Abstract
In recent years, people have often settled in suburban communities around large cities. Mobility is usually identified with one’s own means of transport, which provides access to work or shopping. In the era of urban development, it is important to analyze the impact of various features on the safety of city and region inhabitants. With help come modern theories from computer science and statistics that can be applied in practice through formalized notation. Then, it becomes possible, in the long perspective, to increase the safety of road users. The aim of this paper is to analyze the influence of various characteristics based on a large testing ground (all national roads in the Świętokrzyskie Voivodeship). The Bayesian regression model for road accidents was evaluated based on actual data of road incidents that occurred in Poland, in the Świętokrzyskie Voivodeship, in 1999–2012. Logistic regression analysis was used to create the Bayesian model. The a posteriori distributions of the random variables of the model were obtained using a sampling Monte Carlo method based on Markov chains. In addition, the paper presents the selected possibilities of using the SAS system in supporting data analysis.
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Marek, M. (2022). Bayesian Regression Model Estimation: A Road Safety Aspect. In: Ben Ahmed, M., Boudhir, A.A., Karaș, İ.R., Jain, V., Mellouli, S. (eds) Innovations in Smart Cities Applications Volume 5. SCA 2021. Lecture Notes in Networks and Systems, vol 393. Springer, Cham. https://doi.org/10.1007/978-3-030-94191-8_13
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