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

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间表变化对网络状态的影响。借助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，直观地描绘铁路资产的位置。通过这个系统，铁路运营商可以查看计划和实际列车位置之间的差异，分析旅程中每个路段的时间损失或增加，并查看网络上每个车站的到达和出发时间。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，通过使分析师能够可视化和模拟服务以及意外延误场景（包括天气，侵入事件，硬件故障和其他不可预见的情况）的级联影响来缓解管理问题。

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.