Investigation of Factors That Have Affected the Outcomes of Road Traffic Accidents on Lithuanian Roads

Authors

DOI:

https://doi.org/10.7250/bjrbe.2020-15.504

Keywords:

accident data, fatal accident, logistic regression, road safety, road traffic accident

Abstract

The purpose of this paper is to analyse the possibility for predicting the outcome of a road traffic accident concerning the traffic environment, personal traits of the traffic participant and the vehicle, i.e. aiming to answer the question whether specific values of the factors analysed to increase the likelihood of a fatal accident. The logistic regression model that allows identifying the relationship between the dependent and independent variables were used in the research. Other methods for describing and analysing categorical variables were also used alongside the logistic regression. When analysing the results, it was recognised that the odds ratio above 1 shows a higher likelihood for a representative of the category in question to be involved in a fatal accident compared to a representative of the base category. Odds ratios of likelihoods for calculation of the road traffic accident types show that the likelihood of a fatal accident is statistically significant affected by rollovers or driving into obstacles, compared to vehicular collisions. When summarising the results, it was stated that most of the factors researched have an impact on the outcome of a road traffic accident. The influence of some factors has a higher probability of resulting in a fatal accident as compared to other factors.

References

Antov, D., & Smirnovs, J. (2016). 78 Improving road safety in the Baltic states − role of strategies. Injury Prevention, 22(Suppl 2), A30.1-A30. https://doi.org/10.1136/injuryprev-2016-042156.78

Belin, M. Å., Tillgren, P., & Vedung, E. (2012). Vision Zero – a road safety policy innovation. International Journal of Injury Control & Safety Promotion, 19(2), 171-179. https://doi.org/10.1080/17457300.2011.635213

Brazinova, A., & Majdan, M. (2016). Road traffic mortality in the Slovak Republic in 1996–2014. Traffic Injury Prevention, 17(7), 692-698. https://doi.org/10.1080/15389588.2016.1143095

Bureika, G., Gaidamauskas, E., Kupinas, J., Bogdevičius, M., & Steišūnas, S. (2017). Modelling the assessment of traffic risk at level crossings of Lithuanian railways. Transport, 32(3), 282-290. https://doi.org/10.3846/16484142.2016.1244114

Bureika, G., Žuraulis, V., & Sadauskas, V. (2012, October). Research on automobile technical state impact on road traffic accident level in the country. In Transport Means-2012: Proceedings of the 16th International Conference (pp. 69-72).

Chen, C. (2017). Analysis and forecast of traffic accident big data. In ITM Web of Conferences (Vol. 12, p. 04029). EDP Sciences. https://doi.org/10.1051/itmconf/20171204029

Ćosić, M., Šimunović, L., & Jakovljević, M. (2019). Relationships between external factors and pedestrian accident blackspots – a case study of the city of Zagreb. Promet-Traffic & Transportation, 31(3), 329-340. https://doi.org/10.7307/ptt.v31i3.3119

Denning, G. M., & Jennissen, C. A. (2016). All-terrain vehicle fatalities on paved roads, unpaved roads, and off-road: evidence for informed roadway safety warnings and legislation. Traffic Injury Prevention, 17(4), 406-412. https://doi.org/10.1080/15389588.2015.1057280

Drozdziel, P., & Wrona, R. (2018, April). Legal and utility problems of accidents on express roads and motorways. In 2018 XI International Science-Technical Conference Automotive Safety (pp. 1-5). IEEE. https://doi.org/10.1109/AUTOSAFE.2018.8373315

Gailienė, I., & Laurinavičius, A. (2017). The need and benefit of slab track: case of Lithuania. Gradevinar, 69(5), 387-396. https://doi.org/10.14256/JCE.1776.2016

Gupta, M., Solanki, V. K., & Singh, V. K. (2017). Analysis of datamining technique for traffic accident severity problem: a review. In Proceedings of the Second International Conference on Research in Intelligent & Computing in Engineering (pp. 197-199). https://doi.org/10.15439/2017R121

Hosmer, Jr, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (Vol. 398). John Wiley & Sons.

