提出时空规划框架,动态优化多轨距铁路的行车路线并应对突发干扰。
A Temporal Planning Framework for Disruption Aware Dynamic Route Optimization in Heterogeneous Railway Systems
- 用PDDL 2.1建模铁路运行,显式处理轨距兼容与多种扰动场景。
- 在含1000个轨道点、120趟列车的200个实例上成功生成无冲突时序计划。
- 适合铁路调度系统研发者与自动化运维团队参考,提升安全与效率。
高效的路径优化对保障铁路运营的安全与准点至关重要,尤其在多轨距异构铁路网络中,列车速度、停靠模式差异以及基础设施兼容性约束使协调复杂度显著上升。单线系统中,所有列车共享同一轨道,需频繁换道,挑战加剧。随机扰动事件如线路封锁、列车阻塞、机车故障和限速等进一步增加运营不确定性,导致时刻表偏离。然而现有研究主要聚焦高层时刻表制定,忽略诸如换道协调等操作细节,迫使人工决策,增大安全风险。本文提出基于时空规划的动态路径优化与扰动管理框架,将铁路运营建模为使用PDDL 2.1的时空规划问题,显式刻画轨距兼容性约束与多样化扰动情景。该框架生成无冲突的带时间戳操作计划,明确指定优化时刻表与可执行动作序列。为评估框架性能,构建包含200个实例的基准测试集,涵盖最多1000个轨道点和120趟列车。采用两种先进时空规划器及一个计划验证工具进行测试。实验结果表明,该框架能有效生成异构铁路系统的时空操作计划,妥善处理多轨距约束与各类扰动,显著降低对人工决策的依赖。
原文摘要 · Abstract (English)
Efficient route optimization play a vital role in ensuring both safety and punctuality in railway operations. It is very crucial particularly in heterogeneous multi-gauge railway networks with varying train speed, stopping pattern, infrastructure compatibility constraints increase coordination complexity. In single-track systems these challenges are further intensify due to all trains to share the same track and requires frequent track switching.Stochastic disruptions events including blocked tracks, blocked trains, engine failure and speed slowdowns introduces additional unpredictability in operations and deviate the timetable. However, existing studies predominantly focuses on high-level timetabling, omitting operational details such as track switching coordination. As a result leaving decision to human operators, increasing safety risks into railway operations. This study proposes a framework based on temporal planning for dynamic route optimization and disruption management in heterogeneous railway systems. The framework formulates railway operations as a temporal planning problem using PDDL 2.1 with explicitly modeling gauge compatibility constraints and diverse disruption scenarios. It generates conflict-free timestamped operational plans specifying both optimized schedules and executable action sequences. To evaluate the proposed framework, we developed a benchmark problem set with 200 instances using up to 1,000 track points and 120 trains. Two state-of-the-art temporal planners and a plan validator were employed to assessed the framework. The experimental results demonstrate that the framework effectively generates temporal operational plans for heterogeneous railway systems and handles multi-gauge constraints, disruptions, and reduces dependence on manual decision making.
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