Transportation demand management (TDM), long used to reduce car traffic, receives increasing attention as means to ease congestion in overcrowded public transit systems. A more structured approach to transit-specific TDM can help agencies find better combinations of demand management and infrastructure investments to satisfy customer need. This paper develops a framework for public transportation demand management (PTDM) including problem identification and formulating program goals, program design, and program evaluation. The problem identification phase includes a spatio-temporal passenger flow analysis, while the design phase categorizes and integrates possible interventions along spatial, temporal, modal, and targeted user group parameters. The evaluation examines effectiveness, efficiency, and acceptability, and utilizes detailed smart card transaction data for analysis at system-wide, group, and individual levels. We apply the framework in a case study of the pre-peak pricing policy in Hong Kong’s MTR network. Contrasting data from before and after the implementation of the scheme, we identified six customer groups using cluster analysis. We used a panel of 20,000 users and the change-point analysis to to study policy-induced behavior shifts at the individual level. Estimating a logit model we identified that the duration of required departure time shift, departure time variability, fare savings, and price sensitivity are key factors influencing behavioral change.

A public transportation demand management framework helps transit agencies structure policy design and evaluation. Multi-level evaluation using disaggregate smart card data reveals heterogeneous policy responses among disparate groups, implying that targeting specific groups can improve incentives. PTDM complements infrastructure or service expansions, providing additional policy tools for transportation planners.