Towards efficient traffic state estimation using sparse UAV-based data in urban networks

Traffic state estimation (TSE) is a challenging task due to the collection of sparse and noisy measurements from fixed points in the traffic network. Unmanned Aerial Vehicles (UAVs) have been gaining popularity as traffic sensors due to their ability to monitor a number of important traffic parameters over space and time. In this work, we develop a novel UAV-based sensing architecture which provides sparse, noisy measurements of traffic densities and transfer flows of the traffic network. Assuming free-flow conditions, we construct a Kalman filter approach that utilises knowledge of regional split ratios along with the UAV-based measurements. To avoid the assumption of known split ratios, we further develop a weighted least-squares optimization approach that minimizes measurement and process errors over a moving horizon window subject to linear traffic dynamics to accurately estimate traffic densities. We compare the UAV-based sensing architecture to an all-measurement method where we assume that measurements for all traffic densities and transfer flows are available at every time-step. Results show that the UAV-based sensing architecture compares favourably to the all-measurement scenario and the proposed optimization based estimator achieves similar results to the Kalman filter, even when regional split ratios are unknown.