Citation

Florin R, Olariu S. Transp. Res. B Methodol. 2020; 138: 435-456.

Copyright

(Copyright © 2020, Elsevier Publishing)

DOI

10.1016/j.trb.2020.06.001

PMID

unavailable

Abstract

Traffic state estimation is a fundamental task of Intelligent Transportation Systems (ITS). Recent advances in sensor technology and emerging computer and vehicular communications paradigms have brought the task of estimating traffic state parameters in real time within reach. Recognizing this, the US-DOT started promoting the Connected Vehicles (CV) initiative. By using wireless connectivity between the vehicles participating in the traffic, the CV initiative aims to promote an increased awareness of real-time traffic conditions and, as a result, to reduce the number and severity of crashes. A number of recent papers have proposed CV-based approaches to estimating traffic state parameters including density and flow. However, virtually all the CV-based approaches for density estimation also rely on communication with stationary detectors and other pre-deployed roadside infrastructure. This assumption is problematic since such infrastructure is often not available. The main contribution of this paper is to propose a simple and easy to implement real-time traffic density estimation method that uses only vehicle-to-vehicle communications and the on-board sensing capabilities of present-day vehicles. In our method, using their on-board devices, vehicles maintain a tally that keeps track of the difference between the number of times other vehicles pass them and the number of times they pass other vehicles. Notice that since vehicles may vary their speed as they please, they may pass and be passed by the same vehicle multiple times and, consequently, maintaining a correct tally is a non-trivial task. We show that the tallies computed by vehicles relate, in an interesting way, to traffic density. We provide a detailed proof of our method using techniques that avoid the use of common simplifications inherent to visual time-space traffic diagrams. Furthermore, we demonstrate the accuracy of our method through extensive simulations using real NGSIM traffic traces along with SUMO-generated synthetic traffic traces.

Language: en

Keywords

Connected vehicles; Intelligent vehicle; Mobile observers; Real-time traffic state estimation; Traffic density