Traffic state estimation using small imaging satellites and connected vehicles

Abstract

Abstract Small imaging satellites (SISs), which has attracted attention in remote sensing field recently, would provide a novel type of data for traffic state estimation (TSE): that is, spatial distribution of every cars in anywhere on our planet with relatively short time interval. This nature is particularly useful to complement connected vehicle (CV) data, which is sampled but time-continuous data. This paper proposes an ensemble Kalman filter-based TSE method using SIS and CV data. The proposed method endogenously estimates the fundamental diagram of traffic flow and penetration rate of CVs based only on SIS and CV data, making the method completely free from roadside detectors. The accuracy of the proposed method is verified by numerical simulation.