A Dynamic Choice Model to Estimate the User Cost of Crowding with Large Scale Transit Data

摘要: Efficient mass transit provision should be responsive to the behaviour of passengers. Operators often conduct surveys to elicit passenger perspectives, but these can be expensive to administer and can suffer from hypothetical biases. With the advent of smart card and automated vehicle location data, operators have reliable sources of revealed preference (RP) data that can be utilized to estimate transit riders’ valuation of service attributes. To date, effective use of RP data has been limited due to modelling complexities. We propose a dynamic choice model (DCM) for population-level longitudinal RP data to address prominent challenges. In the DCM, riders are assumed to follow different decision rules (compensatory and inertia/habit) and temporal switching between decision rules based on experience-based learning is also formulated. We develop an expectation–maximization algorithm to estimate the DCM …