Citation

Guo Y, Wang J, Peeta S, Ch. Anastasopoulos P. Travel Behav. Soc. 2020; 19: 137-150.

Copyright

(Copyright © 2020, Elsevier Publishing)

DOI

10.1016/j.tbs.2020.01.002

PMID

unavailable

Abstract

This study investigates the personal and societal impacts of motorcycle ban policy on the home-to-work morning commute of motorcyclists using self-reported travel behavioral data and stated travel mode shift response under such policy collected in Foshan City, China. The policy aims to force all motorcyclists in the city to shift to walk, bike, bus, or car mode by 2020. The complex impacts of the policy on travel mode shifts are studied across population sub-groups defined by gender and residential status (migrants and residents). For model estimation, random parameters multinomial logit models with heterogeneity in parameter means and variances were estimated to better track unobserved heterogeneity compared to their counterpart models with fixed means and variances. Model estimation results show that contributing factors of travel mode shift response include individual and household sociodemographic characteristics, travel-related behaviors, and residential location factors. In addition, out-of-pocket cost, opportunity cost of travel time, emissions, and energy consumption before and after motorcyclists would make their travel mode shifts and safety-related impacts were quantified and compared across the population sub-groups. The results show that motorcyclists, on average, would experience significant increases in their out-of-pocket cost and opportunity cost of travel time, particularly for male migrant motorcyclists. These results suggest that policy and infrastructural support for using public transit, walk, and bike modes, household mobility, and plan to purchase a car were likely to affect the personal and societal impacts of the motorcycle ban policy on travel mode shifts.

Language: en

Keywords

China; Home-to-work morning commute; Motorcycle ban policy; Random-parameters logit model with heterogeneity in means and variances; Travel mode shifts