Research on the Causes of Road Traffic Accidents Based on Cause and Effect Diagrams

Abstract

To reduce the probability of traffic accidents and enhance the safety of traffic travel, this study aims to analyze the strength of the causal effect of the causes of traffic accidents. This study collected some road and social attribute feature data in California, used the Bayesian network to build a dependency graph, used the causal science to evaluate the causal effect between each attribute, then marked the weak correlation between attributes and ranked the importance. The final conclusion: Based on this data, the total local population has the strongest causal effect on causing traffic accidents, the Spanish account for the largest number of the local population, and the Principal Arterial added under MAP-21 has an impact on the average annual daily traffic (AADT) and annual average daily traffic of vehicles with 2 axles and 6 wheels and above (AADTT).