The railway sector is currently experiencing a rapid evolution from fully manual towards automatic rail traffic control systems, due to the growth of transport demand and networks complexity. One of the main issues is to automatically and effectively reschedule the railway traffic in case of unexpected events, thus avoiding dramatic drops in the system performance. In the literature, the majority of contributions aims at automatically minimizing the train delays or optimizing the railway system performance (e.g., energy consumption). However, such strategies are not always able to ensure satisfaction of passengers that in many cases experience the side-effects of the rescheduling actions (e.g., cancellation of train runs, cancellation of coincidences, rerouting of trains, etc.). In this paper, we propose a demand-oriented train rescheduling automatic technique that minimizes simultaneously the train delays and the discomfort perceived by passengers. When an unexpected event occurs, the rescheduling problem is set, based on the current state and nominal timetable of the system and its passengers flows. Hence, the problem is solved providing the control actions necessary to minimize both the delays and number of passengers subject to severe side-effects. The rescheduling is here formulated as a mixed integer linear programming problem, where the operating rules of the railway network are represented by linear equality and inequality constraints, while the objective is a linear function to be minimized. The possible control actions consist in re-timing the rail traffic and modifying the connections among lines. The proposed technique is preliminarily evaluated on a test case and a discussion is provided on the outcomes.

Cavone, G., Montaruli, V., van den Boom, T.J.J., Dotoli, M. (2020). Demand-Oriented Rescheduling of Railway Traffic in Case of Delays. In 2020 7th International Conference on Control, Decision and Information Technologies (CoDIT) (pp.1040-1045). 345 E 47TH ST, NEW YORK, NY 10017 USA : IEEE.

Demand-Oriented Rescheduling of Railway Traffic in Case of Delays

Cavone, Graziana
;
2020

Abstract

The railway sector is currently experiencing a rapid evolution from fully manual towards automatic rail traffic control systems, due to the growth of transport demand and networks complexity. One of the main issues is to automatically and effectively reschedule the railway traffic in case of unexpected events, thus avoiding dramatic drops in the system performance. In the literature, the majority of contributions aims at automatically minimizing the train delays or optimizing the railway system performance (e.g., energy consumption). However, such strategies are not always able to ensure satisfaction of passengers that in many cases experience the side-effects of the rescheduling actions (e.g., cancellation of train runs, cancellation of coincidences, rerouting of trains, etc.). In this paper, we propose a demand-oriented train rescheduling automatic technique that minimizes simultaneously the train delays and the discomfort perceived by passengers. When an unexpected event occurs, the rescheduling problem is set, based on the current state and nominal timetable of the system and its passengers flows. Hence, the problem is solved providing the control actions necessary to minimize both the delays and number of passengers subject to severe side-effects. The rescheduling is here formulated as a mixed integer linear programming problem, where the operating rules of the railway network are represented by linear equality and inequality constraints, while the objective is a linear function to be minimized. The possible control actions consist in re-timing the rail traffic and modifying the connections among lines. The proposed technique is preliminarily evaluated on a test case and a discussion is provided on the outcomes.
Cavone, G., Montaruli, V., van den Boom, T.J.J., Dotoli, M. (2020). Demand-Oriented Rescheduling of Railway Traffic in Case of Delays. In 2020 7th International Conference on Control, Decision and Information Technologies (CoDIT) (pp.1040-1045). 345 E 47TH ST, NEW YORK, NY 10017 USA : IEEE.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11590/415990
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