01123166

Component

https://doi.org/10.3141/2102-04

http://scholar.google.com...ayed&publication_year=2009

This paper investigates the inclusion of spatial effects in accident prediction models. Two types of spatial modeling techniques—the Gaussian conditional autoregressive (CAR) and the multiple membership (MM) models—were compared with the traditional Poisson–lognormal model. A variation of the MM model (extended MM or EMM) was also investigated to study the effect of clustering segments within the same corridor on spatial correlation. Full Bayes estimation was used by means of the Markov chain Monte Carlo methodology to estimate the parameters. The study made use of 281 urban road segments in Vancouver, British Columbia, Canada. Various traffic and geometric variables were included in the accident prediction models. The models were compared in terms of their goodness of fit and inference. For the data set under consideration, the results showed that annual average daily traffic, business land use, the number of lanes between signals, and the density of unsignalized intersections have significant positive impact on the number of accidents. The fitted CAR and MM models had significant estimates for both heterogeneity and spatial correlation parameters. The best-fit model was EMM, followed by CAR. Furthermore, a significant portion of the total variability was explained by the spatial correlation. A significant correlation was also found between the heterogeneity and spatial effects. This may be because neighboring road segments typically have similar environmental and geographic characteristics and thereby form a cluster with similar accident occurrence. The results also showed that corridor variation was a major component of total variability and that the spatial effects have been considerably alleviated by clustering segments within the same corridor.

01143826

09-2385

English

pp 27-33

2009

Transportation Research Record: Journal of the Transportation Research Board

9780309142588

Print

References (47) ; Tables (3)

Annual average daily traffic; Arterial highways; Crash risk forecasting; Land use; Markov chains; Monte Carlo method; Poisson ratio; Unsignalized intersections

Clustering; Crash prediction models; Spatial effects

Vancouver (Canada)

Data and Information Technology; Highways; Safety and Human Factors; I80: Accident Studies

TRIS, TRB, ATRI

Jan 30 2009 6:44PM

• Analysis of Transit Safety at Signalized Intersections in Toronto, Ontario, Canada
• Differences in the Performance of Safety Performance Functions Estimated for Total Crash Count and for Crash Count by Crash Type
• Driver Injury Severity Resulting from Single-Vehicle Crashes Along Horizontal Curves on Rural Two-Lane Highways
• Driving Performance, Alignment Consistency, and Road Safety: Real-World Experiment
• Effects of Road Geometry and Cross-Section Variables on Traffic Accidents: Study Using Homogeneous Road Segments
• Effects of Street Patterns on Injury Risks in Two-Vehicle Crashes
• Exploring Impacts of Factors Contributing to Injury Severity at Freeway Diverge Areas
• How to Incorporate Accident Severity and Vehicle Occupancy into the Hot Spot Identification Process?
• Identifying Secondary Crashes and Their Contributing Factors
• Predicting Segment-Intersection Crashes with Land Development Data
• Probabilistic Determination of Crash Locations in a Road Network with Imperfect Data
• Safety Screening of Road Networks with Limited Exposure Data
• Statistical Versus Simulation Models in Safety: Steps Toward a Synthesis Using Median-Crossing Crashes
• Use of Expert Panels in Highway Safety: A Critique