A Data-Driven Path Choice Estimation Method Based on Passengers’ Time-Dependent Travel Characteristics

The complex patterns of passenger travel in metropolitan rail systems pose substantial challenges for daily operations. To allocate revenues and optimize train schedules, operators require accurate data on passenger flows and path choices. We propose a data-driven method for path choice estimation that captures time-dependent segmented travel information in public transport systems. The method combines mobile signals, automatic fare collection (AFC), and train operation data to infer passenger travel behaviors within the metro system. Aspatiotemporal metro hypernetwork models passenger travel and train operations along feasible paths. The proposed travel-chain reconstruction method estimates segmented travel time distributions for each station across multiple time periods. A nonlinear programming optimization model calibrates path choice probabilities by minimizing discrepancies between theoretical and observed origin-destination (OD) journey time distributions. Case studies using both synthetic and real world data validate the model’s performance for the Nanjing metro system, China. Synthetic data validation shows the proposed model achieves approximately 85%-90% prediction accuracy for OD paths with ascending transfers, and remains robust to changes in path travel times. Real data results show that the TIDE-PCE model accurately estimates true path fractions and observed exit demand, with superior performance during peak hours.