Predicting Road Accident Counts in Poland and the Czech Republic Using Neural Network Models

• Autorzy: Gorzelańczyk, Piotr , Ližbetinová, Lenka , Pečman, Jan
• Języki publikacji: EN

Abstrakty

EN

Every year, there is a decline in the number of car accidents reported in Poland, the Czech Republic, and globally. While recent trends due to the pandemic have influenced these figures, the overall rate remains significant. Therefore, it is crucial to take measures aimed at reducing this number. The primary focus of this article is to analyze the traffic accident statistics for Poland and the Czech Republic. Annual data regarding traffic incidents in both countries has been scrutinized to achieve this. Projections for 2024 to 2030 have been developed based on police reports. Various neural network models were utilized to forecast the number of accidents. The findings indicate that the number of traffic incidents is likely to stabilize. This stabilization can be viewed in the context of the increasing number of vehicles on the roads and the expansion of new highways. Additionally, selecting sample sizes for training, testing, and validation is crucial in influencing the results. Forecasting the number of traffic accidents is important for environmental protection, as accidents can lead to air and water pollution and increase noise, negatively affecting human health and ecosystems.

Słowa kluczowe

EN

road accident; pandemic; forecasting; neural networks; Poland; Czech Republic

• Czasopismo: Rocznik Ochrona Środowiska
• Rocznik: 2024
• Tom: Tom 26
• Strony: 603--615
• Opis fizyczny: Bibliogr. 39 poz., rys., tab.

Twórcy

autor

Gorzelańczyk, Piotr

• Department of Transport, Stanislaw Staszic State University of Applied Sciences in Pila, Poland

autor

Ližbetinová, Lenka

• Department of Transport and Logistics, Institute of Technology and Business in České Budějovice, Czech Republic ,

autor

Pečman, Jan

• Department of Transport and Logistics, Institute of Technology and Business in České Budějovice, Czech Republic

