Abstract
Railway scheduling is a complex and safety critical problem that has recently attracted attention in the formal verification community. We provide a formal model of railway scheduling as a stochastic timed game and using the tool Uppaal Stratego, we synthesise the most permissive control strategy for operating the lights and points at the railway scenario such that we guarantee system’s safety (avoidance of train collisions). Among all such safe strategies, we then select (with the help of reinforcement learning) a concrete strategy that minimizes the time needed to move all trains to their target locations. This optimizes the speed and capacity of a railway system and advances the current state-of-the-art where the optimality criteria were not considered yet. We successfully demonstrate our approach on the models of two Danish railway stations, and discuss the applicability and scalability of our approach.
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- 1.
The event that two or more trains reach the end of their sections simultaneously has measure zero and may be ignored.
- 2.
Our experiment files can be found online at http://people.cs.aau.dk/~florber/TrainGames/ExperimentFiles.rar.
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Acknowledgments
We would like to thank to Peter G. Jensen for his support with the experiments and advice on Uppaal Stratego. We also thank the anonymous reviewers for their detailed comments and in particular for pointing out a problem in our original formal model that could have made the constructed controller potentially unsafe. The research leading to these results has received funding from the project DiCyPS funded by the Innovation Fund Denmark, the Sino Danish Research Center IDEA4CPS and the ERC Advanced Grant LASSO. The fourth author is partially affiliated with FI MU, Brno, Czech Republic.
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Karra, S.L., Larsen, K.G., Lorber, F., Srba, J. (2019). Safe and Time-Optimal Control for Railway Games. In: Collart-Dutilleul, S., Lecomte, T., Romanovsky, A. (eds) Reliability, Safety, and Security of Railway Systems. Modelling, Analysis, Verification, and Certification. RSSRail 2019. Lecture Notes in Computer Science(), vol 11495. Springer, Cham. https://doi.org/10.1007/978-3-030-18744-6_7
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