Road Traffic Density Estimation in Vehicular Network

In recent decades, vehicular networks or intelligent transportation systems are being increasingly investigated and used to provide solutions to next generation traffic systems. Road traffic density estimation provides important information for road planning, intelligent road routing, road traffic control, vehicular network traffic scheduling, routing and dissemination. The ever increasing number of vehicles equipped with wireless communication capabilities provide new means to estimate the road traffic density more accurately and in real time than traditionally used techniques. In this thesis, we consider two research problems on road traffic density estimation. First research problem is the estimation algorithm design of road traffic density where each vehicle estimates its local road traffic density using some simple measurements only, i.e. the number of neighboring vehicles. A maximum likelihood estimator of the traffic density is obtained based on a rigorous analysis of the joint distribution of the number of vehicles in each hop. Analysis is also conducted on the accuracy of the estimation and the amount of neighborhood information required for an accurate road traffic density estimation. Simulations are performed which validate the accuracy and the robustness of the proposed density estimation algorithm. Secondly, we consider the problem of road traffic density estimation based on the use of a stochastic geometry concept—contact distribution function, which obtains density estimates by a probe vehicle traveling within objective area, measuring the inter-contact vehicle numbers and lengths. A maximum likelihood estimator of the traffic density is applied. Analysis is also performed on the accuracy of the estimation and the small sample sizes’ bias has been corrected. Simulations are performed which validate the accuracy and robustness of the proposed density estimation algorithm