'Institute of Electrical and Electronics Engineers (IEEE)'
Abstract
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
