In order to explore the as sociation characteristics between influencing factors of accidents from existing traffic data and provide precise and fine-grained decision support information for highway operation and management departments,a constrained improved Apriori algorithm was established to mine the association rules affecting highway traffic accidents by considering four dimensions:driver,environment,road,and vehicle.On the basis of the traditional Apriori algorithm,the improvement of antecedent and consequent constraints of the rule may increase the accuracy and efficiency of mining association rules.Based on the analysis of 3 178 highway traffic accident data,it is shown that the improved Apriori algorithm reduces the number of invalid association rules by accurately mining the correlation between potential factors and accident levels and improves the accuracy of association rules and mining efficiency.There is a strong correlation between driver gender,age,lighting intensity,vehicle type and the severity of highway traffic accidents.Wet and slippery road surfaces can escalate traffic accidents into general accidents.The lighting conditions under the dark night are the main factors that escalate mi nor accidents into general and serious accidents.