A Novel Approach in Evaluating the Impact of Vehicle Age on Road Safety

  • Árpád Török Budapest University of Technology and Economics, Faculty of Transportation Engineering and Vehicle Engineering
Keywords: vehicle age, road accident, multilevel model, hierarchical structure

Abstract

This study examines the correlation between road accident casualties and the age of the vehicle, assuming 
that the age of vehicles and the improvements in their safety designs are related. The study evaluates the impact of the interrelationship between road segment characteristics and road accident type on vehicle age at the time of the accident (AVC). To analyse the nested relationship between these variables, a multinomial logistic regression (MML) model has been developed. The result of the analysis also duly finds that vehicle age has an emphatic role in the occurrence of accidents.

Author Biography

Árpád Török, Budapest University of Technology and Economics, Faculty of Transportation Engineering and Vehicle Engineering

Department of Automotive Technologies

References

Ghadi M, Török Á, Tánczos K. Study of the Economic Cost of Road Accidents in Jordan. Period. Polytech. Transp. Eng. 2018;46(3): 129-134. Available from: https://pp.bme.hu/tr/article/view/10392 [Accessed 18 June 2019].

Huang Z. Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Min. Knowl. Discov. 1998;2(3): 283-304. Available from: doi:10.1023/A:1009769707641 [Accessed 18 Nov. 2019].

Michalaki P, Quddus MA, Pitfield D, Huetson A. Exploring the factors affecting motorway accident severity in England using the generalised ordered logistic regression model. J. Safety Res. 2015;55: 89-97. Available from: https://www.sciencedirect.com/science/article/pii/S0022437515000833 [Accessed 3 Oct. 2019].

Yannis G, Athanasios T, George P. Investigation of road accident severity per vehicle type. Transportation Research Procedia. 2017;25: 2081-2088. Available from: https://www.sciencedirect.com/science/article/pii/S2352146517307081 [Accessed 5 Oct. 2019].

Blows S, Ivers RQ, Woodward M, Connor J, Ameratunga S, Norton R. Vehicle year and the risk of car crash injury. Inj. Prev. 2003;9(4): 353-356. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1731030/ [Accessed 1 Sep. 2019].

Høye A. Vehicle registration year, age, and weight – Untangling the effects on crash risk. Accid. Anal. Prev. 2019;123: 1-11. Available from: https://www.ncbi.nlm.nih.gov/pubmed/30447490 [Accessed 6 Aug. 2019].

Glassbrenner D. An Analysis of Recent Improvements to Vehicle Safety. Ann. Emerg. Med. 2013;61(2): 222. Available from: https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/811572 [Accessed 7 Sep. 2019].

National Highway Traffic Safety Administration. How vehicle age and model year relate to driver injury severity in fatal crashes; 2013. Available from: https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/811825 [Accessed 17 Oct. 2019].

Al-Ghamdi AS. Using logistic regression to estimate the influence of accident factors on accident severity. Accid. Anal. Prev. 2002;34(6): 729-741. Available from: https://www.sciencedirect.com/science/article/pii/S0001457501000732 [Accessed 3 Nov. 2019].

Yau KKW. Risk factors affecting the severity of single vehicle traffic accidents in Hong Kong. Accid. Anal. Prev. 2004;36(3): 333-340. Available from: https://www.ncbi.nlm.nih.gov/pubmed/15003577 [Accessed 3 Nov. 2019].

Haghighi N, Liu XC, Zhang G, Porter RJ. Impact of roadway geometric features on crash severity on rural two-lane highways. Accid. Anal. Prev. 2018;111: 34-42. Available from: https://www.ncbi.nlm.nih.gov/pubmed/29169103 [Accessed 3 Nov. 2019].

Greene D, Hossain A, Hofmann J, Helfand G, Beach R. Consumer willingness to pay for vehicle attributes: What do we Know?. Transportation Research Part A: Policy and Practice. 2018;118: 258-279. Available from: https://www.sciencedirect.com/science/article/pii/S0965856417308546 [Accessed 3 Nov. 2019].

Department for Transport. Road Safety Data 2014. [Online]. Available from: https://data.gov.uk/dataset/cb7ae6f0-4be6-4935-9277-47e5ce24a11f/road-safety-data [Accessed 1 Aug. 2019].

Department for Transport. Road Safety Data 2015. [Online]. Available from: https://data.gov.uk/dataset/cb7ae6f0-4be6-4935-9277-47e5ce24a11f/road-safety-data [Accessed 1 Aug. 2019].

