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Correlation Analysis of Influencing Factors of Autonomous Vehicle Accidents Based on Improved Apriori Algorithm

Tao Wang et al.

Journal of Advanced Transportation2026https://doi.org/10.1155/atr/7024232article
ABDC B
Weight
0.50

Abstract

The purpose of this study was to explore the risk factors for autonomous vehicle (AV) crashes and their interdependencies. A total of 659 AV crash data were collected between 2018 and July 2024 from AV crash reports published by the California Department of Motor Vehicles. Characteristics such as crash location and time, driving patterns, vehicle motion, crash type and vehicle damage, and traffic conditions were considered as potential risk factors in the study. Considering the multilevel and multidimensional nature of the crash data, the study adopted an association rule mining (ARM) approach to identify the risk factors that frequently occur in AV crashes. By improving the Apriori algorithm, based on the traditional Apriori algorithm, the association rule judgment index is added, and the accuracy and mining efficiency of association rules are improved. The results show that rear‐end collisions in autonomous driving mode are more serious, especially when stopping at intersections, while the rear vehicle chooses to continue driving or slow down. Accident risk is higher at night, with on‐street parking and 2‐lane conditions in both directions. The occurrence of no‐damage and minor crashes is more likely to be influenced by roadway characteristics and traffic conditions, and nonmotorized lanes, on‐street parking, and median strips on the roadway play a key role in reducing crash damage. The results of the study inform relevant policies to improve road safety and the efficiency of AVs to enhance the overall safety of road traffic.

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https://doi.org/https://doi.org/10.1155/atr/7024232

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@article{tao2026,
  title        = {{Correlation Analysis of Influencing Factors of Autonomous Vehicle Accidents Based on Improved Apriori Algorithm}},
  author       = {Tao Wang et al.},
  journal      = {Journal of Advanced Transportation},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1155/atr/7024232},
}

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Correlation Analysis of Influencing Factors of Autonomous Vehicle Accidents Based on Improved Apriori Algorithm

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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