Modelling of Road Transport Safety Indicators in Russian Regions

Marina Malashenko () and Svetlana Gutman
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Marina Malashenko: Graduate School of Industrial Economics, Peter the Grate Polytechnic University, St. Petersburg 195251, Russia
Svetlana Gutman: Graduate School of Industrial Economics, Peter the Grate Polytechnic University, St. Petersburg 195251, Russia

Sustainability, 2025, vol. 17, issue 14, 1-24

Abstract: Introduction. Road safety issues occupy scientists around the world. This study is aimed at creating a comprehensive digital model that will become a tool for developing recommendations for improving road safety in the regions of the Russian Federation. Methods. The assessment of the current state of road safety in the regions of Russia was carried out by means of rating. The object of the study was studied using econometric models, machine learning models, and system dynamics; sensitivity analysis and a balanced scorecard were used. Results. The regions of Russia were divided into three groups according to the level of safety. The econometric model and machine learning model made it possible to assess the influence of independent variables on dependent variables. The identified interrelations formed the basis of a system dynamics model. It was concluded that it is possible to extrapolate the results to groups of regions. For each group of regions, recommendations are given on the formation of a strategy for improving road safety. Conclusions. The practical significance of the study lies in the creation of a tool for the formation of recommendations for the creation of a strategy for improving road safety in the regions of the Russian Federation.

Keywords: road safety; regions of the Russian Federation; econometric modeling; machine learning applications; system dynamics; panel data; strategic planning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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