Probabilistic traffic density estimation using measurements from Unmanned Aerial Vehicles

Traffic state estimation (TSE) is an important task for traffic management, however it can be challenging due to the sparse deployment of traffic sensors in the network. The advancement of new technologies, such as the Unmanned Aerial Vehicles (UAVs), provide new capabilities for traffic state estimation using measurements at irregular time-points from all links of a given network under study. This work proposes a probabilistic 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. We propose the use of the Gaussian process model to interpolate measurements obtained from a swarm of UAVs and derive fine-grained traffic density estimations of distinct road segments in an offline Bayesian framework both under free-flow and congested conditions. The proposed approach is validated using a macroscopic simulation scenario of a part of the M25 highway stretch in London, England. Preliminary results show the effectiveness of UAVs in traffic density estimation and the efficiency of the proposed probabilistic method.