title: Modeling mode choice behavior incorporating household and individual sociodemographics and travel attributes based on rough sets theory.
abstract: Most traditional mode choice models are based on the principle of random utility maximization derived from econometric theory. Alternatively, mode choice modeling can be regarded as a pattern recognition problem reflected from the explanatory variables of determining the choices between alternatives. The paper applies the knowledge discovery technique of rough sets theory to model travel mode choices incorporating household and individual sociodemographics and travel information, and to identify the significance of each attribute. The study uses the detailed travel diary survey data of Changxing county which contains information on both household and individual travel behaviors for model estimation and evaluation. The knowledge is presented in the form of easily understood IF-THEN statements or rules which reveal how each attribute influences mode choice behavior. These rules are then used to predict travel mode choices from information held about previously unseen individuals and the classification performance is assessed. The rough sets model shows high robustness and good predictive ability. The most significant condition attributes identified to determine travel mode choices are gender, distance, household annual income, and occupation. Comparative evaluation with the MNL model also proves that the rough sets model gives superior prediction accuracy and coverage on travel mode choice modeling.
authors.affiliation: Jiangsu Key Laboratory of Urban ITS, Southeast University, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Si Pai Lou No. 2, Nanjing 210096, China.
authors.forename: Long
authors.identifier: 0000-0003-3720-4093
authors.initials: L
authors.lastname: Cheng
authors.affiliation: Jiangsu Key Laboratory of Urban ITS, Southeast University, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Si Pai Lou No. 2, Nanjing 210096, China.
authors.forename: Xuewu
authors.initials: X
authors.lastname: Chen
authors.affiliation: School of Transportation, Nantong University, Nantong 226019, China.
authors.forename: Ming
authors.initials: M
authors.lastname: Wei
authors.affiliation: Jiangsu Key Laboratory of Urban ITS, Southeast University, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Si Pai Lou No. 2, Nanjing 210096, China.
authors.forename: Jingxian
authors.initials: J
authors.lastname: Wu
authors.affiliation: Jiangsu Key Laboratory of Urban ITS, Southeast University, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Si Pai Lou No. 2, Nanjing 210096, China.
authors.forename: Xianyao
authors.initials: X
authors.lastname: Hou
mesh_terms: D002755:Choice Behavior; D003625:Data Collection; D005191:Family Characteristics; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D008962:Models, Theoretical; D012449:Safety; D012718:Set, Psychology; D012923:Social Class; D014195:Travel