DYNAMIC EARLY WARNING MODELING OF PASSENGER AGGREGATION RISK AT RAILWAY PASSENGER HUBS
Keywords:
Railway passenger hubs, Passenger aggregation risk, Dynamic early warning, Risk assessmentAbstract
Under conditions such as holiday travel peaks, concentrated train arrivals and departures, and operational disturbances, railway passenger hubs are prone to localized passenger aggregation in key areas such as security checkpoints, waiting halls, ticket gates, and platforms, which may further lead to congestion propagation and operational risk. To address the limitations of existing studies, including insufficient focus on railway passenger hub scenarios, inadequate dynamic characterization of risk identification, and limited management orientation of warning results, this study investigates the dynamic early warning of passenger aggregation risk at railway passenger hubs. First, the formation mechanism of passenger aggregation risk is analyzed from the perspectives of passenger demand fluctuations, facility service capacity constraints, and operational disturbance propagation, and a risk indicator system is established from three dimensions: passenger-state conditions, facility operations, and disturbance factors. On this basis, a dynamic early warning framework consisting of data input, state identification, risk assessment, and warning output is proposed to support graded identification and dynamic response for passenger aggregation risk in key functional areas. Finally, an illustrative case study is conducted to demonstrate the applicability of the proposed framework. The results show that passenger aggregation risk at railway passenger hubs exhibits significant time-varying, spatially heterogeneous, and propagative characteristics, while bottleneck areas such as security checkpoints and ticket gates are more sensitive in both risk formation and escalation. The proposed framework is capable of representing the evolution of risk and translating operational states into graded warning results with direct managerial relevance. This study provides theoretical support and methodological guidance for passenger flow safety management and operational optimization at railway passenger hubs.References
[1] Pshinko O, Charkina T, Martseniuk L, et al. Hubs as a key tool for improving the quality of the service and development of multimodal passenger traffic. Transport Problems, 2022, 17.
[2] Zhu Z, Zhang A, Zhang Y. Connectivity of intercity passenger transportation in China: A multi-modal and network approach. Journal of Transport Geography, 2018, 71: 263-276.
[3] Fernndez-Lobo A, Benavente J, Monzon A. Dynamic Management Tool for Improving Passenger Experience at Transport Interchanges. Future Transportation, 2025, 5(2): 59.
[4] Zhang S, Lo H K, Ng K F, et al. Metro system disruption management and substitute bus service: a systematic review and future directions. Transport Reviews, 2021, 41(2): 230-251.
[5] Rozario S D, Venkatraman S, Marimuthu M, et al. Creating smart cities: A review for holistic approach. Applied System Innovation, 2021, 4(4): 70.
[6] Yang Z, Liu X, Wang J, et al. Assessment on resilience of urban agglomeration transportation system considering passenger choice and load-capacity factor. Reliability Engineering & System Safety, 2025, 253: 110527.
[7] Jenelius E. Rail transport resilience to demand shocks and COVID-19//Rail infrastructure resilience. Elsevier, 2022:65-79.
[8] Zhang Z, Gou J. Risk Prevention and Control Strategies of Large Passenger Flow in Railway Passenger Stations. International Conference on Logistics, Informatics and Service Sciences: Springer, 2024: 395-404.