Exploring the factors of major road traffic accidents: A case study of China

Abstract

Since the implementation of the transportation power strategy, China’s transportation industry has developed rapidly, yet the number of road traffic accidents has remained high in recent years. Many scholars have investigated the factors influencing traffic accidents to find the underlying mechanisms, thereby enhancing road traffic safety. Compared to general accidents, the factors influencing major road traffic accidents are more complex. This study focuses on examining the relationships between factors affecting major road traffic accidents. Data on 968 major road traffic accidents from 2012 to 2018 in China were collected and organized. The accident information fields were analyzed to identify seven attributes: accident province, accident region, accident quarter, accident time, accident form, accident vehicle, and weather condition. The Apriori association rule algorithm was employed to mine and solve the strong association rules between accident attribute values. The associations between different influencing factors and the form of accident results were analyzed, with a deeper exploration of three-factor and four-factor rules. The results indicate that certain causal factors jointly contribute to major accidents, particularly in the western region, represented by Guangxi. These accidents mainly involved trucks and occurred in rainy and snowy weather during the first quarter. The conclusions of this research can provide the transportation management department with measures to improve urban road traffic safety and reduce the occurrence of traffic accidents.

Graphical abstract

Keywords

road safety / traffic accidents / influencing factor / association rules / apriori algorithm

References

[1] AgrawalRSrikantR (1994). Fast Algorithms for Mining Association Rules. In: Proceedings of the 20th International Conference on Very Large Data Bases. Santiago: Chile, 487–499

[2] Ait-Mlouk A, Agouti T, (2019). DM-MCDA: A web-based platform for data mining and multiple criteria decision analysis: A case study on road accident. SoftwareX, 10: 100323

[3] Çeven S, Albayrak A, (2024). Traffic accident severity prediction with ensemble learning methods. Computers & Electrical Engineering, 114: 109101

[4] Hong J, Tamakloe R, Park D, (2020). Discovering insightful rules among truck crash characteristics using Apriori algorithm. Journal of Advanced Transportation, 2020( 1): 1

[5] HouY Q (2020). Analysis of causes of traffic accidents on different sections of expressway based on improved association rules. Dissertation for the Master’s Degree. Beijing: Beijing Jiaotong University (in Chinese)

[6] Huang D Z, Liang T T, Hu S P, Loughney S, Wang J, (2023). Characteristics analysis of intercontinental sea accidents using weighted association rule mining: Evidence from the Mediterranean Sea and Black Sea. Ocean Engineering, 287: 115839

[7] Kang L J, Li H, Li C, Xiao N, Sun H J, Buhigiro N, (2021). Risk warning technologies and emergency response mechanisms in Sichuan–Xizang Railway construction. Frontiers of Engineering Management, 8( 4): 582–594

[8] Kang L J, Lu Z Y, Meng Q, Gao S, Wang F W, (2019a). Maritime simulator based determination of minimum DCPA and TCPA in head-on ship-to-ship collision avoidance in confined waters. Transportmetrica A: Transport Science, 15( 2): 1124–1144

[9] Kang L J, Meng Q, Zhou C B, Gao S, (2019b). How do ships pass through L-shaped turnings in the Singapore Strait. Ocean Engineering, 182: 329–342

[10] Kuang L, Yan H, Zhu Y, Tu S, Fan X, (2019). Predicting duration of traffic accidents based on cost-sensitive Bayesian network and weighted K-nearest neighbor. Journal of Intelligent Transport Systems, 23( 2): 161–174

[11] Ma X, Ding C, Luan S, Wang Y, Wang Y, (2017). Prioritizing influential factors for freeway incident clearance time prediction using the gradient boosting decision trees method. IEEE Transactions on Intelligent Transportation Systems, 18( 9): 2303–2310

[12] Martín L, Baena L, Garach L, López G, de Oña J, (2014). Using data mining techniques to road safety improvement in Spanish roads. Procedia: Social and Behavioral Sciences, 160: 607–614

[13] Moslem S, Farooq D, Esztergar-Kiss D, Yaseen G, Senapati T, Deveci M, (2024). A novel spherical decision-making model for measuring the separateness of preferences for drivers’ behavior factors associated with road traffic accidents. Expert Systems with Applications, 238: 122318

[14] XiaoG PZhuX N (2011). Traffic safety engineering. China: Railway Publishing House (in Chinese)

[15] Yu S, Jia Y H, Sun D Y, (2019). Identifying factors that influence the patterns of road crashes using association rules: A case study from Wisconsin, United States. Sustainability, 11( 7): 1925

[16] Zhang C J, He J, Bai C G, Yan X T, Wang C W, Guo Y Z, (2022). Exploring relationships between months and different crash types on mountainous freeways using a combined modeling approach. Journal of Advanced Transportation, 2022( 1): 1

[17] Zhang S L, Jin H Q, Song Y, Yu W S, Sun L D, (2016). Genetic study identifies CBLN4 as a novel susceptibility gene for accident proneness. Frontiers of Engineering Management, 3( 1): 30–38

Competing Interests

The authors declare that they have no competing interests.

RIGHTS & PERMISSIONS

2024 Higher Education Press