Luz, K.; Tavares, J.E.D.R.; Barbosa, J.L.V.; Iglesia, D.H.D.L.; Leithardt, V.R.Q. RoadLytics: Road Accidents Analytics Using Artificial Intelligence to Support Deaths’ Prevention on Highways. Preprints2021, 2021050698. https://doi.org/10.20944/preprints202105.0698.v1
APA Style
Luz, K., Tavares, J.E.D.R., Barbosa, J.L.V., Iglesia, D.H.D.L., & Leithardt, V.R.Q. (2021). RoadLytics: Road Accidents Analytics Using Artificial Intelligence to Support Deaths’ Prevention on Highways. Preprints. https://doi.org/10.20944/preprints202105.0698.v1
Chicago/Turabian Style
Luz, K., Daniel Hernández de la Iglesia and Valderi Reis Quietinho Leithardt. 2021 "RoadLytics: Road Accidents Analytics Using Artificial Intelligence to Support Deaths’ Prevention on Highways" Preprints. https://doi.org/10.20944/preprints202105.0698.v1
Abstract
Daily thousands of people and goods move along Brazilian Federal highways. Traffic accidents are numerous on these highways and have a significant impact, whether on the economy or the health system. Identifying predictor variables, the probability of an event occurring and how to mitigate them are of paramount importance for the actions of the transit authorities that manage these roads. The main contribution of this study is the development of a predictive machine learning model which uses open data to shows graphically the critical points in the highways. This model is fully reproducible and can be applied to any region worldwide helping to minimize the number of accidents and to prevent deaths by automotive collisions. For this study, 43 variables were analyzed supporting the identification of the causes of accidents with fatal victims on the main highways in the south of Brazil. RoadLytics is proposed as a supervised machine learning model, using the Random Forest algorithm to analyze about 33 thousand occurrences between 2017 and 2020. An exploratory analysis of the data was carried out to support the modeling and to facilitate data visualization. In this sense, heat maps were developed to support the analysis and identification of potential risk areas. The results show that BR386 highway registers the highest number of fatal occurrences, regardless of the season. Additionally, concerning the weather conditions, the analysis shows that 52% of accidents occurred in favorable conditions, such as clear skies, victimizing 501 people. The driver’s lack of attention is the main reason for the accidents’ occurrences. Applying the developed model, an accuracy of 77% was achieved for the classification of fatal accidents.
Keywords
Accidents, Data Analysis, Machine Learning, Transport
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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