Vehicle Tracking and Speed Estimation from Traffic Videos

The rapid recent advancements in the computation ability of everyday computers have made it possible to widely apply deep learning methods to the analysis of traffic surveillance videos. Traffic flow prediction, anomaly detection, vehicle re-identification, and vehicle tracking are basic components in traffic analysis. Among these applications, traffic flow prediction, or vehicle speed estimation, is one of the most important research topics of recent years. Good solutions to this problem could prevent traffic collisions and help improve road planning by better estimating transit demand. In the 2018 NVIDIA AI City Challenge, we combine modern deep learning models with classic computer vision approaches to propose an efficient way to predict vehicle speed. In this paper, we introduce some state-of-the-art approaches in vehicle speed estimation, vehicle detection, and object tracking, as well as our solution for Track 1 of the Challenge

Estimation of road traffic induced environmental pollutants based on a point-to-point traffic detection system

This paper aims at the estimation of road traffic induced environmental pollutants for the city of Thessaloniki, based on travel time detections of a point-to-point detection system. The hourly and daily pollutant emissions (NOx, CO, HC) and fuel consumption (FC) were estimated based on the COPERT model and include hot emissions of passengers’ cars circulating in high hierarchy links of the transport network. The system detections (travel time) were correlated based on each path’s length, in order to determine the average vehicle speed per analyzed time interval, which was the main determinant for calculating traffic induced emissions. The paper concludes with a sensitivity analysis based on link capacity and the prevailing traffic flow characteristics for optimally determining the vehicle speed and flow that minimize environmental pollutants