Distributed Approximate Dynamic Control for Traffic Management of Busy Railway Networks
Distributed Approximate Dynamic Control for Traffic Management of Busy Railway Networks
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DOI：
10.1109/tits.2019.2934083
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发表时间：
2019-08
影响因子：
8.5
通讯作者：
T. Ghasempour;G. Nicholson;D. Kirkwood;T. Fujiyama;B. Heydecker
中科院分区：
文献类型：
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作者：
T. Ghasempour;G. Nicholson;D. Kirkwood;T. Fujiyama;B. Heydecker
铁路运营容易受到干扰，这些干扰可以通过大型网络快速传播，导致延误和性能低下。自动重新安排工具已经显示出限制这种不良结果的潜力。本研究提出了一个自适应交通控制器的实时操作，是建立在近似动态规划（ADP）的本地部署的网络范围内的影响。控制器旨在通过有利地控制关键位置处的列车的排序来限制列车延迟。通过使用对动态规划的优化值函数的近似，并通过强化学习技术进行更新，ADP大大降低了计算负担。这个框架已经建立了孤立的本地控制，所以在这里，我们调查分布式部署的影响。我们的ADP控制器与微观铁路交通模拟器连接，以评估其对独立控制关键点的大型动态铁路系统的影响。与先来先服务控制相比，该方法实现了列车延误的减少。我们还发现，与我们控制区附近相比，终点站的改善程度更大。
Railway operations are prone to disturbances that can rapidly propagate through large networks, causing delays and poor performance. Automated re-scheduling tools have shown the potential to limit such undesirable outcomes. This study presents the network-wide effects of local deployment of an adaptive traffic controller for real-time operations that is built on approximate dynamic programming (ADP). The controller aims to limit train delays by advantageously controlling the sequencing of trains at critical locations. By using an approximation to the optimised value function of dynamic programming that is updated by reinforcement learning techniques, ADP reduces the computational burden substantially. This framework has been established for isolated local control, so here we investigate the effects of distributed deployment. Our ADP controller is interfaced with a microscopic railway traffic simulator to evaluate its effect on a large and dynamic railway system, which controls critical points independently. The proposed approach achieved a reduction in train delays by comparison with First-Come-First-Served control. We also found the improvements to be greater at terminal stations compared to the vicinity of our control areas.