Identifying Interrelated Factors of Fatal and Injury Traffic Accidents Using Association Rules

Abstract

This study aims to investigate the possible relationships of risk factors related to traffic accidents playing important roles in increasing the likelihood of accidents. In the previous studies, parametric models are mostly used to investigate the causes of traffic accidents. As a non-parametric data mining model with its increasing usage in recent years; association rule mining was employed in this study to analyse the traffic accident data for the period of 2015 and 2020 in the city of Sakarya, Turkey. The analysis of the data studied revealed the relationships among the external/environmental, driver, road, vehicle and nature of accident factors. Some important rules regarding accidents occurring on daylight came into prominence within the scope of this study. In addition, the correlations between the driver casualties and their education level and ages are established to be related. The findings are beneficial for transportation authorities to apply effective operational strategies and campaigns to increase the road safety.

Keywords

• Traffic accidents
• road crashes
• traffic safety
• data mining
• association rules
• driver faults

References

1. World Health Organization (WHO) 2022. https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries (accessed August 15, 2022).
2. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. 2020.
3. Injuries in the European Union 2009-2018. EuroSafe; 2021.
4. Ministry of the Interior. Turkey Road Traffic Safety Strategy Report (2021-2030). 2021.
5. The World Bank. World Bank 2022. https://data.worldbank.org/country/turkiye (accessed August 25, 2022).
6. International Transport Forum. Road Safety Annual Report. IRTAD-OECD; 2020.
7. Turkey Traffic Accident Report-2021. Turkey General Directorate of Highways. 2022.
8. Kadilar GO. Effect of driver, roadway, collision, and vehicle characteristics on crash severity: a conditional logistic regression approach. Int J Inj Contr Saf Promot 2016;23:135–44. https://doi.org/10.1080/17457300.2014.942323.

