European Journal of Business Science and Technology 2025, 11(1):23-38 | DOI: 10.11118/ejobsat.2024.014

GAM Modelling of Daily Number of Traffic Accidents as a Function of Meteorological Variables in the Czech Republic

Petra Kolísková1, Jiří Neubauer1
1 University of Defence, Brno, Czech Republic

Meteorological conditions exert a considerable influence on traffic patterns. This paper examines the influence of meteorological variables on the daily number of traffic accidents requiring fire brigade intervention. The influence of meteorological variables, including maximum temperature, wind speed, air pressure, precipitation, snow cover and sunshine, was examined. A Generalized Additive Model for variables with a Poisson distribution was employed for modelling purposes, as this allows for the representation of non-linear dependencies. The analysis demonstrates that the lowest incidence of accidents occurs at temperatures approximating 10 °C. The average daily number of accidents increases with windy weather, the minimum number of accidents occurs at zero precipitation, and the accident rate rises with higher levels of sunshine. In the Czech Republic, the period of greatest risk in terms of road traffic accidents is the summer and winter months. The findings may have several practical applications, for example, in the improvement of meteorological warnings in traffic.

Keywords: traffic accidents, integrated rescue system, weather, Poisson distribution, generalised additive model
JEL classification: C02, C51

Received: August 7, 2024; Revised: November 23, 2024; Accepted: November 25, 2024; Published: June 30, 2025  Show citation

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Kolísková, P., & Neubauer, J. (2025). GAM Modelling of Daily Number of Traffic Accidents as a Function of Meteorological Variables in the Czech Republic. European Journal of Business Science and Technology11(1), 23-38. doi: 10.11118/ejobsat.2024.014
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References

