Lateral driving behavior analysis is the foundation of freeway cross-section design and the focus of road safety research. However, the factors that influence vehicle lateral driving behavior have not been clearly explained. The dataset of the natural driving trajectory of freeways is used in this study to analyze vehicle lateral driving behavior and trajectory characteristics. As vehicle trajectory characteristic indicators, parameters such as preferred trajectory deviation and standard deviation are extracted. The effects of lane position, speed, road safety facilities, and vehicle types on freeway trajectory behavior are investigated. The results show that lane width and lane position significantly impact vehicle trajectory distribution. As driving speed increases, the lateral distance between vehicles in the inner lane and the guardrail tends to increase. In contrast, vehicles in the outside lane will stay away from the road edge line, and vehicles in the middle lane will stay away from the right lane dividing line when the speed increases. Statistical analysis shows that the preferred trajectory distribution of the same vehicle type in different lane positions is significantly different among groups (Cohen’s d > 0.7). In the same lane, the lateral position characteristics of the center of mass of different vehicle types are basically the same (Cohen’s d < 0.35). This work aims to explain what variables cause trajectory deviation behaviors and how to design traffic safety facilities (guardrail and shoulder) and lane width to accommodate various vehicle types and design speeds.

The lateral oscillations of vehicle trajectories are a significant cause of collisions. There is a dearth of research, however, on the oscillatory behaviors of vehicles driving on straight sections of freeways. This study aimed to investigate the effects of vehicle type, lane position, and speed on oscillation behavior and to propose quantitative indicators to explain lateral oscillation characteristics. Based on these characteristics, a more appropriate lane width can be determined. First, the k-means algorithm was performed to cluster the vehicles into three categories: passenger cars, medium-large cars, and extra-large trucks. Then, statistical methods such as analysis of variance (ANOVA) and regression analysis were employed to elaborate on the speed distribution, lateral amplitude (LA), and distance traveled within the oscillation cycle (DTOC) for various vehicle types. The results show that different types of vehicles have different lateral oscillation tendencies. The LA and DTOC for passenger cars are generally more extensive than for medium-large cars and extra-large trucks, and their oscillation patterns are the most complicated. The vehicle trajectory oscillation pattern varies significantly for different lane positions and speeds, but speed is the dominant influencing factor. The naturalistic driving dataset from German freeways served as the foundation for this study. These results can assist road engineers in better understanding the behavioral characteristics of vehicle trajectory oscillations and designing safer freeways.

Lane width is crucial for traffic safety management. Previous studies have shown that inappropriate width will lead to an increase in accident probability and unsafe driving behaviors. However, there have been limited studies on determination method of special lane width for different models, especially for passenger cars. To tackle this problem, the lateral driving behavior characteristics of the expressway need to be clarified. This work aims to obtain the appropriate lane width according to the vehicles' width and lateral position characteristics. In this study, the lane lateral residual width (the distance between the vehicle body's contour and the lane marking on the same side) and the lateral safety margin are extracted as characterization indices of vehicle lateral characteristics from natural driving trajectory data of expressways. The effect of vehicle type, driving speed, and lane position on the expressway's trajectory behavior is investigated. The results show that the utilization rate of lane width is higher in the outer lane compared to the inner lane. As driving speed increases, vehicles in the inner and middle lanes exhibit shy away effect, moving away from obstacles. Substantial variations exist in the lateral width of lanes among different vehicle types. When driving in the same lane, passenger cars' lane lateral residual width that is 0.4–0.5 m wider than that of heavy vehicles. The recommended lane width for the safe operation of trucks on expressways is 3.75 m. After a comprehensive analysis of vehicle width, trajectory oscillation, and lateral safety margin, this study proposes a minimum lane width of 3.25 m and a recommended general width of 3.5 m for a passenger car‐only expressway. This study holds great significance in enhancing road safety and provides a valuable theoretical foundation for the design of lane width in a passenger car‐only expressway.

Highway guardrails are critical safety infrastructure along roadways, designed to redirect vehicles back into their lanes and facilitate a gradual deceleration to a complete stop. Traditional highway steel guardrails exhibit significant limitations, including inadequate energy absorption, susceptibility to corrosion, and an increased risk of vehicles leaving the roadway during severe collisions. Furthermore, the production and transportation of these guardrails contribute to substantial carbon emissions and environmental pollution. This study presents an optimization of the cross-sectional shape of the conventional corrugated beam guardrail, proposing a lightweight structure that incorporates high yield strength steel plate HR700F to enhance energy absorption capacity. The safety performance of the proposed guardrail is rigorously assessed through finite element numerical simulations and full-scale collision tests with real vehicles. Key performance indicators—such as the maximum dynamic lateral deflection of the guardrail, occupant impact velocity, and acceleration—are utilized to evaluate the energy absorption and protective efficacy of the structure. Results indicate that the optimized guardrail not only meets SB-level safety standards but also demonstrates superior anti-collision performance and effective energy absorption and buffering characteristics. The proposed design achieves a reduction in beam plate thickness by 1.3 mm, resulting in a lightweight structure with a weight reduction of up to 44%, thereby supporting the advancement of low-carbon, environmentally sustainable transportation solutions.

A mature theoretical model is an integral component of pavement management systems. Typically, such models encompass pavement performance prediction, cost–benefit analysis of maintenance measures, and decision-making frameworks for maintenance strategies. This paper primarily investigates the economic analysis and decision planning aspects of pavement maintenance measures. Initially, considering greenhouse gas emissions, a comprehensive cost evaluation is conducted for three typical maintenance measures (microsurfacing, slurry sealing, and milling with overlay), encompassing carbon emission costs, delay costs, and construction costs, to establish comprehensive cost indicators for subsequent decision optimization models. Subsequently, an optimization algorithm with Clip pruning is introduced for near-end strategy optimization, along with the development of suitable evaluation functions for maintenance measure effectiveness, thereby establishing a reinforcement learning-based optimization model for pavement maintenance decisions. Regarding the model results, the costs of microsurfacing, 4 cm AC slurry seal, 4 cm SMA slurry seal, and 4 cm milling with overlay are 10 ¥(m2), 50 ¥(m2), 66 ¥(m2), and 60 ¥(m2), respectively. Furthermore, by comparing the reinforcement learning decision model developed in this study with the Q-learning algorithm, the model demonstrates a 60% improvement in convergence speed and a 20% increase in average reward.