Modeling crash-flow-density and crash-flow-V/C ratio relationships for rural and urban freeway segments.
There has been considerable research conducted in recent years into establishing relationships between crashes and various traffic flow characteristics for freeway segments. Most of the research has focused on determining the relationship between crashes and highway traffic volumes, while little attention has been focused on the relationships of vehicle density, level of service (LOS), vehicle occupancy, V/C ratio and speed distribution. Despite overall progress, there is still no clear understanding about the effects of different traffic flow characteristics on safety. In fact, several studies reviewed in this work were found to have methodological limitations. These include using predictive models with a normal error structure, aggregated crash rates, and inadequate functional forms for the data at hand. The original research on which this paper is based is aimed to determine the statistical relationship using commonly applied predictive models (i.e., functional forms) between crashes and hourly traffic flow characteristics, such as traffic volume, vehicle density and V/C ratios, for rural and urban freeway segments respectively. To accomplish this objective, predictive models have been developed from data collected on freeway segments located in downtown and outside of Montreal, Quebec. Three different functional forms are evaluated. The results show that predictive models that use traffic volume as the only explanatory variable may not adequately characterize the accident process on freeway segments. Functional forms that incorporate density and V/C ratio offer a richer description of crashes occurring on these facilities, whether they are located in a rural or urban environment. Finally, separate predictive models for single- and multi-vehicle crashes should be developed rather than one common model for all crash types.
Lord
Dominique
D
Department of Civil Engineering, Center for Transportation Safety, Texas Transportation Institute, Texas A and M University System, 3135 TAMU, College Station, TX, 77843-3135, USA. d-lord@tamu.edu
Manar
Abdelaziz
A
Vizioli
Anna
A
D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D001334:Automobile Driving / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D012424:Rural Population; D014505:Urban Population