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Preprints, Working Papers, ... Year : 2016

Predicting traffic load in public transportation networks

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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Dates and versions

hal-01286476 , version 1 (10-03-2016)
hal-01286476 , version 2 (29-03-2016)

Identifiers

  • HAL Id : hal-01286476 , version 2

Cite

Stefan Haar, Simon Theissing. Predicting traffic load in public transportation networks. 2016. ⟨hal-01286476v2⟩
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