Research on Driving Conditions Based on Principal Component and K-means Clustering Optimization

In order to overcome the problems of traditional K-means algorithm being sensitive to the initial cluster centers and easily affected by noise points, this study proposes an enhanced K-means hybrid clustering algorithm that integrates improved principal component analysis and density optimization. By combining the distance optimization strategy with the density assessment mechanism, a data density evaluation model based on spatial distribution characteristics was established. The algorithm prioritizes data samples with large spacing in high-density areas as the initial cluster center candidate set. It realizes intelligent filtering of abnormal data points while improving the clustering quality, and selects characteristic parameters with higher principal component contribution rates to reconstruct driving conditions, and finally completes the fuel consumption characteristics verification. Experimental data show that the driving conditions constructed by this method have only a 1.17% statistical difference in the speed-acceleration joint probability distribution, and the relative error mean of key characteristic parameters remains at a low level. The research confirms that the constructed driving conditions are statistically significantly consistent with the actual road operation characteristics and can accurately characterize the essential characteristics of traffic flow in a specific area.