A strategy-based recursive path choice model for public transit smart card data

Document Type

Journal Article

Publication Date


Subject Area

mode - bus, mode - rail, place - australasia, technology - ticketing systems


Public transit smart card data, Public transit path choice, Optimal strategy transit assignment, Recursive path choice model


A recursive logit model is proposed for path choice modeling with transit smart card data in higher-frequency bus and rail services. In such circumstances, it is commonly assumed that passengers may arrive randomly to a stop and may behave according to a “strategy”; such a strategy is associated with a so-called “attractive” set of routes: a passenger selects a set of routes departing from the stop, and will board the next vehicle to depart from among that set of routes. We extend the conventional notion of attractive sets by introducing a measure of “attractiveness” that allows for randomness in the choice of attractive routes.

The proposed model uses a link-based (recursive, or sequential choice) formulation, rather than a path-based formulation, which has the advantage of including all path alternatives without the need for path set enumeration. The recursive formulation is also very suitable for smart card data because it allows model calibration with incomplete path choice observations.

The link-based approach was originally advocated by Nguyen et al. (1988) in the strategy-based transit assignment context, but without investigating methods for model calibration or empirical analysis. Recently, Fosgerau et al. (2013) and Mai et al. (2015) have presented methods to empirically estimate traffic path choice models using a link-based formulation. Our framework builds off these previous works to formulate and estimate a strategy-based path choice model with smart card data. The proposed model is tested with a 6 months extract from the smart card transactions in Brisbane, Australia, for two popular origin-destination pairs with diverse path alternatives.


Permission to publish the abstract has been given by Elsevier, copyright remains with them.


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