- Title
- A multi-agent system with reinforcement learning for railway traffic management
- Creator
- Bretas, Allan M. C.; Mendes, Alexandre; Jackson, Martin; Clement, Riley; Sanhueza, Claudio; Chalup, Stephan
- Relation
- 13th International Conference on Bulk Materials Storage, Handling, and Transportation (ICBMH 2019). Proceedings of 13th International Conference on Bulk Materials Storage, Handling and Transportation (ICBMH 2019) (Surfers Paradise, QLD 09-11 July, 2019) p. 907-916
- Relation
- https://www.engineersaustralia.org.au
- Publisher
- Engineers Australia
- Resource Type
- conference paper
- Date
- 2019
- Description
- Australia is the world's largest coal exporter and in New South Wales, more than 87% of the coal is transported through railways. One of the strategies to increase throughput is to use complex computational techniques for train scheduling optimisation. This paper applies artificial intelligence techniques to the scheduling of coal trains in the context of the Hunter Valley Coal Chain Coordinator (HVCCC). We modelled a reduced version of the HVCCC network using a Multi-Agent System consisting of a centralized dispatcher agent and multiple train agents. The network has 9 single track sections, 6 passing loops, and 3 loading/dumping loops, in a closed tree design. Three methods are tested and analysed: (a) First-in-First-Out (FIFO), (b) Random Walk and (c) Reinforcement Learning (RL) by Q-learning algorithm. The dispatcher agent is responsible for traffic management of the trains and oversees the entire rail network. Our results show that the Q-learning algorithm outperforms the FIFO strategy in all test cases, between a minimum of 3.5% and a maximum of 15.2%. Different congestion scenarios were also compared and the RL technique presented the best values for the objective of minimizing the total dwell time of all trains in the network. Considering the results achieved by the Q-learning algorithm, this work presents a first step towards the application of intelligent agents for the traffic management of HVCCC's network.
- Subject
- coal; railways; transport; artificial intelligence; Hunter Valley Coal Chain Coordinator (HVCCC); Q-learning algorithm
- Identifier
- http://hdl.handle.net/1959.13/1446350
- Identifier
- uon:42841
- Identifier
- ISBN:9781925627299
- Language
- eng
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