Analysis of Influencing Factors of Urban Road Traffic Accidents Based on Association Rules

Jiaqi Yang

doi:10.62051/4en65x47

Authors

Jiaqi Yang

DOI:

https://doi.org/10.62051/4en65x47

Keywords:

Urban Road Traffic Accidents; Influencing Factors; Association Rules; Preventive Measures; Apriori Algorithm.

Abstract

Urban road traffic conditions are complex and changeable. Affected by multiple factors, the mechanism and main influencing factors of traffic accidents are difficult to define. This paper aims to study the distribution characteristics and main factors influencing urban road traffic accidents. According to the collected data of urban traffic accidents in Britain, the basic characteristics including trunk road flap, weather conditions, time, maximum speed limit, first road classification and other factors are all analyzed, with association rules used to explore its influence mechanism. From aspects of vehicle, environment and road, this paper not only focuses on the influence mechanism of the interaction of two factors and three factors in urban road traffic accidents, but also puts forward targeted accident prevention measures accordingly.

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References

[1] Chen, L., Li, C. Y., Zhan, L. et al. (2022). Influencing factor analysis and prediction of urban road traffic accident patterns. Journal of Chang’an University (Natural Science Edition), 42(04), 98-107. DOI:10.19721/j.cnki.1671-8879.2022.04.010.

[2] Vanlaar, W., Simpson, H., Mayhew, D. & Robertson, R. (2008). Fatigued and drowsy driving: A survey of attitudes, opinions and behaviors. Journal of Safety Research, 39(3): 303-309.

[3] Wan, J. Y. (2015). Analysis and preventive countermeasures of expressway traffic accidents in ice and snow weather. Nanjing: Master’s Dissertation of Southeast University.

[4] Song, L. G. (2024). Research on the influencing factors and countermeasures of traffic accidents in the operation area of highway renovation and expansion based on 5M1E. Transport Energy Conservation & Environmental Protection, 1-5.

Retrieved from http://kns.cnki.net/kcms/detail/10.1261.U.20240521.0845.002.html.

[5] Ke, X. A., Ding, L. M. & Zhao, D. (2023). Analysis on the influencing factors of traffic accidents severity for electric bicycles based on logistic-TAN. Journal of People’s Public Security University of China (Science and Technology), 29(02), 47-54.

[6] Li, R. & Zhao, L. H. (2014). Analysis on the influencing factors of traffic accidents. Journal of Mathematical Medicine, 27(06), 634-636.

[7] Cui, X. X. (2018). Analysis of road traffic accidents and the influential factors of severity. Dalian: Master’s Dissertation of Dalian University of Technology.

[8] Das, S., Dutta, A., Jalayer, M., Bibeka, A. & Wu, L. T. (2018). Factors influencing the patterns of wrong-way driving crashes on freeway exit ramps and median crossovers: Exploration using ‘Eclat’ association rules to promote safety. International Journal of Transportation Science and Technology, 7(2): 114-123.

[9] Wu, T. C., He, M. M., Feng, H. Y. et al. (2024). Medication rule analysis of traditional Chinese medicine compound in treatment of type 2 diabetes mellitus with qi and yin deficiencies based on data mining. Chinese Traditional and Herbal Drugs, 55(14), 4811-4823.

[10] Wang, J. D., Huang, Y., Zhou, K. Y. et al. (2024). River regulation planning method of water conservancy project based on weighted association rules. Pearl River Water Transport, (13): 117-119.

[11] Ma, X. P., Cao, S. Z. & Li, Y. (2024). Correlation analysis of print inspection data based on optimized Apriori algorithm. Journal of Beijing Institute of Graphic Communication, (06): 22-26. DOI:10.19461/j.cnki.1004-8626.2024.06.003.