Vehicular Network Based Reliable Traffic Density Estimation

Traffic density estimation with vehicular ad hoc networks (VANETs) can facilitate many applications. Traditional density estimation is achieved by counting the number of vehicles occupied in a certain area with inductive loop detectors and cameras, which is applied over limited coverage and brings high cost. In this paper, we propose to fuse vehicle spacing information and and compute average spacing in a specific area during a short period of time for density estimation. The robustness of the proposed scheme against Byzantine attack is then analyzed. Finally, we carry our experiments with U.S. Highway 101 data and the results show that the average spacing estimation is consistent with the real value.

I. Introduction

Vehicular ad hoc networks (VANETs) can be applied in reducing traffic accidents, improving traffic efficiency and management and many other fields [1]. VANETs are mainly composed of vehicle-to-vehicle (V2V) [2] network and the vehicle-to-infrastructure (V2I) [3] network, where the Road Side Unit (RSU) acts as an Internet access point to send data to the data center (DC) or cloud [4]. There are many research areas of VANETs, among all of which traffic density estimation is most widely studied [5]. Traffic density is essential and necessary to many applications like mobility-centric data dissemination [6] and other routing mechanisms [7]. Moreover, higher traffic density means higher connectivity, which facilitates the V2V communication. As a result, traffic density information is favorable to V2V related applications. In addition, a density distribution map can be used to estimate the pollution status in different places and optimize the deployment of VANETs infrastructure [8]. Therefore, it is rather meaningful to estimate traffic density.