To address the challenges of supply-demand imbal-ance in rail transit and the complex passenger flow interactions among multiple hub stations under high-passenger-volume scenarios,this study proposes an optimized rail...To address the challenges of supply-demand imbal-ance 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 strat-egy(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 sta-tions and operational costs.An improved nondominated sorting genetic algorithm incorporating chaotic mapping and adaptive evolutionary parameters is designed for efficient so-lution optimization.This method overcomes the limitations of fixed train formations by supporting diversified modular unit detachment and reconnection,enabling dynamic capac-ity 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 re-duces the operational costs under low-demand scenarios by 44.8%.Pareto frontier analysis further reveals the trade-off mechanism between transport capacity elasticity and opera-tional costs.These findings validate the effectiveness of the flexible train formation model in mitigating platform conges-tion and enhancing passenger flow evacuation efficiency at transport hubs,providing multiobjective decision-making support for managing extreme passenger flow during holi-days and peak events.展开更多
基金Key Project of the National Natural Science Foundation of China (No. 52432011)the National Natural Science Foundation of China (No. 524B2153)。
文摘To address the challenges of supply-demand imbal-ance 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 strat-egy(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 sta-tions and operational costs.An improved nondominated sorting genetic algorithm incorporating chaotic mapping and adaptive evolutionary parameters is designed for efficient so-lution optimization.This method overcomes the limitations of fixed train formations by supporting diversified modular unit detachment and reconnection,enabling dynamic capac-ity 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 re-duces the operational costs under low-demand scenarios by 44.8%.Pareto frontier analysis further reveals the trade-off mechanism between transport capacity elasticity and opera-tional costs.These findings validate the effectiveness of the flexible train formation model in mitigating platform conges-tion and enhancing passenger flow evacuation efficiency at transport hubs,providing multiobjective decision-making support for managing extreme passenger flow during holi-days and peak events.
