An Intelligent Optimization Clustering Algorithm for Driver Risk Tendency Recognition in Intelligent Transportation Systems

Driving behavior assessment is of great significance in intelligent transportation systems to improve road safety and traffic efficiency. This paper proposes a novel weighted fuzzy C-means (FCM) clustering algorithm, called GSA-WFCM, which combines a genetic algorithm and a simulated annealing algorithm to optimize the evaluation process of driving behavior. GSA-WFCM first uses a genetic algorithm to optimize the clustering center to ensure the diversity and effectiveness of the initial selection; then, it uses a simulated annealing algorithm to avoid local optimal solutions, thereby improving the global nature of the clustering results. In order to enhance the robustness and accuracy of clustering, the algorithm introduces a point density weighting mechanism to improve the algorithm’s adaptability to data features of different dimensions. By integrating the advantages of genetic algorithms and simulated annealing algorithms, the algorithm achieves intelligent initialization, optimizes the initial clustering center, and significantly improves the convergence speed and stability of the algorithm. Experimental results on multiple data sets show that GSA-WFCM effectively overcomes the shortcomings of traditional FCM in initial cluster center selection, showing faster convergence and stronger robustness. Further verification on traffic data sets shows that the algorithm performs well in identifying different types of driving behaviors and can effectively classify cautious driving, normal driving and aggressive driving. The accuracy and stability of clustering are significantly improved, and the clustering performance reaches 0.81, providing a reliable tool for driving behavior analysis in intelligent transportation systems.