Abstract
Departure time choice is critical for subway passengers to avoid congestion during morning peak hours. In this study, we propose a Bayesian network (BN) model to capture departure time choice based on data learning. Factors such as travel time saving, crowding, subway fare, and departure time change are considered in this model. K2 algorithm is then employed to learn the BN structure, and maximum likelihood estimation (MLE) is adopted to estimate model parameters, according to the data obtained by a stated preference (SP) survey. A real-world case study of Beijing subway is illustrated, which proves that the proposed model has higher prediction accuracy than typical discrete choice models. Another key finding indicates that subway fare discount higher than 20% will motivate some passengers to depart 15 to 20 minutes earlier and release the pressure of crowding during morning peak hours.
Highlights
Passenger congestion causes a safety hazard during morning peak hours in subway stations
With the help of stated preference (SP) data collected in subway stations, the K2 algorithm and maximum likelihood estimation (MLE) are employed to estimate the Bayesian network (BN) structure and its parameters, respectively
An multinomial logit (MNL) model is developed to verify the accuracy of the proposed model
Summary
Passenger congestion causes a safety hazard during morning peak hours in subway stations. Thorhauge et al [3] analyzed the departure time choice of drivers and public transport commuters using the structural equation model and MNL, which suggested that fixed start time of work had a strong effect on departure time choice. These models cannot address the nonlinear feature of departure time choice in subways in a quality manner. This paper is organized as follows: Section 2 presents a brief introduction of BN; Section 3 provides a new BN approach that models departure time choice of subway passengers, where algorithms to determine model structure and parameters are developed.
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