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Differences in causality factors for single and multi-vehicle crashes on two-lane roads
J N Ivan1, R K Pasupathy, P J Ossenbruggen
1Department Civil and Environmental Engineering U-37, University of Connecticut, Connecticut Transportation Institute, Storrs 06269-2037, USA. johnivan@eng2.uconn.edu
Accident; Analysis and Prevention
|September 16, 1999
Summary
Highway crash rates are non-linearly related to traffic intensity. Site characteristics influence single-vehicle and multi-vehicle crashes differently on rural two-lane roads.
Area of Science:
- Transportation Engineering
- Traffic Safety Research
- Roadway Design
Background:
- Previous studies indicate a non-linear relationship between traffic intensity (or Level of Service - LOS) and highway crash rates.
- Understanding factors influencing crash rates is crucial for improving road safety.
Purpose of the Study:
- To investigate the relationship between traffic intensity, site characteristics, and highway crash rates.
- To develop separate Poisson regression models for predicting single-vehicle and multi-vehicle crashes on rural two-lane highways.
Main Methods:
- Analysis of rural two-lane highways using hourly Level of Service (LOS), traffic composition, and highway geometric characteristics as independent variables.
- Estimation of separate Poisson regression models for single-vehicle and multi-vehicle crashes.
- Inclusion of site-specific characteristics in the crash prediction models.
Main Results:
- Single-vehicle crash rates decrease with higher traffic intensity (lower LOS), wider shoulders, and increased sight distance.
- Multi-vehicle crash rates increase with more signals, higher percentages of single-unit trucks, and wider shoulders.
- Multi-vehicle crash rates were lower on principal arterials compared to other roadway classes.
- Level of Service (LOS) was not a significant predictor for multi-vehicle crash rates.
Conclusions:
- The factors influencing single-vehicle and multi-vehicle crashes differ significantly.
- Roadway geometry and traffic composition are key predictors, but further research is needed to incorporate factors like driveway density and intersection LOS for improved crash prediction.