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.

To improve work zone safety by replacing human flaggers, a new Automated Flagger Assistance Device (AFAD) developed by the Missouri Department of Transportation (MoDOT) was investigated through field and driving simulator studies. The MoDOT AFAD configuration, including STOP/SLOW paddles, Red/Yellow lights, and a changeable message sign, was incorporated into a truck-mounted attenuator. Driver behavior measures were collected to compare the effectiveness of the MoDOT AFAD, its alternative designs, and human flaggers. In the field study, the MoDOT AFAD was able to slow vehicle approach speeds and stop vehicles farther back than flaggers. In the driving simulator study, the MoDOT AFAD and its alternative designs significantly reduced average approach speeds and increased the distance between completely stopped vehicles and the AFAD/flagger. Surveys conducted after each study showed that drivers understood the AFAD well and preferred it over flaggers, especially the MoDOT AFAD configuration. Overall, this case study demonstrated that the AFAD has the potential to improve the safety of work zones and workers.

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.

Road hazardous material transportation plays a critical role in road traffic management. Due to the dangerous nature of the cargo, hazardous material transportation trucks (HMTTs) have different route selection and driving characteristics compared to traditional freight trucks. These differences lead to unique travel and emission patterns, which in turn affect traffic management strategies and emission control measures. However, existing research predominantly focuses on safety aspects related to individual vehicle behavior, with limited exploration of the broader travel and emission characteristics of HMTTs. To bridge this gap, this study develops a comprehensive framework for analyzing the travel patterns and emissions of HMTTs. The methodology begins by applying a Gaussian mixture distribution model to identify vehicle stop points, eliminating biases associated with subjective settings. Origin–destination (OD) pairs are then determined through stop time clustering, followed by the extraction of travel characteristics using non-negative matrix factorization. Emissions are subsequently calculated based on the identified trip data. The relationship between emissions and land use characteristics is further analyzed using geographically weighted regression (GWR). Crucially, this study leverages data from the BeiDou Satellite Navigation System, focusing on HMTTs operating within Shanghai. The processed data reveal three distinct travel modes of HMTTs, categorized by spatiotemporal patterns: Daytime—Surrounding cities, Early morning—In-city, and Midnight—Scattered. Moreover, unlike other road vehicles, HMTT emissions are heavily influenced by industrial and company-related points of interest (POIs). These findings highlight the significant role of BeiDou Satellite Navigation System data in optimizing HMTT management strategies to reduce emissions and improve overall safety.

The advent of transportation network companies (TNCs), also known as commercial ridesharing providers, has disrupted transportation systems leading to new facility designs. Dedicated pickup/dropoff areas can facilitate the future shift to autonomous vehicles. In this study, angled stalls and parallel curbside parking, along with signage and education, were investigated using pedestrian and wheelchair simulators. Human behavior was examined with regard to safety, efficiency, and accessibility. Microsimulation and Surrogate Safety Assessment Model were used to examine the effect of increased demand on design capacity and number of conflicts. In simulations, both stall and curbside designs performed comparably in crosswalk usage, but the stall design was safer when passengers waited longer for vehicle arrival. It is preferable to wait to approach the loading area until the TNC vehicle arrives. Stall design was more accessible but curbside design had greater efficiency because of shorter vehicle waiting time (−7.31 s [ p < 0.01]). Signage and education were found to be effective in increasing sidewalk usage, decreasing deviations from the crosswalk, and reducing vehicle waiting time. Post-simulator surveys showed similar ratings for safety (80.65%), with stall design scoring higher in accessibility (90% versus 52%) and curbside design slightly higher in efficiency (77% versus 68%). Participants reported almost equal preference for stall or curbside designs (48% versus 52%), and over 90% found the signage and education effective. Microsimulation indicated that stall design had higher capacity (500 vehicles per hour) than curbside design (350 vehicles per hour) within 180 linear feet, and fewer overall conflicts at 300 loadings.