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

Xi Zhang, Shouming Qi (), Ao Zheng, Ye Luo and Siqi Hao
Additional contact information
Xi Zhang: School of Architecture, Harbin Institute of Technology, Shenzhen 518055, China
Shouming Qi: Shenzhen Urban Public Safety and Technology Institute, Shenzhen 518000, China
Ao Zheng: Shenzhen Urban Public Safety and Technology Institute, Shenzhen 518000, China
Ye Luo: Shenzhen Urban Public Safety and Technology Institute, Shenzhen 518000, China
Siqi Hao: School of Port and Shipping Management, Guangzhou Maritime College, Guangzhou 510700, China

Sustainability, 2023, vol. 15, issue 4, 1-19

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.

Keywords: fatal traffic accidents; Term Frequency-Inverse Document Frequency; spatial autocorrelation; principal component analysis; safety management strategies (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/4/3259/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/4/3259/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:4:p:3259-:d:1064438

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().