Serious Conflicts: A Safety Performance Measure at Signalized Intersections

  • Raghad Zeki Abdul-MajeedEmail author
  • Hussein A. Ewadh
Conference paper
Part of the Lecture Notes in Civil Engineering book series (LNCE, volume 53)


There is a challenge to identify potential sites for safety improvement in case of shortage in crash data. This study explores alternative method based on traffic conflicts as a surrogate safety measure instead of crash data. The study demonstrates two family major safety assessment streams; three of crash-based methods proposed by Highway Safety Manual and two conflict-based methods. For crash-based methods, Empirical Bayes (EB-method), crash frequency and crash rate measures are used. Conflicts frequency and conflicts rate for two surrogate safety indicators are used in the conflict-based methods, in this study, EB-method is used as a benchmark for comparison. The safety evaluation was performed separately for 9 signalized intersections, the safety measures are estimated and compared through Pearson correlation analysis while hazard location identification results through the use of rank-based mean absolute. Results showed that the serious conflicts frequency as a conflict-based method had a high correlation and a coefficient of 0.986 with the EB-method in the resulting outcomes and performed better than crash frequency method in identifying hazard location when compared with EB-method. Therefore, the serious conflicts frequency can serve as a viable option for safety performance evaluation and hazard locations identification, especially when sufficient crash data are not obtainable.


