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Link Dependent Origin-Destination Matrix Estimation : Nonsmooth Convex Optimisation with Bluetooth-Inferred Trajectories

Abstract : Origin Destination matrix estimation is a critical problem of the Transportation field since the fifties. OD matrix is a two-entry table taking census of the zone-to-zone traffic of a geographic area. This traffic description tools is therefore paramount for traffic engineering applications. Traditionally, the OD matrix estimation has solely been based on traffic counts collected by networks of magnetic loops. This thesis takes place in a context with over 600 Bluetooth detectors installed in the City of Brisbane. These detectors permit in-car Bluetooth device detection and thus vehicle identification.This manuscript explores first, the potentialities of Bluetooth detectors for Transport Engineering applications by characterising the data, their noises and biases. This leads to propose a new methodology for Bluetooth equipped vehicle trajectory reconstruction. In a second step, based on the idea that probe trajectories will become more and more available by means of new technologies, this thesis proposes to extend the concept of OD matrix to the one of link dependent origin destination matrix that describes simultaneously both the traffic demand and the usage of the network. The problem of LOD matrix estimation is formulated as a minimisation problem based on probe trajectories and traffic counts and is then solved thanks to the latest advances in nonsmooth convex optimisation.This thesis demonstrates that, with few hypothesis, it is possible to retrieve the LOD matrix for the whole set of users in a road network. It is thus different from traditional OD matrix estimation approaches that relied on successive steps of modelling and of statistical inferences.
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Submitted on : Friday, December 16, 2016 - 1:06:50 AM
Last modification on : Friday, October 23, 2020 - 5:00:43 PM
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  • HAL Id : tel-01417805, version 1


Gabriel Michau. Link Dependent Origin-Destination Matrix Estimation : Nonsmooth Convex Optimisation with Bluetooth-Inferred Trajectories. Data Analysis, Statistics and Probability []. Université de Lyon; Queensland University of Technology. Brisbane, Australie, 2016. English. ⟨NNT : 2016LYSEN017⟩. ⟨tel-01417805⟩



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