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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
12.07.2022
DOI
https://doi.org/10.7307/ptt.v34i4.3997
ISSUE
2022 (Vol 34), Issue 4
LICENSE
Copyright (c) 2022 Robertas PEČELIŪNAS, Vidas ŽURAULIS, Paweł DROŹDZIEL, Saugirdas PUKALSKAS
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Prediction of Road Accident Risk for Vehicle Fleet Based on Statistically Processed Tire Wear Model

Authors:Robertas PEČELIŪNAS, Vidas ŽURAULIS, Paweł DROŹDZIEL, Saugirdas PUKALSKAS

Abstract

The goal of the paper is to investigate the impact of tire tread depth on road accident risk and to develop an accident rate prediction model. The state of 4288 vehicle tires using tread depth gauge was inspected and processed statistically. The tread depth of the most worn tire from each vehicle was registered for further analy-sis. Based on the collected data, a statistical tire tread depth model for an insurance company vehicle fleet had been developed. The conformity of the gamma distribu-tion to the data was verified upon applying the Pearson compatibility criterion. The paper provides the histo-grams of the frequencies of tire tread depths and the theoretical curves of the distribution density. The prob-ability of the accident risk depending on the tire tread depth (adaptive risk index) was calculated applying the formed distributions and risk index dependence on the tire tread depth for the inspected vehicle fleet. Accord-ing to the developed prediction model, an upgrade of the regulation for the minimum allowed tire tread depth by 2 mm (up to 3.6 mm) could reduce road accident risk (caused by poor adhesion to road surface) to 19.3% for the chosen vehicle fleet. Such models are useful for road safety experts, insurance companies and accident cost evaluation specialists by predicting expenses related to insurance events.

Keywords:road accident, prediction, tread depth, distribution, accident rate, accident risk