Department for Transport. Road Safety Data 2016. [Online]. Available from: https://data.gov.uk/dataset/cb7ae6f0-4be6-4935-9277-47e5ce24a11f/road-safety-data [Accessed 1 Aug. 2019].

Zöldy M, Zsombók I. Modelling fuel consumption and refuelling of autonomous vehicles. MATEC Web of Conferences. 2018;235: 37. EDP Sciences. Available from: https://www.matec-onferences.org/articles/matecconf/abs/2018/94/matecconf_hort2018_00037/matecconf_hort2018_00037.html [Accessed 1 Aug. 2019].

Department for Transport-b. GB Road Traffic Counts 2014. [Online]. Available from: https://data.gov.uk/dataset/208c0e7b-353f-4e2d-8b7a-1a7118467acc/gb-roadtraffic-counts [Accessed 1 Aug. 2019].

Department for Transport-b. GB Road Traffic Counts 2015. [Online]. Available from: https://data.gov.uk/dataset/208c0e7b-353f-4e2d-8b7a-1a7118467acc/gb-roadtraffic-counts [Accessed 1 Aug. 2019].

Department for Transport-b. GB Road Traffic Counts 2016. [Online]. Available from: https://data.gov.uk/dataset/208c0e7b-353f-4e2d-8b7a-1a7118467acc/gb-road-traffic-counts [Accessed 1 Aug. 2019].

American Association of State Highway and Transportation Officials. Highway Safety Manual. 1st Edition; 2010.

Azen R, Walker CM. Categorical data analysis for the behavioral and social sciences; 2011.

Barcikowski RS. Statistical power with the group mean as the unit of analysis. J. Educ. Behav. 1981;6(3): 267-285. Available from: https://www.jstor.org/stable/1164877?seq=1#metadata_info_tab_contents [Accessed 23 Nov. 2019].

Boodlal L, Donnell ET, Porter RJ, Garimella D, Le T, Croshaw K, Himes C, Kulis P, Wood J. Factors Influencing Operating Speeds and Safety on Rural and Suburban Roads. Turner-Fairbank Highway Research Center. No. FHWA-HRT-15-030, 2015.

EurosStat. Available from: https://ec.europa.eu/eurostat/web/products-datasets/product?code=road_eqs_carage2019.04.05. [Accessed 23 Nov. 2019].

Bair ST, Huang RJ, Wang KC. Can vehicle maintenance records predict automobile accidents?. Journal of Risk and Insurance. 2012;79(2): 567-584. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/j.1539-6975.2011.01433.x [Accessed 23 Nov. 2019].

Knobloch K, Wagner S, Haasper C, Probst C, Krettek C, Otte D, Richter M. Sternal fractures occur most often in old cars to seat-belted drivers without any airbag often with concomitant spinal injuries: Clinical findings and technical collision variables among 42,055 crash victims. The Annals of Thoracic Surgery. 2006;82(2): 444- 450. https://www.sciencedirect.com/science/article/pii/S0003497506005935 [Accessed 23 Nov. 2019].

Szalay Z, Tettamanti T, Esztergár-Kiss D, Varga I, Bartolini C. Development of a test track for driverless cars: Vehicle design, track configuration, and liability considerations. Periodica Polytechnica Transportation Engineering. 2018;46(1): 29-35. Available from: https://pp.bme.hu/tr/article/view/10753 [Accessed 23 Nov. 2019].

Hulse LM, Xie H, Galea ER. Perceptions of autonomous vehicles: Relationships with road users, risk, gender and age. Safety Science. 2018;102: 1-13. Available from: https://www.sciencedirect.com/science/article/pii/S0925753517306999 [Accessed 23 Nov. 2019].

Zöldy M. Legal Barriers of Utilization of Autonomous Vehicles as Part of Green Mobility. In: Burnete N, Varga B. (eds) Proceedings of the 4th International Congress of Automotive and Transport Engineering (AMMA 2018). Proceedings in Automotive Engineering. Springer, Cham; 2019. Available from: https://doi.org/10.1007/978-3-319-94409-8_29 [Accessed 28 Oct. 2019].

Published
2020-11-10
How to Cite
1.
Török Árpád. A Novel Approach in Evaluating the Impact of Vehicle Age on Road Safety. PROMET [Internet]. 2020Nov.10 [cited 2020Nov.29];32(6):789-96. Available from: https://traffic.fpz.hr/index.php/PROMTT/article/view/3441
Section
Articles