Intra-trip driving style variability: influences of traffic conditions and parking durations using high-frequency vehicle data

Driving style varies not only among different drivers but also within the same driver under different traffic conditions. This study investigates the relationship between traffic conditions, parking durations, and driving style variability using high-frequency data collected from 36 drivers during 5 months of urban driving experiments. The driving data were segmented into trip segments, and representative driving behavior features were extracted and reduced using principal component analysis. Driving styles and traffic conditions were then identified through K-means clustering. Based on the clustering results, the distribution of driving styles under different traffic conditions was analyzed at both segment and trip levels. Furthermore, regression analysis was conducted to quantify the relationship between traffic conditions, parking durations, and driving styles. The results indicate that driving style aggressiveness is strongly associated with traffic conditions and parking durations, with more aggressive driving behaviors being more frequently observed under congested traffic conditions. These findings provide empirical insights into driving style variability under complex urban traffic environments.