Travel mode choice on multiday traveling occasions: a multilevel and mixed-effects approach. Issue 2 (2nd January 2019) - Fu, Xuemei
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.