MODELLING VEHICLE ACCIDENTS AND EFFICIENCY OF HIGHWAY GEOMETRIC DESIGN

Osasu, Osamuyi and Ogbeifun, Nowamagbe Prince

Abstract

This study investigates the relationship and effect of geometric variables such as curve radius (Horizontal and vertical), road width, median width, shoulder width and super-elevation on the frequency of accident in highways. Accident predictions were done for the Benin-Ore road and Benin-Agbor road using field data such as numbers of crashes, length of the route and Average Annual Daily Traffic (AADT) values etc. Poisson and Negative Binomial Regressions techniques were adopted for accident prediction modelling and model validation was done using the Statistical Package for Social Sciences (SPSS) to determine the reliability of the parameters. Where the Poisson regression model failed because of over-dispersion and under-dispersion of data, the Negative Binomial (NB) proved very useful because of its ability to cater to such shortfall. Results from the analysis show that of the designated geometric (independent) variables selected, vertical curves and shoulder-width were the most significant in influencing the probability of crash occurrence in the designated highways of the study domain. For the Benin Ore road, the shoulder width and the road width accounted for the principal causes of accident with a probability of crash occurrence of 73.6% and 8.7% respectively. The vertical alignment with a probability of crash of 19.60% accounted for the principal cause of accident along the Benin Agbor expressway. Super elevation and the horizontal curve accounted for just 1% each of the probability of crash occurrence along the Benin Agbor road. It can, therefore, be concluded that some form of checks be carried out on the shoulder width and the road width along the Benin Ore road while similar checks must be focused on the vertical alignment along the Benin Agbor road to mitigate the occurrence of accidents arising from defective geometric configuration. Read full PDF

Keywords: Vehicle Accident, Speed, Horizontal Curve, Vertical Curve, Shoulder, Super-elevation, Poisson Regression, Negative Binomial

References

[1] Joshua, S. and Garber, N. (2013): Estimating Truck Accident Rate and Involvements Using Linear and Poisson Regression Models. Journal of Transportation Planning and Technology. 15(1): p. 41-58

[2] Global Status report on Road Safety (2018), Summary, Division, R., Editor, World Health Organization.

[3] Aram, A. (2010): Effective Safety Factors on Horizontal Curves of Two-Lane Highways. Journal of Applied Sciences. 10(22): p. 2814-2822

[4] Mohamadshah, Y.M., et al. (1993): Truck Accident Models for Interstate and Two-Lane Rural Roads. 1407. Transportation Research Record. 1407: p. 35-41

[5] FHWA (2010): Traffic Volume Trend 2009. Federal Highway Administration. 02

[6] Daniel, J., et al. (2002): Factors in Truck Crashes on Roadways with Intersections: Transportation Research Record. Journal of the Transportation Research Board. 1818(1): p. 54-59 Osasu, O. and Ogbeifun, N. P / Journal of Science and Technology Research 2(1) 2020 pp. 21-33 33

[7] Garnaik, M.M. (2014), Effects of Highway Geometric Elements on Accident Modelling, in Department of Civil Engineering, National Institute of Technology: Rourkela-Odisha-769008, India.

[8] Zhang, Y. (2009). Analysis of the Relation between Highway Horizontal Curve and Traffic Safety. in In Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation.

[9] Hassan, Y. and Easa, S.M. (2003): Effect of Vertical Alignment on Driver Perception of Horizontal Curves. Journal of transportation engineering. 129(4): p. 399-407

[10] Miaou, S.P. (1994): The Relationship between Truck Accidents and Geometric Design of Road Sections: Poisson Versus Negative Binomial Regressions. Journal of Accident Analysis and Prevention. 26(4): p. 471-482

[11] Lin, F.-B. (1990): Flattening of Horizontal Curves on Rural Two-Lane Highways. Journal of Transportation Engineering. 116(2)March 1990.

[12] Pedan, A. (2009): Analysis of Count Data Using the Sas System.” Sugi 26. National Highway Traffic Safety Administration (NHTSA). Traffic Safety Facts 2009.No. HS-8113888. 2009. p. 22-25

[13] Agresti, A. (2007), An Introduction to Categorical Data Analysis. Vol. 423 ed: Wiley-Interscience