Skip Top Navigation

Research Reports

Use of Automatic Vehicle Identification Techniques for Measuring Traffic Performance and Performing Incident Detection

Description:

Traffic performance information is an integral part of traffic control and motorist information systems. Good traffic performance information is also needed to optimize system control functions, detect congestion and incidents, and inform travelers to help them plan their trips. Yet, good traffic performance information is rarely available for these functions. One of several new technologies being investigated to improve the collection of traffic performance information is automatic vehicle identification (AVI)

The primary objectives of this project were to determine the possible benefits of using AVI systems for monitoring the performance of traffic and detecting incidents. A secondary objective was to determine whether the truck fleet tagged as part of the Heavy Vehicle Electronic License Plate (HELP) project, or even the entire truck population, would provide an unbiased measure of traffic performance.

The findings presented in this report show that AVI based systems can produce superior traffic performance data for use in both real-time control systems and more general transportation planning and engineering analyses. Furthermore, the mathematical algorithms needed to operate the AVI system are straightforward and easily programmed.

Continuing improvements in transponder, computing, and communications technologies provide the opportunity to reliably collect the information necessary to operate the planned intelligent vehicle-highway systems of the future. Given the current state of the technology and expected improvements, the impediments to using AVI technology in this manner are not technical, but fiscal and political.

  • Date Published: October, 1992
  • Publication Number: WA-RD 273.1
  • Last Modified: June 10, 2007
  • Authors: Mark E. Hallenbeck, Tim Boyle, Jennene Ring.
  • Originator: Washington State Transportation Center (TRAC)
  • # of Pages: 76 p., 2,681 KB (PDF)
  • Subject: Algorithms, Automatic vehicle detection and identification systems, Automatic vehicle identification, Automatic incident detection, Measurement, Performance, Traffic congestion, Traffic data, Traffic flow, Traffic incidents.
  • Keywords: AVI, Congestion Monitoring, Incident Detection, I-5, Tacoma.
  • Related Publications:


This abstract was last modified April 29, 2008