Dataset of traffic accidents in motorcyclists in Bogotá, Colombia

According to the World Health Organization, in 2016, Colombia obtained the tenth position worldwide, the third in the continent and the second in South America, according to the accident rate of 9.7 motorcycle fatalities per 100,000 populations. Between 2012 and 2021, the number of deceased and injured motorcyclists among all road users was 50%, with an annual average of 3140 fatal victims and 20,800 injured victims. Bogotá, Cali, and Medellín were the cities with the most accidents. In Bogota in 2017, the deaths of motorcyclists on the roads were around 32% of the road actors. This data article presents the dataset used to analyze and predict the severity of motorcyclist road accidents in Bogota in the article entitled “Extraction of decision rules using genetic algorithms and simulated annealing for prediction of severity of traffic accidents by motorcyclists” [1]. The data set was consolidated from the registration of 175,245 traffic accidents and the report of 337,828 road actors involved in crashes in Bogotá between January 2013 and February 2018. The data was compiled, processed, and enriched with additional information about infrastructure and weather conditions. The data corresponds to 35,693 motorcyclist traffic accidents, represented by 28 variables, and classified into five categories: road actors, motorcyclists and individuals involved, weather conditions and timing, road conditions and location and characteristics of the accident. The data on motorcyclist traffic accidents opens up a scenario to deepen and compare road safety in Latin America, where studies on vulnerable road users are limited. According to severity, the data on motorcycle traffic accidents recorded 28% with material damage, 69% with injured and 3% with fatal victim

Extraction of decision rules using genetic algorithms and simulated annealing for prediction of severity of traffic accidents by motorcyclists

The objective of this study is to analysis of accident of motorcyclists on Bogotá roads in Colombia. For detection of conditions related to crashes and their severity, the proposed model develops the strategies to enhance road safety. In this context, data mining and machine learning techniques are used to investigate 34,232 accidents by motorcyclists during January 2013 to February 2018. Both the Genetic algorithm and simulated annealing are applied in conjunction with mining rules (support, confidence, lift, and comprehensibility) as per objectives of the problem. The application of a hybrid algorithm allows for the creation and definition of optimal hierarchical decision rules for the prediction of the severity of motorcycle traffic accidents. The proposed method yields good results in the metrics of recall (90.07%), precision (89.87%), and accuracy (90.06%) on the data set. The results increase the prediction by 20–21% in comparisons with the following methods: Decision Trees (CART, ID3, and C4.5), Support Vector Machines (SVMs), K-Nearest Neighbor (KNN), Naive Bayes, Neural Networks, Random Forest, and Random Tree. The proposed method defines 11 rules for the prediction of accidents with material damage, 24 rules with injuries, and 12 rules with fatalities. The variables with the most recurrence in the definition of rules are time, weather and road conditions, and the number of victims involved in the accidents. Finally, the interactions of the conditions and characteristics presented in motorcycle accidents are analyzed which contribute to the definition of countermeasures for road safety. © 2021, Springer-Verlag GmbH Germany, part of Springer Nature