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F. Gündling, P. Hoch, "Stochastic Passenger Flow Prediction in Large Train Networks", in J. Pombo, (Editor), "Proceedings of the Fifth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 1, Paper 23.18, 2022, doi:10.4203/ccc.1.23.18

Keywords: passenger flow, prediction, stochastic, train.

Abstract

We study the stochastic prediction of passenger flows in large train networks. Especially in scenarios that include disturbances and disruptions of train operations, passengers might end up in different trains than planned. We introduce a stochastic passenger behavior model that provides a probability for each real-time alternative in case of a broken connection. Each alternative is tracked from there on. This way, the model is able to predict a occupancy distribution for each train section. Our study on data provided by Deutsche Bahn Fernverkehr AG shows that our approach provides very high performance and is even applicable to real-time scenarios to support dispatchers by providing information about passenger flows in real-time.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 1 PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE Edited by: J. Pombo Paper 23.18 Stochastic Passenger Flow Prediction in Large Train Networks F. Gündling and P. Hoch Technical University of Darmstadt, Germany doi:10.4203/ccc.1.23.18 Full Bibliographic Reference for this paper F. Gündling, P. Hoch, "Stochastic Passenger Flow Prediction in Large Train Networks", in J. Pombo, (Editor), "Proceedings of the Fifth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 1, Paper 23.18, 2022, doi:10.4203/ccc.1.23.18 Keywords: passenger flow, prediction, stochastic, train. Abstract We study the stochastic prediction of passenger flows in large train networks. Especially in scenarios that include disturbances and disruptions of train operations, passengers might end up in different trains than planned. We introduce a stochastic passenger behavior model that provides a probability for each real-time alternative in case of a broken connection. Each alternative is tracked from there on. This way, the model is able to predict a occupancy distribution for each train section. Our study on data provided by Deutsche Bahn Fernverkehr AG shows that our approach provides very high performance and is even applicable to real-time scenarios to support dispatchers by providing information about passenger flows in real-time. download the full-text of this paper (PDF, 4 pages, 201 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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