Urban Development Policy Making

Urban Development Policy Making

Modeling Rear-End and Lane-Change Crash Severity Of Passenger Vehicles At Urban Roundabouts

Document Type : Original Article

Authors
1 Department of Highway and Transportation, Imam Khomeini International University, Qazvin, Iran
2 Associated Professor, Department of Highway and Transportation & Road and Transportation Engineering Research Center, Tarbiat Modares University, Tehran, Iran
10.22034/judpm.2026.574180.1088
Abstract
Predicting crash severity based on recorded crash data is associated with several challenges, including limited accessibility, incomplete information, and potential reporting errors. Consequently, surrogate safety measures provide a proactive approach that reduces reliance on crash data and enables the assessment of factors influencing crash severity prior to crash occurrence. Urban roundabouts, as intersections with a circular central island, play an important role in moderating vehicle speeds, making the analysis of factors affecting interaction severity particularly relevant. In this study, aerial image processing techniques were used to analyze rear-end interactions among passenger cars at the Roudband roundabout, located in Dezful County, Khuzestan Province, Iran. Performance-related variables were extracted using safety analysis software, and gamma regression models were employed to examine the factors influencing interaction severity. Surrogate safety measures, including time to collision (TTC), were used as the dependent variable to evaluate factors affecting the severity of rear-end and lane-change interactions. The results indicate that maximum speed has the strongest effect in the final rear-end interaction model, such that a one-unit increase in maximum speed leads to a 0.488-unit reduction in interaction severity. This finding highlights the statistically significant mitigating effect of maximum speed on the safety level of traffic interactions and demonstrates the influence of speed variations on reducing interaction severity at urban roundabouts.
Keywords
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1. de Oña J, de Oña R, Eboli L, Forciniti C, Mazzulla G. How to identify the key factors that affect driver perception of accident risk. A comparison between Italian and Spanish driver behavior. Accident Analysis & Prevention. 2014;73:225–35.
2. Guo R, Liu L, Wang W. Review of Roundabout Capacity Based on Gap Acceptance. Journal of Advanced Transportation. 2019;2019:1–11.
3. Hasanvand M, Nasiri ASA, Rahmani O, Shaaban K, Samadi H. A Conflict-Based Safety Diagnosis of SCI Roundabouts Using a Surrogate Safety Measure Model. Sustainability. 2023;15(17):13166.
4. Lord D, Qin X, Geedipally SR. Highway safety analytics and modeling: Elsevier; 2021.
5. Giuffrè O, Granà A, Tumminello ML, Giuffrè T, Trubia S, Sferlazza A, et al. Evaluation of Roundabout Safety Performance through Surrogate Safety Measures from Microsimulation. Journal of Advanced Transportation. 2018;2018(1):4915970.
6. Sadeq H, Sayed T. Automated roundabout safety analysis: diagnosis and remedy of safety problems. Journal of Transportation Engineering. 2016;142(12):04016062.
7. Parker Jr M, Zegeer CV. Traffic conflict techniques for safety and operations: Observers manual. United States. Federal Highway Administration; 1989.
8. Harwood DW, Bauer KM, Potts IB, Torbic DJ, Richard KR, Rabbani ER, et al. Safety effectiveness of intersection left-and right-turn lanes. Transportation Research Record. 2003;1840(1):131–9.
9. Ambros RT, Paukrt J, Ambros J, Turek R, Paukrt J, editors. Road safety evaluation using traffic conflicts: pilot comparison of micro-simulation and observation-Jiří. International Conference on Traffic and Transport Engineering-Belgrade; 2014.
10. Yang H. Simulation-based evaluation of traffic safety performance using surrogate safety measures: Rutgers The State University of New Jersey, School of Graduate Studies; 2012.
11. Hydén C. The development of a method for traffic safety evaluation: The Swedish Traffic Conflicts Technique. Bulletin Lund Institute of Technology, Department. 1987(70).
12. Saunier N, Sayed T. Probabilistic framework for automated analysis of exposure to road collisions. Transportation research record. 2008;2083(1):96–104.
13. Yang H, Ozbay K, Bartin B. Application of simulation-based traffic conflict analysis for highway safety evaluation. Proceedings of the 12th WCTR, Lisbon, Portugal. 2010;4.
14. Songchitruksa P, Tarko AP. Practical method for estimating frequency of right-angle collisions at traffic signals. Transportation research record. 2006;1953(1):89–97.
15. Tripathi RC, Gupta RC, Pair RK. Statistical tests involving several independent gamma distributions. Annals of the Institute of Statistical Mathematics. 1993;45(4):773–86.
16. Pan J-J, Mahmoudi MR, Baleanu D, Maleki M. On comparing and classifying several independent linear and non-linear regression models with symmetric errors. Symmetry. 2019;11(6):820.
17. Bates DM, Watts DG. Nonlinear regression analysis and its applications: Wiley New York; 1988.
18. Burnham KP, Anderson DR. Model selection and multimodel inference: a practical information-theoretic approach: Springer; 2002.
19. Hardin JWH, J. M. Generalized Linear Models and Extensions. 2nd ed. College Station, TX: Stata Press; 2007.
 
Volume 3, Issue 2
Summer 2026
Pages 233-247

  • Receive Date 28 November 2025
  • Revise Date 29 December 2025
  • Accept Date 27 February 2026
  • Publish Date 02 July 2026