Arabian Journal for Science and Engineering

, Volume 44, Issue 5, pp 4225–4231 | Cite as

A Case Study for Surrogate Safety Assessment Model in Predicting Real-Life Conflicts

  • Mohammad S. GhanimEmail author
  • Khaled Shaaban
Research Article - Civil Engineering


Conflict techniques enable transportation engineers to investigate hazardous network locations without the need to obtain crash data. These techniques are the most developed indirect measure of traffic safety. The concept of these techniques is based upon the ability to identify the occurrence of near accidents and therefore, offers a faster and, in many respects, a more representative way of estimating expected accident frequency and accident outcomes. One way to study conflicts is the use of microscopic models. Surrogate Safety Assessment Model (SSAM), a model developed by Federal Highway Administration, utilizes simulated vehicle trajectories to investigate conflict types, severity, and locations. This study investigates the feasibility of using SSAM to identify and classify traffic conflicts between vehicles and pedestrians by analyzing simulated trajectories. A case study of a major signalized intersection in the city of Doha was used. The traffic network was modeled using VISSM. Simulated vehicular trajectories were generated and analyzed using SSAM. The results were then compared with the real-life conflicts extracted from the video data collected at the same intersection based on the time-to-collision criteria. Although the results indicate many similarities between the observed and simulated conflicts, the simulation results were found to overestimate the collision risks, especially in the case of pedestrians. The results also indicate that the simulation approach is capable of identifying conflicts related to special maneuvers such as merging and diverging at intersections.


Traffic safety Surrogate Safety Assessment Model Traffic simulation Traffic conflict analysis Traffic operations Signalized intersections 


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  1. 1.
    Pu, L.; Joshi, R.: Surrogate safety assessment model (SSAM): software user manual. In: Federal Highway Administration Report FHWA-HRT-08-050, (2008)Google Scholar
  2. 2.
    Vasconcelos, L.; Neto, L.; Seco, Á.; Silva, A.: Validation of the surrogate safety assessment model for assessment of intersection safety. Transp. Res. Rec. J. Transp. Res. Board 2432, 1–9 (2014)CrossRefGoogle Scholar
  3. 3.
    Ariza, A.: Validation of road safety surrogate measures as a predictor of crash frequency rates on a large-scale microsimulation network. (2011)Google Scholar
  4. 4.
    Roach, D.; Christofa, E.; Knodler, M.A.: Evaluating the applicability of SSAM for modeling the safety of roundabouts. In: Transportation Research Board 94th Annual Meeting 2015, vol. 15-5207, (2015)Google Scholar
  5. 5.
    Stevanovic, A.; Stevanovic, J.; Jolovic, D.; Nallamothu, V.: Retiming traffic signals to minimize surrogate safety measures on signalized road networks. In: 91st Annual Meeting of the Transportation Research Board, Washington, DC (2012)Google Scholar
  6. 6.
    Zhou, H.; Huang, F.: Development of traffic safety evaluation method based on simulated conflicts at signalized intersections. Proc. Soc. Behav. Sci. 96, 881–885 (2013)CrossRefGoogle Scholar
  7. 7.
    El-Basyouny, K.; Sayed, T.: Safety performance functions using traffic conflicts. Saf. Sci. 51(1), 160–164 (2013)CrossRefGoogle Scholar
  8. 8.
    Wu, J.; Radwan, E.; Abou-Senna, H.: Determine if VISSM and SSAM could estimate pedestrian-vehicle conflicts at signalized intersections. J. Transp. Saf. Secur. (2017) (in press) Google Scholar
  9. 9.
    Planung, P.T.V.: PTV VISSM 7 users manual. PTV Planung Transport Verkehr AG, Karlsruhe, Germany (2015)Google Scholar
  10. 10.
    Ratrout, N.; Rahman, S.; Reza, I.: Calibration of PARAMICS model: application of artificial intelligence-based approach. Arab. J. Sci. Eng. 40(12), 3459–3468 (2015)CrossRefGoogle Scholar
  11. 11.
    Antoniou, C.; Barcelò, J.; Brackstone, M.; Celikoglu, H.; Ciuffo, B.; Punzo, V.; Sykes, P.; Toledo, T.; Vortisch, P.; Wagner, P.: Traffic simulation: case for guidelines. (2014)Google Scholar
  12. 12.
    Aksoy, G.; Celikoglu, H.B.; Gedizlioglu, E.: Analysis of toll queues by micro-simulation: results from a case study in Istanbul. Proc. Soc. Behav. Sci. 111, 614–623 (2014)CrossRefGoogle Scholar
  13. 13.
    Department of land transportation: guidelines and procedures for transport studies. In: Ministry of Transportation and Communication in State of Qatar (ed.) (2011)Google Scholar
  14. 14.
    Abuamer, I.M.; Celikoglu, H.B.: Local ramp metering strategy ALINEA: microscopic simulation based evaluation study on Istanbul freeways. Transp. Res. Proc. 22, 598–606 (2017)CrossRefGoogle Scholar
  15. 15.
    Abuamer, I.M.; Sadat, M.; Silgu, M.A.; Celikoglu, H.B.: Analyzing the effects of driver behavior within an adaptive ramp control scheme: a case-study with ALINEA. In: IEEE International Conference on 2017 Vehicular Electronics and Safety (ICVES), pp. 109–114. IEEE (2017)Google Scholar
  16. 16.
    Abuamer, I.M.; Silgu, M.A.; Celikoglu, H.B.: Micro-simulation based ramp metering on istanbul freeways: an evaluation adopting ALINEA. In: IEEE 19th International Conference on 2016 Intelligent Transportation Systems (ITSC), pp. 695–700. IEEE (2016)Google Scholar
  17. 17.
    Sadat, M.; Celikoglu, H.B.: Simulation-based variable speed limit systems modelling: an overview and a case study on Istanbul freeways. Transp. Res. Proc. 22, 607–614 (2017)CrossRefGoogle Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2018

Authors and Affiliations

  1. 1.Department of Civil EngineeringBirzeit UniversityBirzeitPalestine
  2. 2.Department of Civil and Architectural EngineeringQatar UniversityDohaQatar

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