Traffic congestion parameter estimation in time series of airborne optical remote sensing images

Abstract

In this paper we propose a new model based traffic parameter estimation approach in congested situations in time series of airborne optical remote sensing data. The proposed approach is based on the combination of various techniques: change detection, image processing and incorporation of a priori information such as road network, information about vehicles and roads and finally a traffic model. The change detection in two images with a short time lag of several seconds is implemented using the multivariate alteration detection method resulting in a change image where the moving vehicles on the roads are highlighted. Further, image processing techniques are applied to derive the vehicle density in the binarized change image. Finally, this estimated vehicle density is related to the vehicle density, acquired by modelling the traffic flow for a road segment. The model is derived from a priori information about the vehicle sizes and road parameters, the road network and the spacing between the vehicles. Then, the modelled vehicle density is directly related to the average vehicle velocity on the road segment and thus the information about the traffic situation can be derived. To confirm our idea and to validate the method several flight campaigns with the DLR airborne experimental wide angle optical 3K digital camera system operated on a Do-228 aircraft were performed. Experiments are performed to analyse the performance of the proposed traffic parameter estimation method for highways and main streets in the cities. The estimated velocity profiles coincide qualitatively and quantitatively quite well with the reference measurements.