Bibliografia

• Abdullah, E., Emam, A. (2016). Traffic accidents analyzer using big data. Proceedings - 2015 International Conference on Computational Science and Computational Intelligence, CSCI 2015, 392-397. https://doi.org/10.1109/CSCI.2015.187
• Arteaga, C., Paz, A., Park, J. (2020). Injury severity on traffic crashes: A text mining with an interpretable machine-learning approach. Safety Science, 132, 104988. https://doi.org/10.1016/j.ssci.2020.104988
• Bąk, I., Cheba, K., Szczecińska, B. (2019). The statistical analysis of road traffic in cities of Poland. Transportation Research Procedia, 39, 14-23. https://doi.org/10.1016/J.TRPRO.2019.06.003
• Chamier-Gliszczyński, N. (2012). Structure analysis of system mobility in urban areas. Congress Proceedings, CLC 2012: Carpathian Logistics Congress, 509-515, 111467, Jesenik, Czech Republic, Tanger Ltd.
• Chamier-Gliszczyński, N. (2012a). Modeling system mobility in urban areas. Congress Proceedings, CLC 2012: Carpathian Logistics Congress, 501-508, 111467, Jesenik, Czech Republic, Tanger Ltd.
• Chamier-Gliszczynski, N. (2016). City Logistics – Sustainable Urban Mobility. CLC 2015: Carpathian Logistics Congress – Conference Proceedings, 263-268, Jesenik, Czech Republic, Tanger Ltd.
• Chand, A., Jayesh, S., Bhasi, A.B. (2021). Road traffic accidents: An overview of data sources, analysis techniques and contributing factors. Materials Today: Proceedings, 47, 5135-5141. https://doi.org/10.1016/J.MATPR.2021.05.415
• Chen, C. (2017). Analysis and Forecast of Traffic Accident Big Data. ITM Web of Conferences, 12, 04029. https://doi.org/10.1051/ITMCONF/20171204029
• Chovancová, M., Stopka, O., Klapita, V. (2017). Modeling the distribution network applying the principles of linear programming. Proceedings – 21st International Scientific on Conference Transport Means 2017, Juodkrante; Lithuania; 20-22 September 2017, Code 135093, 73-77.
• Čubranić-Dobrodolac, M., Švadlenka, L., Čičević, S., Dobrodolac, M. (2020). Modelling driver propensity for traffic accidents: a comparison of multiple regression analysis and fuzzy approach. International Journal of Injury Control and Safety Promotion, 27(2), 156-167. https://doi.org/10.1080/17457300.2019.1690002
• Cubranic-Dobrodolac, M., Svadlenka, L., Cicevic, S., Trifunovic, A., Dobrodolac, M. (2020). Using the Interval Type-2 Fuzzy Inference Systems to Compare the Impact of Speed and Space Perception on the Occurrence of Road Traffic Accidents. Mathematics, 8(9). https://doi.org/10.3390/math8091548
• Čubranić-Dobrodolac, M., Švadlenka, L., Čičević, S., Trifunović, A., Dobrodolac, M. (2022). A bee colony optimization (BCO) and type-2 fuzzy approach to measuring the impact of speed perception on motor vehicle crash involvement. Soft Computing, 26(9), 4463-4486. https://doi.org/10.1007/s00500-021-06516-4
• Czech statistics office. (2024). Road Traffic accidents in Czech Republic – time series. https://csu.gov.cz/produkty/nehody_v_doprave_casove_rady
• Dyczkowska, J., Olkiewicz, M., Chamier-Gliczynski, N., Królikowski, T. (2023). Mobility-as-a-Service (MaaS) as a solution platform for the city and the region: case study. Procedia Computer Science, 225, 4092-4100, 196245. https://doi.org/10.1016/j.procs.2023.10.405
• Dyczkowska, J., Chamier-Gliszczynski, N., Olkiewicz, M., Królikowski, T. (2023a). Decision support in the area of Logistics 4.0. Procedia Computer Science, 225, 4758-4765, 196245. https://doi.org/10.1016/j.procs.2023.10.475
• Gorzelanczyk, P., Huk, A. (2022). Road traffic safety: A case study of the Pila poviat in Poland. Scientific Journal of Silesian University of Technology. Series Transport, 114, 31-42. https://doi.org/10.20858/SJSUTST.2022.114.3
• Hudec, J., Cződörová, R. (2022). Analysis of the system of driving schools in selected EU countries and the Slovak Republic. Perner's Contacts, 17(1). https://doi.org/10.46585/pc.2022.1.2115
• Hudec, J., Sarkan, B., Caban, J., Stopka, O. (2021). The Impact of Driving Schools' Training on Fatal Traffic Accidents in the Slovak Republic. Scientific Journal of Silesian University of Technology-Series Transport, 110, 45-57. https://doi.org/10.20858/sjsutst.2021.110.4
• Khaliq, K.A., Chughtai, O., Shahwani, A., Qayyum, A., Pannek, J. (2019). Road Accidents Detection, Data Collection and Data Analysis Using V2X Communication and Edge/Cloud Computing. Electronics, 8(8), 896. https://doi.org/10.3390/electronics8080896
• Kielc, R., Sąsiadek, M., Woźniak, W. (2018). Adoption of the evolutionary algorithm to automate the scheduling of the production processes. Proceedings of 31st International Business Information Management Association Conference, IBIMA 2018, International Business Information Management Association Conference, Milan 2018, 5039-5046, 143853.