Traffic conflicts Serious conflicts Hazard location Safety 


  1. 1.
    Lord D, Persaud BN (2004) Estimating the safety performance of urban road transportation networks. Accid Anal Prev 36(4):609–620CrossRefGoogle Scholar
  2. 2.
    Leur P, Sayed T (2002) Development of a road safety risk index. J Transp Res Board 1784(1):33–42. Scholar
  3. 3.
    AASHTO (2010) Highway safety manual, 1st edn. Washington, DCGoogle Scholar
  4. 4.
    Tarko A, Davis G, Saunier N, Sayed T, Washington S (2009) Surrogate measures of safety white paper. Subcommittee on Surrogate Measures of Safety and Committee on Safety Data Evaluation and AnalysisGoogle Scholar
  5. 5.
    Gettman D, Head L (2003) Surrogate safety measures from traffic simulation models. J Transp Res Board 1840:104–115CrossRefGoogle Scholar
  6. 6.
    Gettman D, Pu L, Sayed T, Shelby S (2008) Surrogate safety assessment model and validation: final report FHWA-HRT-08-051Google Scholar
  7. 7.
    Sayed T, Zaki MH, Autey J (2013) Automated safety diagnosis of vehicle-bicycle interactions using computer vision analysis. Saf Sci 59:163–172CrossRefGoogle Scholar
  8. 8.
    Zheng L, Ismail K, Meng X (2014) Traffic conflict techniques for road safety analysis: open questions and some insights. Can J Civ Eng 41(7):633–641CrossRefGoogle Scholar
  9. 9.
    Elvik R (1988) Some difficulties in defining populations of “entities” for estimating the expected number of accidents. Accid Anal Prev 20(4):261–275CrossRefGoogle Scholar
  10. 10.
    Elvik R, Mysen A (1999) Incomplete accident reporting: meta-analysis of studies made in 13 countries. J Transp Res Board 1665:133–140CrossRefGoogle Scholar
  11. 11.
    Hauer E, Hakkert AS (1988) Extent and some implications of incomplete accident reporting. Transp Res Rec 1185:1–10Google Scholar
  12. 12.
    Persaud B, Lyon C, Nguyen T (1999) Empirical Bayes procedure for ranking sites for safety investigation by potential for improvement. Transp Res Rec 1665:7–9. TRB, National Research Council, Washington, DCGoogle Scholar
  13. 13.
    Montella A (2010) A comparative analysis of hotspot identification methods. Accid Anal Prev 42(2):571–581CrossRefGoogle Scholar
  14. 14.
    Lim L, Kweon Y (2013) Identifying high-crash-risk intersections: comparison of traditional methods with the empirical Bayes-safety performance function method. Transp Res Board Nat Acad, Washington, D.C., pp 44–50Google Scholar
  15. 15.
    So J, Lim I, Kweon Y (2015) Exploring traffic conflict-based surrogate approach for safety assessment of highway facilities. Transportation Research Board, Washington, D.C., pp 56–62Google Scholar
  16. 16.
    FHWA (2013) Signalized intersections informational guide, 2nd edn. Publication no. FHWA-SA-13-027Google Scholar
  17. 17.
    Sayed T, Vahidi H, Rodriguez F (1999) Advance warning flashers: do they improve safety? Transp Res Rec 1692. TRB, NRC, Washington, DCGoogle Scholar
  18. 18.
    Maskooni E, Haghighi F (2018) Evaluation and statistical validation of black-spots identification methods. Int J Transp Eng 6(1):1–15CrossRefGoogle Scholar
  19. 19.
    Laureshyn A, Varhelyi A (2018) The Swedish traffic conflict technique-observer’s manual. Lund UniversityGoogle Scholar
  20. 20.
    Tageldin A, Sayed T (2016) Developing evasive action-based indicators for identifying pedestrian conflicts in less organized traffic environments. J Adv Transp 50:1193–1208. Scholar
  21. 21.
    Parker MR, Zegeer CV (1989) Traffic conflict techniques for safety and operation. Report no. FHWA-IP-88-027Google Scholar
  22. 22.
    Amundsen FN, Hydén C (1977) Proceedings of the first international traffic conflicts technique workshop. Institute of Transport Economics, OsloGoogle Scholar
  23. 23.
    Laureshyn A, Johnsson C, De Ceunynck T, Svensson A, de Goede M, Saunier N, Daniels S (2016) Review of current study methods for VRU safety. Report no. Deliverable 2.1—part 4Google Scholar
  24. 24.
    El-Basyouny K, Sayed T (2013) Safety performance functions using traffic conflicts. Saf Sci 51(1):160–164CrossRefGoogle Scholar
  25. 25.
    Williams M (1981) Validity of the traffic conflicts technique. Accid Anal Prev 13(2):133–145CrossRefGoogle Scholar
  26. 26.
    Johnsson C, Laureshyn A, De Ceunynck T (2018) In search of surrogate safety indicators for vulnerable road users: a review of surrogate safety indicators. Transp Rev 38:765–785CrossRefGoogle Scholar
  27. 27.
    Grayson GB, Hyden C, Kraay JH, Muhlrad N, Oppe S (1984) The Malmo study: a calibration of traffic conflict techniques. Report no. R-84-12, Institute for Road Safety Research, LeidschendamGoogle Scholar
  28. 28.
    Chin HC, Quek ST (1997) Measurement of traffic conflicts. Saf Sci 26(3):169–187CrossRefGoogle Scholar
  29. 29.
    Hauer E (1978) Traffic conflict surveys: some study design considerations. TRRL supp report 352, Transport and Road Research Laboratory. Berkshire, EnglandGoogle Scholar
  30. 30.
    Baker WT (1972) An evaluation of the traffic conflicts technique. Highway Res Rec 384:1–8Google Scholar
  31. 31.
    Sayed T, Zein S (1999) Traffic conflict standards for intersections’. Transp Plann Technol 22(4):309–323CrossRefGoogle Scholar
  32. 32.
    Hauer E, Garder P (1986) Research into the validity of the traffic conflict technique. Accid Anal Prev 18(6):471–481CrossRefGoogle Scholar
  33. 33.
    Pietrzyk M. (1996). Development of expected value conflict tables for florida-based traffic crashes. USDOT WPI No. 0510721, Washington, D.CGoogle Scholar
  34. 34.
    Songchitruksa P, Tarko A (2006) The extreme value theory approach to safety estimation. Accid Anal Prev 38:811–822. Scholar
  35. 35.
    Sacchi E, Sayed T, Leur P (2013) A comparison of collision-based and conflict-based safety evaluations: the case of right-turn smart channels. Accid Anal Prev 59:260–266. Scholar
  36. 36.
    Svensson A (1998) A method for analysing the traffic process in a safety perspective. Doctoral thesis, University of Lund, Lund Institute of TechnologyGoogle Scholar
  37. 37.
    Glauz W, Migletz D (1980) Application of traffic conflict analysis at intersections. NCHRP Report, Washington, DC, p 219Google Scholar
  38. 38.
    Dean S, Illowsky B (2009) Principles of business statistics. Rice University, Houston, TexasGoogle Scholar
  39. 39.
    Chee J (2013) Pearson’s product moment correlation: sample analysis. University of Hawaii at Manoa School of Nursing, Honolulu, United StatesGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Highway and Transportation Engineering Department, Faculty of EngineeringAl-Mustansiriayah UniversityBaghdadIraq
  2. 2.Road and Traffic Engineering Civil Engineering DepartmentsCollege of Engineering, University of BabylonBabylonIraq

Personalised recommendations