Ghasempour, T;
Nicholson, GL;
Kirkwood, D;
Fujiyama, T;
Heydecker, B;
(2019)
Distributed Approximate Dynamic Control for Traffic Management of Busy Railway Networks.
IEEE Transactions on Intelligent Transportation Systems
pp. 1-11.
10.1109/tits.2019.2934083.
(In press).
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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.
| Type: | Article |
|---|---|
| Title: | Distributed Approximate Dynamic Control for Traffic Management of Busy Railway Networks |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/tits.2019.2934083 |
| Publisher version: | https://doi.org/10.1109/TITS.2019.2934083 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Approximate dynamic programming, railway traffic management, adaptive control, reinforcement learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10080161 |
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