Characterization of Accident Capacity Reduction

ABSTRACT Incidents are a major cause of urban highway congestion. Incidents include any event that temporarily reduces roadway capacity, such as accidents, debris, disabled vehicles, and hazardous material spills. Incident capacity reduction will be used in the incident management systems, advanced traveler information systems, queuing analysis, and computer simulation models. A study conducted in 1970 estimated that an accident or disabled vehicle blocking one lane out of three lanes will reduce traffic flow by an average of 50 percent, an accident blocking two lanes out of three lanes will reduce traffic flow by an average of 79 percent, and an accident or disabled vehicle blocking shoulder lane(s) out of three lanes will reduce traffic flow by an average of 33 percent. However, very little other research has comprehensively addressed the impact of incidents on capacity. The premise of this project is that the incident capacity reduction is best modeled as a random variable, not a deterministic value, as is the current practice. Extensive traffic flow and incident data for the Hampton Roads region of Virginia in the Smart Travel Laboratory provides us the opportunity to model incident capacity reduction as a random variable. Capacity under prevailing conditions can be estimated by calibrating a speed-flow and /or density-flow curve for a given highway. The peak of this curve defines capacity. When an incident occurs and a bottleneck is formed, the reduced capacity of the roadway is reached and can be measured directly as incident capacity. Incident capacity reduction can be computed as the difference between these two values over the capacity under prevailing conditions, and then modeled as a random variable. This research focuses on estimating accident capacity reductions with one lane and two lanes out of three lanes blocked, and modeling them as random variables based on the traffic flow and accident data for the Hampton Roads region. The results indicate that accident capacity reduction with one lane out of three lanes blocked can be modeled as Beta distribution with an average of 63 percent, which is fairly higher than the result of previous research (50 percent), and accident capacity reduction with two lanes out of three lanes blocked can be modeled as Beta distribution with an average of 77 percent which is slightly lower than the result of previous research (79 percent).Incidents are a major cause of urban highway congestion. Incidents include any event that temporarily reduces roadway capacity, such as accidents, debris, disabled vehicles, and hazardous material spills. Incident capacity reduction will be used in the incident management systems, advanced traveler information systems, queuing analysis, and computer simulation models. A study conducted in 1970 estimated that an accident or disabled vehicle blocking one lane out of three lanes will reduce traffic flow by an average of 50 percent, an accident blocking two lanes out of three lanes will reduce traffic flow by an average of 79 percent, and an accident or disabled vehicle blocking shoulder lane(s) out of three lanes will reduce traffic flow by an average of 33 percent. However, very little other research has comprehensively addressed the impact of incidents on capacity. The premise of this project is that the incident capacity reduction is best modeled as a random variable, not a deterministic value, as is the current practice. Extensive traffic flow and incident data for the Hampton Roads region of Virginia in the Smart Travel Laboratory provides us the opportunity to model incident capacity reduction as a random variable. Capacity under prevailing conditions can be estimated by calibrating a speed-flow and /or density-flow curve for a given highway. The peak of this curve defines capacity. When an incident occurs and a bottleneck is formed, the reduced capacity of the roadway is reached and can be measured directly as incident capacity. Incident capacity reduction can be computed as the difference between these two values over the capacity under prevailing conditions, and then modeled as a random variable. This research focuses on estimating accident capacity reductions with one lane and two lanes out of three lanes blocked, and modeling them as random variables based on the traffic flow and accident data for the Hampton Roads region. The results indicate that accident capacity reduction with one lane out of three lanes blocked can be modeled as Beta distribution with an average of 63 percent, which is fairly higher than the result of previous research (50 percent), and accident capacity reduction with two lanes out of three lanes blocked can be modeled as Beta distribution with an average of 77 percent which is slightly lower than the result of previous research (79 percent). CTS Website Center for Transportation Studies http://cts.virginia.edu University of Virginia 351 McCormick Road, P.O. Box 400742 Charlottesville, VA 22904-4742 434.924.6362 Characterization of accident Capacity Reduction 3 TABLE OF CONTENTS ABSTRACT....................................................................................................................... TABLE OF CONTENTS ................................................................................................. LIST OF FIGURES .......................................................................................................... LIST OF TABLES ............................................................................................................ GLOSSARY....................................................................................................................... ACKNOWLEDGEMENTS ............................................................................................. CHAPTER 1: INTRODUCITON.....................................................................................1 1.1 Motivation......................................................................................................................1 1.1.1 Overview........................................................................................................1 1.1.2 Importance for Queuing Analysis..................................................................2 1.1.3 Importance for Computer Simulation Models ...............................................5 1.2 Research Objective, Tasks and Scope ...........................................................................7 1.3 Organization of the Thesis .............................................................................................9 1.4 Summary......................................................................................................................10 CHAPTER 2: LITERATURE REVIEW.......................................................................11 2.1 Incident Management Systems ....................................................................................11 2.2 Previous Work on This Topic ......................................................................................12 2.3 Related