• Lake, B.M., Ullman, T.D., Tenenbaum, J.B., Gershman, S.J. (2017). Building machines that learn and think like people. Behavioral and Brain Sciences, 40, e253. https://doi.org/10.1017/S0140525X16001837
• Marr, B. (2019). The Amazing Ways eBay Is Using Artificial Intelligence To Boost Business Success. Forbes: Enterprise Tech. https://www.forbes.com/sites/bernardmarr/2019/04/26/the-amazing-ways-ebay-is-using-artificial-intelligence-to-boost-business-success/
• Oronowicz-Jaśkowiak, W. (2019). The application of neural networks in the work of forensic experts in child abuse cases. Advances in Psychiatry and Neurology/Postępy Psychiatrii i Neurologii, 28(4), 273-282. https://doi.org/10.5114/PPN.2019.92489
• Polish Police. (2024). Statistic Road Accident. https://statystyka.policja.pl/
• Rajput, H., Som, T., Kar, S. (2015). An automated vehicle license plate recognition system. Computer, 48(8), 56-61. https://doi.org/10.1109/MC.2015.244
• Šarkan B., Pal’o J., Loman M., Stopka O., Caban J., Čeháková K., Gołębiowski W., Pečman J. (2024). Research on the Quantification of Exhaust Emission Volumes in an Opted Road Section. Acta Polytechnica Hungarica, 21(7), 9-30. https://doi.org/10.12700/APH.21.7.2024.7.2
• Stopka, O. (2022). Modelling Distribution Routes in City Logistics by Applying Operations Research Methods. Promet – Traffic & Transportation, 34(5), 739-754. https://doi.org/10.7307/ptt.v34i5.4103
• Tambouratzis, T., Souliou, D., Chalikias, M., Gregoriades, A. (2014). Maximising Accuracy and Efficiency of Traffic Accident Prediction Combining Information Mining with Computational Intelligence Approaches and Decision Trees. Journal of Artificial Intelligence and Soft Computing Research, 4(1), 31-42. https://doi.org/10.2478/JAISCR-2014-0023
• Vilaça, M., Silva, N., Coelho, M.C. (2017). Statistical Analysis of the Occurrence and Severity of Crashes Involving Vulnerable Road Users. Transportation Research Procedia, 27, 1113-1120. https://doi.org/10.1016/J.TRPRO.2017.12.113
• Witt, A. (2023). Determination of the Number of Required Charging Stations on a German Motorway Based on Real Traffic Data and Discrete Event-Based Simulation. LOGI – Scientific Journal on Transport and Logistics, 14(1), 1-11. https://doi.org/10.2478/logi-2023-0001
• Wójcik, A. (2014). Autoregressive vector models as a response to the critique of multi-equation structural econometric models. Publishing House of the University of Economics in Katowice, 193.
• World Health Organization. (2018). Global status report on road safety 2018 Summary. In Global report (Issue 1). http://apps.who.int/bookorders
• Wozniak, W., Walkowiak, J., Sasiadek, M., Stryjski, R. (2018). Organisation of the Research Process into an Innovative, Anti-Clogging Assembly for Heavy Vehicles in the Interests of Increased Road Safety. 32nd Conference of the International-Business-Information-Management-Association (IBIMA), Seville, Spain 2018, Vision 2020: Sustainable Economic Development and Application of Innovation Management, 4772-4784.
• Wu, Y., Schuster, M., Chen, Z., Le, Q.V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., Klingner, J., Shah, A., Johnson, M., Liu, X., Kaiser, Ł., Gouws, S., Kato, Y., Kudo, T., Kazawa, H., … Dean, J. (2016). Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. 1–23. http://arxiv.org/abs/1609.08144
• Yadav, S., Rishi, R. (2022). Algorithm for Creating Optimized Green Corridor for Emergency Vehicles with Minimum Possible Disturbance in Traffic. LOGI – Scientific Journal on Transport and Logistics, 13(1), 84-95. https://doi.org/10.2478/logi-2022-0008
• Yang, Z., Zhang, W., Feng, J. (2022). Predicting multiple types of traffic accident severity with explanations: A multi-task deep learning framework. Safety Science, 146, 105522. https://doi.org/10.1016/J.SSCI.2021.105522
• Yu, A. (2019). How Netflix Uses AI, Data Science, and Machine Learning – From A Product Perspective. Becoming Human Exploring Artificial Intelligence & What It Means to Be Human. https://becominghuman.ai/how-netflix-uses-ai-and-machine-learning-a087614630fe
• Zheng, Z., Wang, C., Wang, P., Xiong, Y., Zhang, F., Lv, Y. (2018). Framework for fusing traffic information from social and physical transportation data. PLOS ONE, 13(8), e0201531. https://doi.org/10.1371/JOURNAL.PONE.0201531
• Zhu, L., Lu, L., Zhang, W., Zhao, Y., Song, M. (2019). Analysis of Accident Severity for Curved Roadways Based on Bayesian Networks. Sustainability, 11(8), 2223. https://doi.org/10.3390/SU11082223

Identyfikatory

DOI

10.54740/ros.2024.053