Road Traffic Accidents in the District of Setúbal
Synopsis
Road Traffic Accidents (RTA) are one of the greatest calamities of our time, due to the human losses and to the irreparable physical and psychological damage they cause to several victims, also involving a very relevant economic dimension.
To scientifically support the study of the problem of serious RTA in Setúbal, the University of Évora established a partnership with the Comando Territorial da Guarda Nacional Republicana de Setúbal (Territorial Command of the Portuguese Gendarmerie of Setúbal), which led to the research project, funded by the Foundation for Science and Technology (FCT), Statistical Modelling of Road Traffic Accidents in the District of Setúbal (MOPREVIS).
In this book, the main results of the project are synthesised in an infographic way, based on a mixed approach between statistical techniques, artificial intelligence, and geographic information systems. Besides an exploratory data analysis focusing on the main variables associated with RTA, the determinants for the occurrence and nature of RTA are presented. The profile of the intervenients is outlined, and a spatial analysis is carried out. Furthermore, it is performed a scale analysis of the municipalities of Palmela and Sesimbra and a study of the effect of the COVID-19 pandemic on RTA. Finally, the predictive models and the digital decision support tool are presented.
This book constitutes an important contribution towards the implementation of appropriate measures that make it possible to reduce serious RTA in the district of Setúbal, but it is also assumed to be a scalable instrument for the whole national territory.
References
Infante, P., Jacinto, G., Afonso, A., Rego, L., Nogueira, P., Silva,M., Nogueira, V., Saias, J., Quaresma, P., Santos, D., Gois, P., Manuel, P.R. (accepted jan 2023). Factors that influence the type of road traffic accidents: a case study in a district of Portugal. Sustainability.
Infante, P., Afonso., A., Jacinto, G., Rego, L., Cesar, R., Nogueira, P., Silva, M., Nogueira, V., Saias, J., Quaresma, P., Santos, D., Gois, P., Manuel, P. R. (2022). Some determinants for road accidents severity in the district of Setúbal. In Recent Developments in Statistics and Data Science. SPE 2021 (Eds. Bispo, R., Henriques-Rodrigues, L., Alpizar-Jara, R., De Carvalho, M.). Springer Proceedings in Mathematics & Statistics, vol. 398, 203-214. Springer, Cham. https://doi.org/10.1007/978-3-031-12766-3_14
Infante, P., Jacinto, G., Afonso, A., Rego, L., Nogueira, V., Quaresma, P., Saias, J., Santos, D., Nogueira, P., Silva, M., Costa, R. P., Gois, P., Manuel, P. R. (2022). Comparison of Statistical and Machine Learning Models on Road Traffic Accident Severity Classification. Computers, 11(5), 80. https://doi.org/10.3390/computers11050080
Santos, D., Saias, J., Quaresma, P., & Nogueira, V. B, (2021). Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction. Computers, 10(12), 157. http://dx.doi.org/10.3390/computers10120157


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