Public transit planning is a complex design problem that involves a wide range of research topics and methodologies. The critical input of these problems is the Origin-Destination (OD) demand and the quality of OD demand information is crucial for these complex design and planning problems. The main deficiency of OD demand data format currently used in various transit planning models is that the ODs are defined at transit stop-level. By using stop-level ODs, a decision-maker neglects the multi-modal nature of transit and loses the information of transit travel demand elasticity and flexibility, which results in suboptimal solutions for transit planning problems.
The objective of this project is to develop scalable inference methods for understanding and expressing public transit system utilization based on fundamental travel behavior. Based on the realization, that stop-level ODs as well as mode choices are themselves outcomes of travelers’ complex route choice decisions in a multi-modal travel environment, the PIs propose a methodology that identifies both the preference vector and true OD-pairs by collecting and analyzing Automatic Fare Collection (AFC) system-type data (stop-level ODs), as travelers make their multi-modal route choice decisions in a given stochastic travel environment. The proposed methodology is developed to utilize "big data" that captures system-wide demand changes with respect to changes in time-dependent travel environment. The methodological framework is then applied for Seoul Metropolitan public transit system transaction data.