Predicting Traffic Load in Public Transportation Networks

Stefan Haar 1 Simon Theissing 2
2 MEXICO - Modeling and Exploitation of Interaction and Concurrency
LSV - Laboratoire Spécification et Vérification [Cachan], ENS Cachan - École normale supérieure - Cachan, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8643
Abstract : This work is part of an ongoing effort to understand the dynamics of passenger loads in modern, multimodal transportation networks (TNs) and to mitigate the impact of perturbations, under the restrictions that the precise number of passengers in some point of the TN that intend to reach a certain destination (i.e. their distribution over different trip profiles) is unknown. We introduce an approach based on a stochastic hybrid automaton model for a TN that allows to compute how such probabilistic load vectors are propagated through the TN, and develop a computation strategy for forecasting the network's load a certain time in the future.
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Communication dans un congrès
2016 American Control Conference, Jul 2016, Boston, United States
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Dernière modification le : jeudi 11 janvier 2018 - 06:23:37

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Stefan Haar, Simon Theissing. Predicting Traffic Load in Public Transportation Networks. 2016 American Control Conference, Jul 2016, Boston, United States. 〈hal-01329632〉

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