References

  1. [1] World Health Organization. Global status report on road safety 2018. 2018.
  3. [2] Road Safety Annual Report 2020. OECD/ITF International Traffic Safety Data and Analysis Group (IRTAD); 2020.
  5. [3] Bucsuházy K, et al. Human factors contributing to the road traffic accident occurrence. Transportation Research Procedia. 2020;45: 555–61. doi: 10.1016/j.trpro.2020.03.057.
  7. [4] Eboli L, Forciniti C, Mazzulla G. Factors influencing accident severity: An analysis by road accident type. Transportation Research Procedia. 2020;47: 449–56. doi: 10.1016/j.trpro.2020.03.120.
  9. [5] Leonavičienė T, et al. Investigation of factors that have affected the outcomes of road traffic accidents on Lithuanian roads. BJRBE. 2020;15: 1–20. doi: 10.7250/bjrbe.2020-15.504.
  11. [6] Chen F, Chen S. Probabilistic assessment of vehicle safety under various driving conditions: A reliability approach. Procedia - Social and Behavioral Sciences. 2013;96: 2414–24. doi: 10.1016/j.sbspro.2013.08.270.
  13. [7] Haq MT, Zlatkovic M, Ksaibati K. Assessment of tire failure related crashes and injury severity on a mountainous freeway: Bayesian binary logit approach. Accident Analysis & Prevention. 2020;145: 105693. doi: 10.1016/j.aap.2020.105693.
  15. [8] Das S, Dutta A, Geedipally SR. Applying Bayesian data mining to measure the effect of vehicular defects on crash severity. Journal of Transportation Safety & Security. 2019: 1–17. doi: 10.1080/19439962.2019.1658674.
  17. [9] Biesse F. Analysis of wet road usage with a driving safety concern. VDA Technischer Kongress: The Future of Mobility, 14-15 Mar. 2019, Berlin, Germany. 2019.
  19. [10] Druta C, Kassing A, Gibbons R, Alden VA. Assessing driver behavior using shrp2 adverse weather data. Journal of Safety Research. 2020;73: 283–95. doi: 10.1016/j.jsr.2020.03.013.
  21. [11] Council Directive 1989 of 18 July 1989 on the approximation of the laws of the Member States relating to the tread depth of tyres of certain categories of motor vehicles and their trailers (89/459/EEC). 1989. https://www.legislation.gov.uk/eudr/1989/459?view=plain.
  23. [12] Jansen S, et al. Study on some safety-related aspects of tyre use: Final report. Publications Office. TNO 2014 R11423, 2014. doi: 10.2832/67191.
  25. [13] Christ D. Simulating the relative influence of tire, vehicle and driver factors on forward collision accident rates. Journal of Safety Research. 2020;73: 253–62. doi: 10.1016/j.jsr.2020.03.009.
  27. [14] Wright KRS, Botha TR, Els PS. Effects of age and wear on the stiffness and friction properties of an SUV tyre. Journal of Terramechanics. 2019;84: 21–30. doi: 10.1016/j.jterra.2019.04.001.
  29. [15] Caban J, et al. Impact of specific factors on the state of the tire pressure value. The Archives of Automotive Engineering. 2019;85: 137–48. doi: 10.14669/AM.VOL85.ART10.
  31. [16] Gajek A. Directions for the development of periodic technical inspection for motor vehicles safety systems. The Archives of Automotive Engineering. 2018;80: 37–51. doi: 10.14669/AM.VOL80.ART3.
  33. [17] Choi E-H. Tire-Related Factors in the Pre-Crash Phase. Washington, DC: National Highway Traffic Safety Administration; 2012.
  35. [18] Regulation (EC) No 1222/2009 of the European Parliament and of the Council of 25 November 2009 on the labelling of tyres with respect to fuel efficiency and other essential parameters. 2009. https://www.legislation.gov.uk/eur/2009/1222.
  37. [19] Regulation (EU) 2020/740 of the European Parliament and of the Council of 25 May 2020 on the labelling of tyres with respect to fuel efficiency and other parameters, amending Regulation (EU) 2017/1369 and repealing Regulation (EC) No 1222/2009 PE/3/2020/INIT. 2020. https://www.legislation.gov.uk/eur/2020/740/introduction.
  39. [20] Todoroff V, Paupy S, Biesse F, Le Chenadec Y. The mechanisms involved during the wet braking of new and worn tires. Vehicle System Dynamics. 2019;57: 1601–20. doi: 10.1080/00423114.2018.1535126.
  41. [21] D'Apuzzo M, Evangelisti A, Nicolosi V. An exploratory step for a general unified approach to labelling of road surface and tyre wet friction. Accident Analysis & Prevention. 2020;138: 105462. doi: 10.1016/j.aap.2020.105462.
  43. [22] Hermange C, Oger G, Le Chenadec Y, Le Touzé D. A 3D SPH–FE coupling for FSI problems and its application to tire hydroplaning simulations on rough ground. Computer Methods in Applied Mechanics and Engineering. 2019;355: 558–90. doi: 10.1016/j.cma.2019.06.033.
