Data-Driven Analysis of Fatal Urban Traffic Accident Characteristics and Safety Enhancement Research

Abstract

The occurrence of fatal traffic accidents often causes serious casualties and property losses, endangering travel safety. This work uses the statistical data of fatal road traffic accidents in Shenzhen from 2018 to 2022 as the basis to determine the characteristic patterns and the main influencing factors of the occurrence of fatal road traffic accidents. The accident description data are also analyzed using the analysis method based on Term Frequency-Inverse Document Frequency (TF-IDF) data mining to obtain the characteristics of accident fields, objects, and types. Furthermore, this work conducts a kernel density analysis combined with spatial autocorrelation to determine the hotspot areas of accident occurrence and analyze their spatial aggregation effects. A principal component analysis is performed to calculate the factors related to the accident subjects. Results showed that weak safety awareness of motorists and irregular driving operations are the main factors for the occurrence of accidents. Finally, targeted safety management strategies are proposed based on the analysis results. In the current data era, the research results of this paper can be used for the prevention and emergency of accidents to formulate corresponding measures, and provide a theoretical basis for decision making.