Scholarlink

<p>Rapid transit systems (RTSs) are essential for sustainable urban mobility, especially in high-density metropolitan areas where passenger demand varies significantly over time and space. Efficient planning of such systems requires balancing service quality with operational cost under complex operational constraints. This study presents a multi-objective framework for the joint optimization of frequency setting, stop and path planning, timetabling, and train circulation in RTSs under time-dependent demand. A mixed-integer nonlinear programming (MINLP) model is formulated to capture these interdependent planning decisions while considering first-in-first-out-based traffic assignment and operational limitations. To solve this large-scale problem, a tailored NSGA-II algorithm is developed, enabling flexible control over journey patterns, routing strategies, and rolling-stock allocation. The framework is validated via extensive numerical experiments based on real-world data from Xi’an Metro Line 1. Compared with traditional operations such as all-stop, skip-stop, and short-turn strategies, the proposed approach achieves reductions in operational costs and passenger waiting times while guaranteeing full coverage of passenger demand. The results highlight the potential of demand-responsive planning for improving service efficiency and resource use in urban transit systems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Pareto-Based Joint Optimization for Rapid Transit Lines: Integrating Flexible Routing, Stop Planning, and Train Scheduling under Time-Dependent Demand

  • 期刊论文

  • 发表日期：

    2026年6月23日

查看全文
  • Mehdi Oldache,
  • Wenliang Zhou

摘要

Rapid transit systems (RTSs) are essential for sustainable urban mobility, especially in high-density metropolitan areas where passenger demand varies significantly over time and space. Efficient planning of such systems requires balancing service quality with operational cost under complex operational constraints. This study presents a multi-objective framework for the joint optimization of frequency setting, stop and path planning, timetabling, and train circulation in RTSs under time-dependent demand. A mixed-integer nonlinear programming (MINLP) model is formulated to capture these interdependent planning decisions while considering first-in-first-out-based traffic assignment and operational limitations. To solve this large-scale problem, a tailored NSGA-II algorithm is developed, enabling flexible control over journey patterns, routing strategies, and rolling-stock allocation. The framework is validated via extensive numerical experiments based on real-world data from Xi’an Metro Line 1. Compared with traditional operations such as all-stop, skip-stop, and short-turn strategies, the proposed approach achieves reductions in operational costs and passenger waiting times while guaranteeing full coverage of passenger demand. The results highlight the potential of demand-responsive planning for improving service efficiency and resource use in urban transit systems.