Enhancing Traffic State Estimation Using UAV-Based Measurements

Traffic state estimation is a challenging task due to the collection of sparse and noisy measurements from specific points of the traffic network. The emergence of Unmanned Aerial Vehicles (UAVs) provides new capabilities for traffic state estimation using density measurements at irregular time-points from all links of a given network under study. This work proposes a data-driven traffic density estimation method utilising measurements collected from a swarm of UAVs deployed over the network under study and no traffic models or historical data are required. A simulation study is conducted to compare the quality of information obtained from UAV-based measurements, to the information provided by other sensing technologies, particularly fixed-location sensors and Connected and Automated Vehicles (CAVs). Notably, while CAV-based and UAV-based sensing provide information with higher spatiotemporal resolutions compared to fixed-location sensors, UAV-based sensing exhibits higher estimation accuracy even under low penetration rates of UAVs flying above the network and low percentages of network coverage.