Citation

van Petegem JW, Wegman FCM. J. Saf. Res. 2014; 49: 121.e1-1127.

Affiliation

Delft University of Technology, Civil Engineering and Geosciences, The Netherlands.

Copyright

(Copyright © 2014, U.S. National Safety Council, Publisher Elsevier Publishing)

DOI

10.1016/j.jsr.2014.03.003

PMID

24913476

Abstract

PROBLEM: About 50% of all road traffic fatalities and 30% of all traffic injuries in the Netherlands take place on rural roads with a speed limit of 80km/h. About 50% of these crashes are run-off-road (ROR) crashes. To reduce the number of crashes on this road type, attention should be put on improving the safety of the infrastructure of this road type. With the development of a crash prediction model for ROR crashes on rural roads with a speed limit of 80km/h, this study aims at making a start in providing the necessary new tools for a proactive road safety policy to road administrators in the Netherlands.

METHOD: The paper presents a basic framework of the model development, comprising a problem description, the data used, and the method for developing the model. The model is developed with the utilization of generalized linear modeling in SAS, using the Negative Binomial probability distribution. A stepwise approach is used by adding one variable at a time, which forms the basis for striving for a parsimonious model and the evaluation of the model. The likelihood ratio test and the Akaike information criterion are used to assess the model fit, and parameter estimations are compared with literature findings to check for consistency.

RESULTS: The results comprise two important outcomes. One is a crash prediction model (CPM) to estimate the relative safety of rural roads with a speed limit of 80km/h in a network. The other is a small set of estimated effects of traffic volume and road characteristics on ROR crash frequencies. PRACTICAL APPLICATIONS: The results may lead to adjustments of the road design guidelines in the Netherlands and to further research on the quantification of risk factors with crash prediction models.

Language: en