9. Zhang G, Yau KKW, Chen G. Risk factors associated with traffic violations and accident severity in China. Accid Anal Prev 2013;59:18–25. https://doi.org/10.1016/j.aap.2013.05.004.
10. Ma Z, Lu X, Chien SI-J, Hu D. Investigating factors influencing pedestrian injury severity at intersections. Traffic Inj Prev 2018;19:159–64. https://doi.org/10.1080/15389588.2017.1354371.
11. Batouli G, Guo M, Janson B, Marshall W. Analysis of pedestrian-vehicle crash injury severity factors in Colorado 2006–2016. Accid Anal Prev 2020;148:105782. https://doi.org/10.1016/j.aap.2020.105782.
12. Alver Y, Demirel MC, Mutlu MM. Interaction between socio-demographic characteristics: Traffic rule violations and traffic crash history for young drivers. Accid Anal Prev 2014;72:95–104. https://doi.org/10.1016/j.aap.2014.06.015.
13. Katanalp BY, Eren E. The novel approaches to classify cyclist accident injury-severity: Hybrid fuzzy decision mechanisms. Accid Anal Prev 2020;144:105590. https://doi.org/10.1016/j.aap.2020.105590.
14. Adanu EK, Lidbe A, Tedla E, Jones S. Factors associated with driver injury severity of lane changing crashes involving younger and older drivers. Accid Anal Prev 2021;149:105867. https://doi.org/10.1016/j.aap.2020.105867.
15. Chiou Y-C, Fu C, Ke C-Y. Modelling two-vehicle crash severity by generalized estimating equations. Accid Anal Prev 2020;148:105841. https://doi.org/10.1016/j.aap.2020.105841.
16. AlKheder S, AlRukaibi F, Aiash A. Risk analysis of traffic accidents’ severities: An application of three data mining models. ISA Trans 2020;106:213–20. https://doi.org/10.1016/j.isatra.2020.06.018.
17. Cai Q. Cause Analysis of Traffic Accidents on Urban Roads Based on an Improved Association Rule Mining Algorithm. IEEE Access 2020;8:75607–15. https://doi.org/10.1109/ACCESS.2020.2988288.
18. Yu S, Jia Y, Sun D. Identifying Factors that Influence the Patterns of Road Crashes Using Association Rules: A case Study from Wisconsin, United States. Sustainability 2019;11:1925. https://doi.org/10.3390/su11071925.
19. Das S, Tamakloe R, Zubaidi H, Obaid I, Alnedawi A. Fatal pedestrian crashes at intersections: Trend mining using association rules. Accid Anal Prev 2021;160:106306. https://doi.org/10.1016/j.aap.2021.106306.
20. Kong X, Das S, Jha K, Zhang Y. Understanding speeding behavior from naturalistic driving data: Applying classification based association rule mining. Accid Anal Prev 2020;144:105620. https://doi.org/10.1016/j.aap.2020.105620.
21. Zhu S. Investigation of vehicle-bicycle hit-and-run crashes. Traffic Inj Prev 2020;21:506–11. https://doi.org/10.1080/15389588.2020.1805444.
22. Hong J, Tamakloe R, Park D. Discovering Insightful Rules among Truck Crash Characteristics using Apriori Algorithm. J Adv Transp 2020;2020:1–16. https://doi.org/10.1155/2020/4323816.
23. Das S, Dutta A, Sun X. Patterns of rainy weather crashes: Applying rules mining. J Transp Saf Secur 2020;12:1083–105. https://doi.org/10.1080/19439962.2019.1572681.
24. Xu C, Bao J, Wang C, Liu P. Association rule analysis of factors contributing to extraordinarily severe traffic crashes in China. J Safety Res 2018;67:65–75. https://doi.org/10.1016/j.jsr.2018.09.013.
25. Turkish Statistical Institute 2021. https://cip.tuik.gov.tr/# (accessed December 16, 2022).
26. Sakarya City Guide. Sakarya Metropolitan Municipality. https://www.sakarya.bel.tr/ (accessed March 10, 2023).
27. Turkey General Directorate of Highways. http://yol.kgm.gov.tr/KazaKaraNoktaWeb/ (accessed March 1, 2022).
28. Hand DJ. Principles of Data Mining 2007:2.
29. Srikant R, Agrawal R. Mining Generalized Association Rules. 1995.
30. Geurts K, Thomas I, Wets G. Understanding spatial concentrations of road accidents using frequent item sets. Accident Analysis and Prevention 2005; 37:787–99. https://doi.org/10.1016/j.aap.2005.03.023.
31. Montella A. Identifying crash contributory factors at urban roundabouts and using association rules to explore their relationships to different crash types. Accid Anal Prev 2011;43:1451–63. https://doi.org/10.1016/j.aap.2011.02.023.
32. Montella A, Aria M, D’Ambrosio A, Mauriello F. Analysis of powered two-wheeler crashes in Italy by classification trees and rules discovery. Accid Anal Prev 2012;49:58–72. https://doi.org/10.1016/j.aap.2011.04.025.
33. Pande A, Abdel-Aty M. Market basket analysis of crash data from large jurisdictions and its potential as a decision support tool. Saf Sci 2009;47:145–54. https://doi.org/10.1016/j.ssci.2007.12.001.
34. Das S, Dutta A, Jalayer M, Bibeka A, Wu L. Factors influencing the patterns of wrong-way driving crashes on freeway exit ramps and median crossovers: Exploration using ‘Eclat’ association rules to promote safety. Int J Transp Sci Technol 2018;7:114–23. https://doi.org/10.1016/j.ijtst.2018.02.001.
35. Agrawal R, Imieliński T, Swami A. Mining association rules between sets of items in large databases. ACM SIGMOD Rec 1993;22:207–16. https://doi.org/10.1145/170036.170072.
36. Albuquerque FDB de, Awadalla DM. Roadside Fixed-Object Collisions, Barrier Performance, and Fatal Injuries in Single-Vehicle, Run-Off-Road Crashes. Safety 2020;6:27. https://doi.org/10.3390/safety6020027.
37. Karabulut NC, Ozen M. Exploring Driver Injury Severity Using Latent Class Ordered Probit Model: A Case Study of Turkey. KSCE J Civ Eng 2023;27:1312–22. https://doi.org/10.1007/s12205-023-0473-6.
38. Celik AK, Oktay E. A multinomial logit analysis of risk factors influencing road traffic injury severities in the Erzurum and Kars Provinces of Turkey. Accid Anal Prev 2014;72:66–77. https://doi.org/10.1016/j.aap.2014.06.010.
39. Bédard M, Guyatt GH, Stones MJ, Hirdes JP. The independent contribution of driver, crash, and vehicle characteristics to driver fatalities. Accid Anal Prev 2002;34:717–27. https://doi.org/10.1016/S0001-4575(01)00072-0.
40. Factor R. The effect of traffic tickets on road traffic crashes. Accid Anal Prev 2014;64:86–91. https://doi.org/10.1016/j.aap.2013.11.010.
41. Paleti R, Eluru N, Bhat CR. Examining the influence of aggressive driving behavior on driver injury severity in traffic crashes. Accid Anal Prev 2010;42:1839–54. https://doi.org/10.1016/j.aap.2010.05.005.
42. Manual on Uniform Traffic Control Devices. MUTCD.pdf 2009.
43. Cakici Z, Murat YS. An Investigation on the Awareness of Traffic Signs: Denizli Sample. Bitlis Eren Üniversitesi Fen Bilim Derg 2017;6:21–21. https://doi.org/10.17798/bitlisfen.305485.