Road traffic density estimation in vehicular networks

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 paper, we consider the problem of road traffic density estimation 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 performed 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.

I. Introduction

Vehicular networks (VANETs) or intelligent transport systems have been attracting increasing research interest. This is evidenced by recent developments from both automobile industry and the wireless communication community that make the vehicles more and more intelligent. Intelligent transport systems projects have been launched in the US, Japan and Europe. Several well-known projects include IntelliDrive(sm)/VII, V2V communication for safety, SAFESPOT and NoW [1]. VANETs or intelligent transport systems have been promising in improving the safety of drivers and passengers, improving the efficiency of road transportation and making the transportation experience more enjoyable by providing a number of services from collision warning, lane change assistance, speed limit notification, intelligent navigation and road traffic control, and multimedia content deliveries.