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Understanding passengers' path choice behavior in urban rail systems is aprerequisite for effective operations and planning. This paper attemptsbridging the gap by proposing a probabilistic approach to infer passengers'path choice behavior in urban rail systems using a large-scale smart card data.The model uses latent classes and panel effects to capture passengers' implicitbehavior heterogeneity and longitudinal correlations, key research gaps in bigdata driven behavior studies. We formulate the probability of each individual'sarrival time at a destination based on their path choice behavior, and estimatecorresponding path choice model parameters as a maximum likelihood estimationproblem. The original likelihood function is intractable due to the exponentialcomputation complexity. We derive a tractable likelihood function and propose anumerical integral approach to efficiently estimate the model. Also, we proposea method to calculate the t-statistic of the estimated choice parameters basedon the numerically estimated Hessian matrix and Cramer-Rao bound (the lowerbound on the coefficient variance). Case studies using synthetic data validatethe model performance and its robustness against parameter initialization andinput errors, and highlight the importance of incorporating crowding impact inpath choice estimation. Applications using actual data from the Mass TransitRailway, Hong Kong reveal two latent groups of passengers: time-sensitive (TS)and comfort-aware (CA). TS passengers are those who are more likely to choosepaths with short travel times. Most of them are regular commuters with hightravel frequency and less schedule flexibility. CA passengers care more aboutthe travel comfort experience and choose paths with less walking and waitingtimes. The proposed approach is data-driven and general to accommodate otherdiscrete choice structures.

Passenger Path Choice Estimation Using Smart Card Data: A Latent Class Approach with Panel Effects Across Days