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arXiv · 2403.18561

A Dynamic Programming Approach for Road Traffic Estimation

Mattia Laurini·Irene Saccani·Stefano Ardizzoni·Luca Consolini·Marco Locatelli
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Abstract

We consider a road network represented by a directed graph. We assume to collect many measurements of traffic flows on all the network arcs, or on a subset of them. We assume that the users are divided into different groups. Each group follows a different path. The flows of all user groups are modeled as a set of independent Poisson processes. Our focus is estimating the paths followed by each user group, and the means of the associated Poisson processes. We present a possible solution based on a Dynamic Programming algorithm. The method relies on the knowledge of high order cumulants. We discuss the theoretical properties of the introduced method. Finally, we present some numerical tests on well-known benchmark networks, using synthetic data.

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Mattia Laurini, Irene Saccani, Stefano Ardizzoni, Luca Consolini, Marco Locatelli. 2024-03-27. A Dynamic Programming Approach for Road Traffic Estimation. https://doi.org/10.1109/cdc56724.2024.10886466

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