DataSim: A Machine Learning-powered simulation tool for rail timetable optimisation

DataSim：用于铁路时刻表优化的机器学习驱动的模拟工具

• 批准号: 10090014
• 金额: $ 37.89万
• 依托单位: DISTRIBUTED ANALYTICS SOLUTIONS LTD
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10090014
• 关键词: DataSim; Machine; Learning; powered; simulation

This project will deliver a Machine Learning powered simulation tool called DataSim, to empower rail operators with the ability to explore the impact of timetable changes on the network state. With DataSim, operators could simulate different scenarios and find the optimal solutions for efficient and reliable rail scheduling. DataSim uses a map interface which intuitively depicts rail asset position. Through this, rail operators can view the differences between planned and actual train positions, analyse the time lost or gained at each section of the journey, and view the arrivals and departures for each station on the network. This allows rail operators and decision makers to take a scientific approach to data analysis, mitigating management problems by empowering analysts to visualise and simulate services and the cascading impacts of unexpected delay scenarios including weather, trespass incidents, hardware failure, and other unforeseen circumstances.

该项目将提供一个名为DataSim的机器学习仿真工具，使铁路运营商能够探索时刻表变化对网络状态的影响。使用DataSim，运营商可以模拟不同的场景，并找到高效可靠的铁路调度的最佳解决方案。DataSim使用地图界面直观地描述铁路资产位置。通过这种方式，铁路运营商可以查看计划和实际列车位置之间的差异，分析每段旅程损失或获得的时间，以及查看网络上每个车站的到达和离开时间。这使得铁路运营商和决策者能够采用科学的方法进行数据分析，通过赋予分析师可视化和模拟服务以及意外延误情景（包括天气、非法侵入事件、硬件故障和其他不可预见的情况）的级联影响，减轻管理问题。