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Operational and Geometrical Conditions of Accident Occurrence and Severity at Signalized Intersections

  • Abdulla Alghafli
  • Mohamed ShawkyEmail author
Conference paper
  • 12 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1212)

Abstract

This research investigated the effect of road geometric features and operational conditions on the occurrence and severity of traffic accidents at signalized intersections in Abu Dhabi city, UAE. Speed, number of lanes, lane configuration, traffic signal sequence (lead/lag or split phasing), average hourly traffic volume per lane were used as independent variables. The accident occurrence was tested by using Poisson’s regression modeling and the accident severity was examined by using multinomial logit modeling approaches. The Poisson model showed that at 4-leg intersections, one of the major causes of the accident, is passing of a street (either minor or major street) through the intersection. It was also found that at 3-leg intersection, the main cause of the accident is minor street passing through the intersection. The research also found that the higher the traffic volume the higher the chance of occurrence of traffic accidents. The multinomial logit model showed that five significant variables affect the severity of traffic accidents occurs at signalized intersections. the significant variables are the speed of the main road, traffic signal sequences, number of through lanes of minor road number of left lanes of main and minor roads.

Keywords

Accident occurrence at intersections 3-leg intersection 4-leg intersection Traffic volume Road geometric feature 

References

  1. 1.
    Ouni, F., Belloumi, M.: Modeling traffic accident occurrence at hazardous road locations: a case study in Tunisia (2018)Google Scholar
  2. 2.
    Haleem, K., Abdel-Aty, M.: Examining traffic crash injury severity at unsignalized intersections. J. Saf. Res. 41(4), 347–357 (2010)CrossRefGoogle Scholar
  3. 3.
    Abdel-Aty, M.A., Radwan, A.E.: Prevention, modeling traffic accident occurrence and involvement. Accident Anal. 32(5), 633–642 (2000)CrossRefGoogle Scholar
  4. 4.
    Miaou, S.-P.: The relationship between truck accidents and geometric design of road sections: poisson versus negative binomial regressions. Oak Ridge National Lab, TN, United States (1993)Google Scholar
  5. 5.
    Cai, Q., Abdel-Aty, M., Lee, J., Eluru, N.: Comparative analysis of zonal systems for macro-level crash modeling. J. Saf. Res. 61, 157–166 (2017)CrossRefGoogle Scholar
  6. 6.
    Joshua, S.C., Garber, N.J.: Estimating truck accident rate and involvements using linear and Poisson regression models. Transp. Plann. Technol. 15(1), 41–58 (1990)CrossRefGoogle Scholar
  7. 7.
    Zewde, T.: Determinants that lead drivers into traffic accidents: a case of Arba Minch city, South Ethiopia. Sci. J. Appl. Math. Stat. 5(6), 205–210 (2017)CrossRefGoogle Scholar
  8. 8.
    Agent, K.R., Deen, R.C.: Relationships between roadway geometrics and accidents (1974)Google Scholar
  9. 9.
    Milton, J.C., Mannering, F.L.: The relationship between highway geometrics, traffic-related elements, and motor vehicle accidents (1996)Google Scholar
  10. 10.
    Xie, Y., Zhao, K., Huynh, N.: Analysis of driver injury severity in rural single-vehicle crashes. Accident Anal. Prevent. 47, 36–44 (2012)CrossRefGoogle Scholar
  11. 11.
    Ye, F., Lord, D.: Comparing three commonly used crash severity models on sample size requirements: multinomial logit, ordered probit and mixed logit models. Anal. Methods Accident Res. 1, 72–85 (2014)CrossRefGoogle Scholar
  12. 12.
    Dong, C., Richards, S.H., Huang, B., Jiang, X.: Identifying the factors contributing to the severity of truck-involved crashes. Int. J. Inj. Control Saf. Promot. 22(2), 116–126 (2015)CrossRefGoogle Scholar
  13. 13.
    Penmetsa, P., Pulugurtha, S.S.: Modeling crash injury by road feature to improve safety (2017)Google Scholar
  14. 14.
    Abdel-Aty, M.: Analysis of driver injury severity levels at multiple locations using ordered probit models. J. Saf. Res. 34(5), 597–603 (2003).  https://doi.org/10.1016/j.jsr.2003. PMID:14733994CrossRefGoogle Scholar
  15. 15.
    Shawky, M., Hasssan, H., Garib, A., Al-Harthei, H.: Examining the factors affecting the severity of run-off-road crashes in Abu Dhabi. Can. J. Civil Eng. 43, 132–138 (2016)CrossRefGoogle Scholar
  16. 16.
    Basu, S., Saha, P.: Regression models of highway traffic crashes: a review of recent research and future research needs. Procedia Eng. 187(Supplement C), 59–66 (2017)Google Scholar
  17. 17.
    Imran, M., Nasir, J.A.: Road traffic accidents. Prof. Med. J. 22(06), 705–709 (2015)Google Scholar
  18. 18.
    Razali, M.S.M.A., Zamzuri, Z.: Modeling motorcycle road accidents with traffic offenses at several potential locations using negative binomial regression model in Malaysia (2016)Google Scholar
  19. 19.
    Mohanty, M., Gupta, A.: Factors affecting road crash modeling. J. Transp. Lit. 9(2), 15–19 (2015)CrossRefGoogle Scholar
  20. 20.
    Yang, H., Ozbay, K., Ozturk, O., Xie, K.: Work zone safety analysis and modeling: a state-of-the-art review. Traffic Inj. Prev. 16(4), 387–396 (2015)CrossRefGoogle Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Unversiti TeknikalDurian TunggalMalaysia
  2. 2.Faculty of EngineeringAin Shams UniversityCairoEgypt

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