Kumar, S., & Toshniwal, D. (2015). A data mining framework to analyse road accident data. Journal of Big Data, 2(1), 26. https://doi.org/10.1186/s40537-015-0035-y

Li, L., Shrestha, S., & Hu, G. (2017, June). Analysis of road traffic fatal accidents using data mining techniques. In 2017 IEEE 15th International Conference on Software Engineering Research, Management & Applications (SERA) (pp. 363-370). https://doi.org/10.1109/SERA.2017.7965753

Lithuanian Road Administration under the Ministry of Transport and Communications of the Republic of Lithuania (2019). Statistics of fatal and injury road accidents in Lithuania, 2015−2018. (in Lithuanian)

Madleňák, R., Hoštáková, D., Madleňáková, L., Drozdziel, P., & Török, A. (2018). The analysis of the traffic signs visibility during night driving. Advances in Science & Technology Research Journal, 12(2). https://doi.org/10.12913/22998624/92103

Olmuş, H., & Erbaş, S. (2012). Analysis of traffic accidents caused by drivers by using Log-linear models. Promet − Traffic & Transportation, 24(6), 495-504. https://doi.org/10.7307/ptt.v24i6.1201

Peltola, H., & Luoma, J. (2017). Comparison of road safety in Finland and Sweden. European Transport Research Review, 9(1), 3. https://doi.org/10.1007/s12544-016-0220-x

Pukalskas, S., Pečeliūnas, R., Sadauskas, V., Kilikevičienė, K., & Bogdevičius, M. (2015). The methodology for calculation of road accident costs. Transport, 30(1), 33-42. https://doi.org/10.3846/16484142.2015.1020871

Russo, F., & Comi, A. (2017). From the analysis of European accident data to safety assessment for planning: the role of good vehicles in urban area. European Transport Research Review, 9(1), 9. https://doi.org/10.1007/s12544-017-0225-0

Shokohyar, S., Taati, E., & Zolfaghari, S. (2017). The Effect of Drivers‘ Demographic Characteristics on Road Accidents in Different Seasons Using Data Mining. Promet - Traffic & Transportation, 29(6), 555-567. https://doi.org/10.7307/ptt.v29i6.2342

Svensson, T., Summerton, J., & Hrelja, R. (2014). The politics of speed–local and regional actors’ views on speed limits, traffic safety and mobility in Sweden. European Transport Research Review, 6(1), 43-50. https://doi.org/10.1007/s12544-013-0109-x

Šeibokaitė, L., Endriulaitienė, A., Sullman, M. J., Markšaitytė, R., & Žardeckaitė- Matulaitienė, K. (2017). Difficulties in emotion regulation and risky driving among Lithuanian drivers. Traffic Injury Prevention, 18(7), 688-693. https://doi.org/10.1080/15389588.2017.1315109

World Health Organization (2013). Global status report on road safety 2013: supporting a decade of action: summary (No. WHO. NMH. VIP 13.01). World Health Organization.

Xi, J., Gao, Z., Niu, S., Ding, T., & Ning, G. (2013). A hybrid algorithm of traffic accident data mining on cause analysis. Mathematical Problems in Engineering, 2013. https://doi.org/10.1155/2013/302627

Yannis, G., Papadimitriou, E., Chaziris, A., & Broughton, J. (2014). Modeling road accident injury under-reporting in Europe. European Transport Research Review, 6(4), 425-438. https://doi.org/10.1007/s12544-014-0142-4

Zeileis, A., Meyer, D., & Hornik, K. (2007). Residual-based shadings for visualising (conditional) independence. Journal of Computational & Graphical Statistics, 16(3), 507-525. https://doi.org/10.1198/106186007X237856

Žuraulis, V., Nagurnas, S., Pečeliūnas, R., Pumputis, V., & Skačkauskas, P. (2018). The analysis of drivers‘reaction time using cell phone in the case of vehicle stabilisation task. International Journal of Occupational Medicine & Environmental Health, 31(5), 633. https://doi.org/10.13075/ijomeh.1896.01264

Downloads

Published

23.12.2020

How to Cite

Leonavičienė, T., Pukalskas, S., Pumputis, V., Kulešienė, E., & Žuraulis, V. (2020). Investigation of Factors That Have Affected the Outcomes of Road Traffic Accidents on Lithuanian Roads. The Baltic Journal of Road and Bridge Engineering, 15(5), 1-20. https://doi.org/10.7250/bjrbe.2020-15.504