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Estimating the Origin-Destination Matrix using link count observations from Unmanned Aerial Vehicles

Efficient estimation of the origin-destination (OD) matrix is a crucial requirement for traffic monitoring and control. The OD matrix estimation problem has received significant attention over the past decades and various approaches using traffic counts from fixed location sensors have been developed and tested. In this work we present a novel methodology for static OD matrix estimation using traffic flow dynamics and link count observations collected from a swarm of Unmanned Aerial Vehicles (UAVs) deployed over the network under study. We assume networks that remain in the free-flow regime and formulate the problem in an optimisation framework for which we propose a solution approach when (i) fixed location sensor and (ii) UAV data are obtained. We compare estimation results using both types of data and show that estimating OD matrices using measurements collected from UAVs results in significantly better performance compared to measurements collected from fixed location sensors, even when the number of measurements per time-step with the UAV swarm is smaller.