高效的线路优化对于确保铁路运营的安全和准点起着至关重要的作用。这是非常重要的，特别是在列车速度不同的异构多轨铁路网络中, 停车模式, 基础设施兼容性约束增加了协调复杂性。在单轨系统中，这些挑战进一步加剧，因为所有列车共享同一轨道，并且需要频繁跟踪此 http URL 中断事件，包括轨道阻塞, 列车阻塞, 发动机故障和速度减慢，导致运营中额外的不可预测性并偏离时间表。然而,现有的研究主要集中在高层时间表,忽略了轨道切换协调等操作细节。因此，将决定权留给人类操作员,，增加了铁路运营的安全风险。本研究提出了一种基于时间规划的框架，用于异构铁路系统中的动态路线优化和中断管理。该框架使用 PDDL 2.1 将铁路运营制定为时间规划问题，并明确建模轨距兼容性约束和不同的中断场景。它生成无冲突的带时间戳的操作计划，指定优化的时间表和可执行的操作序列。为了评估所提出的框架,，我们开发了一个基准问题集，其中包含 200 个实例，使用多达 1,000 个轨迹点和 120 个列车。使用两个最先进的时间规划器和一个计划验证器来评估该框架。实验结果表明，该框架有效地为异构铁路系统生成时间运营计划，并处理多轨距约束, 中断, 并减少对手动决策的依赖。

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 this http URL 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.

科目:人工智能(cs.AI)

Subjects: Artificial Intelligence (cs.AI)