuting in response to ATDM incentives is still largely unknown, given the lack of individual travel data. Using a desensitized one-week travel trajectory data set involving 5641 personal electric vehicles, we examine the major influencing factors of commuting route stability during working days and make suggestions on targeting the most responsive commuters. We first filter family-used vehicles by clustering vehicle usage patterns through employing the Gaussian mixture model and interpret drivers' route choice behaviors during morning peak hours. To look for factors affecting route stability, we develop a generalized additive model and find that route stability is significantly associated with road network density of origins and destinations, departure time, travel duration, commuting distance, reliability of the expressway, and volatility of the congested sections by which the routes passed. The empirical results may contribute to the understanding of individual route choices, and help urban managers develop more refined ATDM policies to alleviate traffic congestion in the future.

Language: en

Keywords

ATDM; Electric vehicle trajectory data; GAM; GMM clustering; Route choice; Traffic congestion