Citation

Fu X. Transportmetrica A: Transp. Sci. 2019; 15(2): 1175-1194.

Copyright

(Copyright © 2019, Informa - Taylor and Francis Group)

DOI

10.1080/23249935.2019.1570384

PMID

unavailable

Abstract

This study is designed to provide a new perspective for the multiday traveling mode choice analysis. A GPS-enabled activity-travel survey conducted in Shanghai, China is used to obtain the multiday traveling dataset. Complete travel records over consecutive days are available for each respondent, which are recognized as a hierarchical and nested structure, i.e. trips nested within a day and days nested within individual traveler. A multilevel and mixed-effects logistics regression model is employed, investigating not only the effect of trip-, day-, and individual-level variables on mode choice, but whether or not particular effect varies across days and individuals.

RESULTS regarding the fixed-effect part suggest that only limited individual-level predictors play a significant role, and the effect of day-of-week variable directly demonstrates the nonexistence of a 'typical' traveling day. More importantly, a substantial amount of random effects is revealed, which explicitly justifies the multilevel approach.

Language: en

Keywords

longitudinal data; mixed-effects; multiday traveling; Multilevel analysis; travel mode choice