@article{ref1,
title="Travel mode choice on multiday traveling occasions: a multilevel and mixed-effects approach",
journal="Transportmetrica A: transport science",
year="2019",
author="Fu, Xuemei",
volume="15",
number="2",
pages="1175-1194",
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. <br><br>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.<p /> <p>Language: en</p>",
language="en",
issn="2324-9935",
doi="10.1080/23249935.2019.1570384",
url="http://dx.doi.org/10.1080/23249935.2019.1570384"
}