Dataset of traffic accidents in motorcyclists in Bogotá, Colombia

Ospina-Mateus, Holman; Garcia, Shyrle Berrio; Jiménez, Leonardo Quintana; Salas-Navarro, Katherinne · 2022 · Crossref

DOI: 10.1016/j.dib.2022.108461

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Summary

This data article addresses the critical issue of road safety for motorcyclists in Bogotá, Colombia, a city with high rates of traffic fatalities. Motivated by Colombia’s ranking as the second-highest in South America for motorcycle fatality rates and the significant proportion of motorcyclist deaths among all road users, the authors present a comprehensive dataset designed to analyze and predict accident severity. The study aims to fill a gap in available data for vulnerable road users in Latin America, providing a resource for comparative safety studies and the development of preventive countermeasures. The dataset was constructed from official records provided by the Secretariat of Mobility and Transit of Bogotá, covering the period from January 2013 to February 2018. From an initial pool of 175,245 traffic accidents involving 337,828 road actors, the authors isolated 35,693 accidents specifically involving motorcyclists. The data was processed using MS Excel and IBM SPSS, and enriched with external historical data on infrastructure conditions from the Governmental Institution of Urbanism of Bogotá and weather conditions from the Institute of Hydrology, Meteorology and Environmental Studies of Colombia. The final dataset comprises 28 variables categorized into five groups: road actors involved, motorcyclist demographics (age, gender), weather and timing conditions, road location and quality, and accident characteristics. All data was anonymized to comply with ethical and legal privacy standards. The findings describe the distribution and characteristics of the 35,693 motorcyclist accidents. In terms of severity, 28% of incidents resulted in material damage only, 69% involved injuries, and 3% were fatal. The data reveals that the majority of accidents involved a single motorcycle (94.6%) and two road actors (89.8%). Most crashes occurred on main roads (71.9%) during daytime hours (66.5%) and on working days (96.9%). Demographically, the majority of involved motorcyclists were male (92.1%) and aged between 20 and 39 years (81.0%). Collision was the most common accident type (76.6%), followed by run-over incidents (20.0%). The dataset also details correlations between variables such as road condition, precipitation levels, and time of day with accident outcomes. The significance of this work lies in providing a robust, open-access resource for transportation engineering and safety research. By consolidating detailed information on pavement conditions, weather, and crash dynamics, the dataset enables researchers to identify causal factors in motorcyclist accidents. This facilitates the application of data mining and machine learning techniques to predict accident severity, as demonstrated in the authors’ related research. Ultimately, the data supports the development of targeted interventions to reduce injuries and fatalities among motorcyclists in Bogotá and offers a benchmark for comparative studies across Latin America.

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Topics

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• motorcycle crash typology
• vru crash typology

Information type

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• Empirical Findings: crash risk outcomes
• Methodological Resource: dataset resource