  1. Anderson, D., Davidson, R., Himoto, K. & Scawthorn, C. 2015. Statistical Modeling of Fire Occurrence Using Data from the Thoku, Japan Earthquake and Tsunami. Risk Analysis: An Official Publication of the Society for Risk Analysis, 36 (2), 378-395. DOI: 10.1111/risa.12455 Go to original source...
  2. Aultman-Hall, L., Lane, D. & Lambert, R. R. 2009. Assessing Impact of Weather and Season on Pedestrian Traffic Volumes. Transportation Research Record: Journal of the Transportation Research Board, 2140 (1), 35-43. DOI: 10.3141/2140-04 Go to original source...
  3. Basu, S. & Saha, P. 2017. Regression Models of Highway Traffic Crashes: A Review of Recent Research and Future Research Needs. Procedia Engineering, 187, 59-66. DOI: 10.1016/j.proeng.2017.04.350 Go to original source...
  4. Becker, N., Rust, H. W. & Ulbrich, U. 2022a. Weather Impacts on Various Types of Road Crashes: A Quantitative Analysis Using Generalized Additive Models. European Transport Research Review, 14, 37 DOI: 10.1186/s12544-022-00561-2 Go to original source...
  5. Becker, N., Rust, H. W. & Ulbrich, U. 2022b. Modeling Hourly Weather-Related Road Traffic Variations for Different Vehicle Types in Germany. European Transport Research Review, 14, 16. DOI: 10.1186/s12544-022-00539-0 Go to original source...
  6. Brázdil, R., Chromá, K., Dolák, L., Řehoř, J., Řezníčková, L., Zahradníček, P. & Dobrovolný, P. 2021. Fatalities Associated with the Severe Weather Conditions in the Czech Republic, 2000-2019. Natural Hazards and Earth System Sciences, 21 (5), 1355-1382. DOI: 10.5194/nhess-21-1355-2021 Go to original source...
  7. Brázdil, R., Chromá, K., Dolák, L., Zahradníček, P., Řehoř, J., Dobrovolný, P. & Řezníčková, L. 2023. The 100-Year Series of Weather-Related Fatalities in the Czech Republic: Interactions of Climate, Environment, and Society. Water, 15 (10), 1965. DOI: 10.3390/w15101965 Go to original source...
  8. Brázdil, R., Chromá, K., Zahradníček, P., Dobrovolný, P. & Dolák, L. 2022. Weather and Traffic Accidents in the Czech Republic, 1979-2020. Theoretical and Applied Climatology, 149, 153-167. DOI: 10.1007/s00704-022-04042-3 Go to original source...
  9. Cerna, S., Guyeux, C., Arcolezi, H. H., Couturier, R. & Royer, G. 2020. A Comparison of LSTM and XGBoost for Predicting Firemen Interventions. In Rocha, Á., Adeli, H., Reis, L., Costanzo, S., Orovic, I. & Moreira, F. (eds.). Trends and Innovations in Information Systems and Technologies. WorldCIST 2020. Advances in Intelligent Systems and Computing, vol. 1160, pp. 424-434. DOI: 10.1007/978-3-030-45691-7_39 Go to original source...
  10. Clark, M. Generalized Aditive Models [online]. Available at: https://m-clark.github.io/generalized-additive-models/introduction.html. [Accessed 2024, April 4].
  11. ČHMÚ. 2018. Suché období 2014-2017: Vyhodnocení, dopady a opatření. Praha: Český hydrometeorologický ústav. 1. vyd. ISBN 978-80-87577-81-3.
  12. ČHMÚ. 2024. Český hydrometeorologický ústav [online]. Available at: https://www.chmi.cz/files/portal/docs/meteo/om/sivs/vitr.html. [Accessed 2024, July 15].
  13. Guyeux, C., Nicod, J.-M., Varnier, C., Al Masry, Z., Zerhouny, N., Omri, N. & Royer, G. 2020. Firemen Prediction by Using Neural Networks: A Real Case Study. In Bi, Y., Bhatia, R. & Kapoor, S. (eds.). Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol. 1037, pp. 541-552. Springer, Cham. DOI: 10.1007/978-3-030-29516-5_42 Go to original source...
  14. Kolísková, P. & Neubauer, J. 2024. Analysis of Traffic Accidents and the Deployment of the Fire Rescue Service in the Czech Republic. AD ALTA: Journal of Interdisciplinary Research, 14 (1), 290-295. Go to original source...
  15. Lepage, S. & Morency, C. 2020. Impact of Weather, Activities, and Service Disruptions on Transportation Demand. Transportation Research Record: Journal of the Transportation Research Board, 2675 (1), 294-304. DOI: 10.1177/0361198120966326 Go to original source...
  16. Li, X., Lord, D. & Zhang, Y. 2011. Development of Accident Modification Factors for Rural Frontage Road Segments in Texas Using Generalized Additive Models. Journal of Transportation Engineering, 137 (1), DOI: 10.1061/(ASCE)TE.1943-5436.0000202 Go to original source...
  17. Lord, D., Washington, S. & Ivan, J. N. 2005. Poisson, Poisson-gamma and Zero Inflated Regression Models of Motor Vehicle Crashes: Balancing Statistical Fit and Theory. Accident Analysis & Prevention, 37 (1), 35-46. DOI: 10.1016/j.aap.2004.02.004 Go to original source...
  18. PČR. 2024. Archiv zpravodajství [online]. Available at: https://www.policie.cz/clanek/dopravni-rizika-v-letnich-mesicich.aspx. [Accessed 2024, July 15].
  19. Pop, D. 2018. Generalised Poisson Linear Model for Fatal Crashes Analysis. Romanian Journal of Automotive Engineering, 24 (4), 131-134.
  20. Rodríguez-Pérez, J. R., Ordóñez, C., Roca-Pardiñas, J., Vecín-Arias, D. & Castedo-Dorado, F. 2020. Evaluating Lightning-Caused Fire Occurrence Using Spatial Generalized Additive Models: A Case Study in Central Spain. Risk Analysis, 40 (7), 1418-1437. DOI: 10.1111/risa.13488 Go to original source...
  21. Roh, H.-J. 2020. Assessing the Effect of Snowfall and Cold Temperatureon a Commuter Highway Traffic Volume Using Several Layers of Statistical Methods. Transportation Engineering, 2, 100022. DOI: 10.1016/j.treng.2020.100022 Go to original source...
  22. Thorsson, S., Lindqvist, M. & Lindqvist, S. 2004. Thermal Bioclimatic Conditions and Patterns of Behaviour in an Urban Park in Göteborg, Sweden. International Journal of Biometeorology, 48 (3), 149-156. DOI: 10.1007/s00484-003-0189-8 Go to original source...
  23. Wood, S. N. 2017. Generalized Additive Models: An Introduction with R. 2nd ed. New York: Chapman and Hall/CRC. DOI: 10.1201/9781315370279 Go to original source...
  24. Yannis, G. & Karlaftis, M. G. 2010. Weather Effects on Daily Traffic Accidents and Fatalities: Time Series Count Data Approach. In Proceedings of the 89th Annual Meeting of the Transportation Research Board, Washington, 17 pp.
  25. Zhang, Y., Xie, Y. & Li, L. 2012. Crash Frequency Analysis of Different Types of Urban Roadway Segments Using Generalized Additive Model. Journal of Safety Research, 43 (2), 107-114. DOI: 10.1016/j.jsr.2012.01.003 Go to original source...

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