In railway networks, stations are probably the most critical points for interconnecting trains' routes: in a restricted geographical area, a potentially large number of trains have to stop according to an official timetable, with the concrete risk of accumulating delays that can then have a knockout effect on the rest of the network. In this context, in-station train dispatching plays a central role in maximising the effective utilisation of available railway infrastructures and in mitigating the impact of incidents and delays. Unfortunately, in-station train dispatching is still largely handled manually by human operators in charge of a group of stations. In this paper we make a step towards supporting the operator with some automatic tool, by describing an approach for performing in-station dispatching by means of automated planning techniques. Given the mixed discrete-continuous nature of the problem, we employ PDDL+ for the specification of the problem, and the ENHSP planning engine enhanced by domain-specific solving techniques. Results on a range of scenarios, using real-data of a station of the North West of Italy, show the potential of our approach.

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In-station train dispatching is the problem of optimising the effective utilisation of available railway infrastructures for mitigating incidents and delays. In this paper, we describe an approach for dealing with the in-station dispatching problem by means of automated planning techniques.

Public railway transport systems play a crucial role in servicing the global society and are the transport backbone of a sustainable economy. While a significant effort has been devoted to predict inter-station trains movements to support stakeholders (i.e., infrastructure managers, train operators, and travellers) decisions, the problem of predicting instation movements, while being crucial to improve train dispatching (i.e., empowering human or automatic dispatchers), has been far more less investigated. In fact, stations are the most critical points in a railway network: even small improvements in the estimation of the duration of trains movements can remarkably enhance the dispatching efficiency in coping with the increase in capacity demand and with delays. In this work we will first leverage on state of the art shallow models, fed by domain experts with domain specific features, to improve the current predictive systems. Then, we will leverage on a customised deep multi scale model able to automatically learn the representation and improve the accuracy of the shallow models. Results on real-world data coming from the Italian railway network will support our proposal. * This work has been partially funded by Hitachi Rail STS through the RAIDLab (Railway Artificial Intelligence and Data Analysis Laboratory), a joint laboratory between Hitachi Rail STS and University of Genoa.1 https://ec.europa.eu/transport/themes/infrastructure en 2 https://shift2rail.org/ 475