Flexible train formation strategies for connecting rail transit in integrated transport hubs under high-demand conditions

Abstract

To address the challenges of supply-demand imbalance in rail transit and the complex passenger flow interactions among multiple hub stations under high-passenger-volume scenarios, this study proposes an optimized rail transit scheduling method based on a flexible train formation strategy (FTFS). By constructing interaction parameters that characterize the coupling effects of high passenger flow across multiple hubs, a multiobjective optimization model is developed to minimize passenger waiting time at hub stations and operational costs. An improved nondominated sorting genetic algorithm incorporating chaotic mapping and adaptive evolutionary parameters is designed for efficient solution optimization. This method overcomes the limitations of fixed train formations by supporting diversified modular unit detachment and reconnection, enabling dynamic capacity adjustment and efficient rolling stock circulation. A case study on Nanjing Metro Line 1 demonstrates that the FTFS reduces the average waiting time at hub stations by 47.2%, alleviates train congestion by approximately 18.6%, and reduces the operational costs under low-demand scenarios by 44.8%. Pareto frontier analysis further reveals the trade-off mechanism between transport capacity elasticity and operational costs. These findings validate the effectiveness of the flexible train formation model in mitigating platform congestion and enhancing passenger flow evacuation efficiency at transport hubs, providing multiobjective decision-making support for managing extreme passenger flow during holidays and peak events.

Keywords

integrated transport hub / urban rail transit / flexible train formation / multiobjective / nondominated sorting genetic algorithm Ⅱ

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Funding

Key Project of the National Natural Science Foundation of China(52432011)

National Natural Science Foundation of China(524B2153)