At present, achieving efficient, sustainable, and safe transportation has led to increasing attention on driving behavior recognition and advancements in autonomous driving. Identifying diverse driving styles and corresponding types is crucial for providing targeted training and assistance to drivers, enhancing safety awareness, optimizing driving costs, and improving autonomous driving systems responses. However, current studies mainly focus on specific driving scenarios, such as free driving, car-following, and lane-changing, lacking a comprehensive and systematic framework to identify the diverse driving styles. This study proposes a novel, data-driven approach to driving-style recognition utilizing naturalistic driving data NGSIM. Specifically, the NGSIM dataset is employed to categorize car-following and lane-changing groups according to driving-state extraction conditions. Then, characteristic parameters that fully represent driving styles are optimized through correlation analysis and principal component analysis for dimensionality reduction. The K-means clustering algorithm is applied to categorize the car-following and lane-changing groups into three driving styles: conservative, moderate, and radical. Based on the clustering results, a comprehensive evaluation of the driving styles is conducted. Finally, a comparative evaluation of SVM, Random Forest, and KNN recognition indicates the superiority of the SVM algorithm and highlights the effectiveness of dimensionality reduction in optimizing characteristic parameters. The proposed method achieves over 97% accuracy in identifying car-following and lane-changing behaviors, confirming that the approach based on naturalistic driving data can effectively and intelligently recognize driving styles.

Signage functions as guidance and distribution assistance, directly affecting the operational efficiency of traffic in and around the comprehensive transportation hubs. Among the elements of signage, the visual guidance effect is the key factor affecting the information conveyance, which should be evaluated during the design and optimization process. This study conducted field investigations and developed panoramic videos for multiple transportation hubs in China and designed a survey accordingly. Human subjects were recruited to watch panoramic videos via virtual reality (VR) and respond to the surveys. The results show that the degree of visual attention of travelers significantly affects the evaluation results of guidance signage, with the influence being inversely proportional. Key factors affecting visual attention include accurate legibility, obstruction and defacement rates, informativeness, and whether signage is set up in a hierarchical manner. In unfamiliar environments, travelers focus on the overall context and closely observe the interaction between directional signs and their surroundings. The prominence and visibility of signage are influenced by interactions within the spatial environment. Notably, simple and clear signs are more likely to attract travelers’ attention, and their directional information is more easily comprehended. Moreover, when the destination is clearly defined, visual attention significantly directs pedestrians’ wayfinding behavior.