Development of a Prototype Vehicle-Infrastructure Integration System for Real-Time Traffic Management

The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. This document is disseminated under the sponsorship of Freeway congestion is a major problem in many urban areas. It has been estimated that freeway incidents (events that impede the flow of traffic: accidents, disabled vehicles, etc.) account for one-half to three-fourths of the total congestion on metropolitan freeways in the United States. Incident detection is the first and most important stage of incident management. The earlier an incident is detected, the sooner the incident is cleared, and the less delay other drivers experience. Highway traffic surveillance systems are widely used for incident management, real-time traffic management, traveler information, and hazard evacuation. Of these surveillance methods, some of the most widely used are closed circuit television (CCTV) systems, driver reports processing, highway crew patrols, and automatic incident detection (AID) systems. However, CCTV systems and sensor networks for AID require extensive infrastructure support, such as wide bandwidth communication and intensive computation resources. Although many studies argue that driver-based incident detection systems (e.g. enhanced 911 services) can provide quick and accurate detection with less capital, maintenance, and operational costs, these systems do not perform well in areas with low cell phone usage or bad signals. There is also a risk of the phone call processing system becoming jammed during a severe incident. The labor intensive nature of highway crew patrols also tends to limit their wide spread deployment. Consequently, existing highway traffic surveillance is limited to major highways and urban areas. In order to support the expansion of traffic surveillance systems and improve the performance of existing traffic-sensor-based systems, Morgan State University and Clemson University developed a prototype for a new traffic condition assessment and prediction system. This proposed framework — a vehicle-infrastructure integration (VII) system — assesses and predicts traffic conditions via wireless communication between roadside sensors and VII-equipped vehicles that have on-board processors, communication interfaces, and global positioning systems (GPS). Data gathered from vehicles are utilized to predict travel time, detect incidents, and determine the location of incidents and the likely number of lanes blocked. Equipping vehicles and roadside infrastructures with wireless communication interfaces makes it possible to provide the traffic surveillance system with changing data on speed, acceleration/deceleration, position, and maneuvers. The expected substantial improvement in the quality and availability of this information would in …