用模仿学习模拟列车延误，提升预测准确率。
Simulation-Driven Railway Delay Prediction: An Imitation Learning Approach
- 将延误预测转为随机仿真任务，通过模仿学习建模状态转移。
- 在比利时300万趟列车数据上，30分钟内预测优于传统方法。
- 无需外部参考或对抗机制，能捕捉延误传播的不确定性。
可靠的列车延误预测对提升铁路运输系统的鲁棒性和效率至关重要。本文将延误预测重新定义为随机仿真任务，通过模仿学习建模状态转移动态。提出一种新型自监督算法Drift-Corrected Imitation Learning（DCIL），在DAgger基础上引入基于距离的漂移修正，从而在不依赖外部真值或对抗方案的情况下缓解滚动过程中的协变量偏移。该方法结合了事件驱动模型的动力学保真度与数据驱动方法的表征能力，通过蒙特卡洛仿真实现不确定性感知的预测。我们在比利时铁路基础设施管理机构Infrabel提供的真实世界数据集上评估了DCIL，该数据集涵盖超过三百万趟列车运行记录。实验聚焦于未来30分钟内的预测表现，结果显示DCIL在深度学习架构上的行为克隆和传统回归模型中均展现出更优性能，验证了其在大规模网络中捕捉延误传播序列性与不确定性的有效性。
原文摘要 · Abstract (English)
Reliable prediction of train delays is essential for enhancing the robustness and efficiency of railway transportation systems. In this work, we reframe delay forecasting as a stochastic simulation task, modeling state-transition dynamics through imitation learning. We introduce Drift-Corrected Imitation Learning (DCIL), a novel self-supervised algorithm that extends DAgger by incorporating distance-based drift correction, thereby mitigating covariate shift during rollouts without requiring access to an external oracle or adversarial schemes. Our approach synthesizes the dynamical fidelity of event-driven models with the representational capacity of data-driven methods, enabling uncertainty-aware forecasting via Monte Carlo simulation. We evaluate DCIL using a comprehensive real-world dataset from \textsc{Infrabel}, the Belgian railway infrastructure manager, which encompasses over three million train movements. Our results, focused on predictions up to 30 minutes ahead, demonstrate superior predictive performance of DCIL over traditional regression models and behavioral cloning on deep learning architectures, highlighting its effectiveness in capturing the sequential and uncertain nature of delay propagation in large-scale networks.
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