  45. [23] Andrieux A, Vandanjon PO, Lengelle R, Chabanon C. New results on the relation between tyre–road longitudinal stiffness and maximum available grip for motor car. Vehicle System Dynamics. 2010;48: 1511–33. doi: 10.1080/00423111003770421.
  47. [24] Žuraulis V, Garbinčius G, Skačkauskas P, Prentkovskis O. Experimental study of winter tyre usage according to tread depth and temperature in vehicle braking performance. Iran J Sci Technol Trans Mech Eng. 2020;44: 83–91. doi: 10.1007/s40997-018-0243-0.
  49. [25] Blythe, W. Tire tread depth and wet traction – A review. ITEC 2014, Akron, Ohio. 2014.
  51. [26] Nævestad T-O, Elvebakk B, Phillips RO. The safety ladder: developing an evidence-based safety management strategy for small road transport companies. Transport Reviews. 2018;38: 372–93. doi: 10.1080/01441647.2017.1349207.
  53. [27] Spitzhüttl F, Goizet F, Unger T, Biesse F. The real impact of full hydroplaning on driving safety. Accident Analysis & Prevention. 2020;138: 105458. doi: 10.1016/j.aap.2020.105458.
  55. [28] Edunyah I. Causes of tyre failure on road traffic accident; A case study of Takoradi Township. IJSRP. 2016;6(2): 30–5. https://www.ijsrp.org/research-paper-0216/ijsrp-p5005.pdf.
  57. [29] Weyssenhoff A, Opala M, Koziak S, Melnik R. Characteristics and investigation of selected manufacturing defects of passenger car tires. Transportation Research Procedia. 2019;40: 119–26. doi: 10.1016/j.trpro.2019.07.020.
  59. [30] Torok A. Importance of tires: Case study in Hungary. American Journal of Vehicle Design. 2015;3: 27–30. doi: 10.12691/ajvd-3-1-4.
  61. [31] Deublein M, et al. Prediction of road accidents: A Bayesian hierarchical approach. Accident Analysis & Prevention. 2013;51: 274–91. doi: 10.1016/j.aap.2012.11.019.
  63. [32] Gutierrez-Osorio C, Pedraza C. Modern data sources and techniques for analysis and forecast of road accidents: A review. Journal of Traffic and Transportation Engineering (English Edition). 2020;7: 432–46. doi: 10.1016/j.jtte.2020.05.002.
  65. [33] Mannering F. Temporal instability and the analysis of highway accident data. Analytic Methods in Accident Research. 2018;17: 1–13. doi: 10.1016/j.amar.2017.10.002.
  67. [34] Lord D, Mannering F. The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives. Transportation Research Part A: Policy and Practice. 2010;44: 291–305. doi: 10.1016/j.tra.2010.02.001.
  69. [35] Soltanali H, Rohani A, Abbaspour-Fard MH, Farinha JT. A comparative study of statistical and soft computing techniques for reliability prediction of automotive manufacturing. Applied Soft Computing. 2021;98: 106738. doi: 10.1016/j.asoc.2020.106738.
  71. [36] Mannering F, Bhat CR, Shankar V, Abdel-Aty M. Big data, traditional data and the tradeoffs between prediction and causality in highway-safety analysis. Analytic Methods in Accident Research. 2020;25: 100113. doi: 10.1016/j.amar.2020.100113.
  73. [37] Danquah B, et al. Statistical model verification and validation concept in automotive vehicle design. Procedia CIRP. 2020;91: 261–70. doi: 10.1016/j.procir.2020.02.175.
  75. [38] Belyaev A, Pushkarev A, Kuzmichev V. Vehicle reliability and safety through simulation of the sequence of restoration of motor vehicle components. Transportation Research Procedia. 2018;36: 50–5. doi: 10.1016/j.trpro.2018.12.042.
  77. [39] Ayyub BM, McCuen RH. Probability, statistics, and reliability for engineers and scientists. 3rd ed. Boca Raton, FL: CRC Press; 2011.
  79. [40] Bertsche B. Reliability in automotive and mechanical engineering: Determination of component and system reliability. Berlin: Springer; 2008.
  81. [41] Makarova I, Mukhametdinov E, Tsybunov E. Management of the reliability of intelligent vehicles as a method to improve traffic safety. Transportation Research Procedia. 2018;36: 465–71. doi: 10.1016/j.trpro.2018.12.129.
  83. [42] Mannering FL, Bhat CR. Analytic methods in accident research: Methodological frontier and future directions. Analytic Methods in Accident Research. 2014;1: 1–22. doi: 10.1016/j.amar.2013.09.001.
  85. [43] Elvik R, Vaa T. (eds) The handbook of road safety measures. Amsterdam, San Diego, CA: Elsevier; 2005.
  87. [44] Elvik R. Spravočnik po bezopasnosti dorožnogo dviženija: Obzor meroprijatij po bezopasnosti dorožnogo dviženija. Oslo: Institut ėkonomiki transporta [Transportøkonomisk institutt]; 1996.
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