Zhuangbin Shi

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.

Activity space offers a valuable perspective for analyzing urban travel behavior and evaluating the performance of transportation systems in increasingly complex urban environments. However, the research on measuring activity spaces in multimodal transportation contexts remains limited. This study investigates multimodal transportation activity spaces in Hangzhou using 2023 smart card data. Multimodal travel chains are extracted, and residents’ activity spaces are quantified using 95% confidence ellipses. By applying the XGBoost and GeoShapley models, this study reveals the nonlinear effects and geospatial heterogeneity in how built environment and socioeconomic factors influence activity spaces. The key findings show that the distance to the nearest metro station, commercial POIs, and GDP significantly shape activity spaces through nonlinear relationships. Moreover, the interaction between the distance to the nearest metro station and geographical location generates pronounced geospatial effects. The results highlight the importance of multimodal integration in urban transport planning and provide empirical insights for enhancing system efficiency and sustainability.