A mode shift Bayesian network model for active travel demand management policies

Abstract

Promotion of mode shift from private to public transportation is one of the difficult tasks in modern travel demand management (TDM) especially for travelers in auto-dependent cities. This study investigates the role and impact of two active travel demand policies, i.e. incentive program and info-mobility system, on encouraging mode shift to public transportation with a questionnaire survey. The incentive program provides monetary or other form of incentives to encourage travelers to alter their travel behavior spatially or temporally. The info-mobility system refers to the use of information technology to disseminate real time travel information to travelers. A total of 670 respondents are surveyed in the Klang Valley region, Malaysia. Bayesian Network Modeling (BNM) is adopted as the analysis tool to predict the traveler mode shift intention. The developed model, termed as Mode Shift Bayesian Network Model (MSBaysnet), shows that: (1) the presence of an incentive program could change travelers’ perceived convenience of using public transportation, especially if they stay in the vicinity of public transportation stations; (2) the info-mobility system should be considered as a crucial component of an integrated public transportation system; (3) Bayesian Network has an advantage in modeling complex interactions of multiple TDM strategies; (4) no standalone TDM strategy works in promoting mode shift intention and synergistic effect of multiple policies is required to encourage mode shift. The findings derived from this study can aid policy maker in formulating useful mode shift strategies to improve public transportation ridership.