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Although the occurrence of road accidents and the number of road accident casualties in almost all Polish voivodeships has decreased over the last few years, the rate of this change varies considerably from region to region. To provide a better understanding of such a tendency, panel data regression models are proposed to conduct this pilot research which evaluates the relative performance of Polish regions in terms of their road traffic safety. Panel data are multi-dimensional data which involve measurements over time. In the research, a voivodeship is a unit analysed at a group level, whereas a year is a unit analysed at a time level. A two-way error component regression model has been applied to survey the impact of regressors, the group effects, and time effects on a dependent variable. The analysis has been conducted using data acquired from the Statistics Poland Local Data Bank website, as well as from the General Directorate for National Roads and Motorways. The panel data from 16 regions in Poland and the 2012–2018 period have been investigated. The examined models refer to road traffic safety indices defined based on the following characteristics: the number of road accidents, the number road fatalities, and the number of people injured. The results of all the three models indicate a negative effect as regards the GDP per capita, (car) motorisation rate, the indicator of government expenditure for current maintenance of national roads, and the road length per capita. A positive association has been found between the truck motorisation rate and the indicator of local government expenditure on roads. The impact of the region’s urbanisation indicators on road safety is ambiguous as, on the one hand, its increase causes a reduction in the road accident and accident injury indices, but, on the other hand, it produces a rise in the accident fatality index. In the models, the significance of time effects has been identified; a decreasing time trend suggests a general improvement in road safety from year to year. Most of the group effects have turned out to be highly significant. However, the effects differ as regards both the road accident and the accident injury indices in magnitude and direction.
Rocznik
Tom
Strony
40--51
Opis fizyczny
Bibliogr. 20 poz., tab., wykr.
Twórcy
autor
- Kielce University of Technology, Poland
autor
- Kielce University of Technology, Poland
Bibliografia
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- Albalate, D., Fernández, L., Yarygina, A. (2013). The road against fatalities: Infrastructure spending vs. regulation? Accident Analysis and Prevention, 59, 227-239. doi: 10.1016/j.aap.2013.06.008
- Antoniou, C., Yannis, G., Papadimitriou, E., & Lassarre, S. (2016). Relating traffic fatalities to GDP in Europe on the long term. Accident Analysis and Prevention, 92, 89-96. doi: 10.1016/j.aap.2016.03.025
- Badura, D. (2017). Urban traffic modeling and simulation. Forum Scientiae Oeconomia, 5(4), 85-97. doi: 10.23762/FSO_VOL5NO4_17_7
- Baltagi, B. H. (2005). Econometric Analysis of Panel Data. John Wiley & Sons, Ltd.
- Besharati, M., Kashani, A.T., & Washington, S. (2020). A comparative analysis of road safety across the provinces of Iran from 2005 to 2015. International Journal of Sustainable Transportation, 1-9. doi: 10.1080/15568318.2019.1710877
- Castro-Nuño, M., Castillo-Manzano, J. I., & Fageda, X. (2018). The role of road safety in a sustainable urban mobility: An econometric assessment of the Spanish NUTS-3case. International Journal Of Sustainable Transportation, 12(3), 205-217. doi: 10.1080/15568318.2017.1346733
- Dupont, E., Commandeur, J. J. F., Lassarre, S., Bijleveldb, F., Martensena, H., Antonioud, C., Papadimitrioud, E., Yannis, G., Hermans, E., Pérezf, K., Santamarina-Rubio, E., Usami, D. S., & Giustiniani G. (2014). Latent risk and trend models for the evolution of annual fatality numbers in 30 European countries. Accident Analysis and Prevention, 71, 327-369. doi: 10.1016/j. aap.2014.06.009
- Farchi S., Molino N., Rossi P. G., Borgia P., Krzyzanowski M., Dalbokova D., Kim R., & The European Road Accident Indicator Working Group (2006). Defining a common set of indicators to monitor road accidents in the European Union. BMC Public Health, 6(183), 1-12. doi: 10.1186/1471-2458-6-183
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- Laaraj, N., & Jawab, F. (2018). Road accident modeling approaches: literature review. International Colloquium on Logistics and Supply Chain Management (LOGISTIQUA), Tangier, 188-193. doi: 10.1109/LOGISTIQUA.2018.8428276
- Muthusamy, A. P., Rajendran, M., Ramesh, K., & Sivaprakash P. (2015). A Review on Road Traffic Accident and Related Factors. International Journal of Applied Engineering Research, 10(11), 28177-28183.
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- Söderlund, N., & Zwi, A. B. (1995). Traffic-related mortality in industrialized and less developed countries. Bull. World Health Organization, 73(2), 175-182.
- Statistics Poland Local Data Bank. Retrieved from https:// bdl.stat.gov.pl/BDL/dane/podgrup/temat
- Ustawa o drogach publicznych. Stan prawny aktualny na dzień: 05.06.2020. Dz.U.2020.0.470 t.j. - Ustawa z dnia 21 marca 1985 r. o drogach publicznych [Act on public roads. Legal status as at: 05.06.2020. Setup log 2020.0.470 – Act of 21 March 1985 on public roads]. Retrieved from https://www.lexlege.pl/ustawa-odrogach-publicznych
- Wachnicka, J., Kustra, W., Jamroz, K., & Budzyński, M. (2018). Road infrastructure safety management tools for national and voivodeship roads in regions (NUTS 2) in Poland. 12th International Road Safety Conference GAMBIT 2018 “ROAD INNOVATIONS FOR SAFETY – the national and regional perspective.
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Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-11a90480-8b1f-4d15-9aa3-a27c405da638