The penetration of automated vehicles (AVs) is expected to gradually increase in the future. Consequently, adding dedicated lanes for AVs to existing roads has become an effective countermeasure to improve traffic efficiency and driving safety. Although the horizontal and vertical alignment are constrained by human-driven vehicles and difficult to adjust, the width of Dedicated lanes for AVs can be redesigned and optimized. However, there is currently a lack of industry standards and calculation basis for designing such lanes. Vehicle trajectory deviation is a crucial factor in determining lane width. This study focuses on complex horizontal curve combinations that significantly affect driving trajectories. Using the PreScan-Simulink simulation platform, it applied typical AV lateral and longitudinal motion control algorithms and considered three types of complex horizontal curve combinations: oval, convex, and C-shaped. It constructed simulation vehicle models and road scenarios for different vehicle types and analyzed the impact of these complex curve combinations on AV trajectory deviation, ultimately developing trajectory deviation models for various vehicle types. This study shows that, unlike in convex curves where the maximum trajectory deviation occurs at the gentle transition point (HH point), in oval and Cshaped curves, the maximum deviation occurs at the first transition curve point (HY1 point). The design speed is significantly correlated with the trajectory offsets of AV on each horizontal alignment combination design: the offsets of the feature points with the largest offsets on each design are about 9~16 cm for AV at 60~130 km/h; the magnitude of the trajectory offsets varies greatly with the change in design speed, and the offsets of the feature points with the largest offsets on each horizontal alignment combination design are about 13~23 cm for AV at 140~150 km/h. Finally, a polynomial regression model was established to describe the relationship between design speed and trajectory deviation. The R² of the model is greater than 0.95, so the model fit meets the prediction requirements. The research method and research results of this thesis can provide a reference basis for the calculation of dedicated lane width.

To throughly analyze the quantitative relationship between the equilibrium of horizontal and vertical alignment combination and road safety, aiming at the “horizontal curve (HC)+crest vertical curve(CVC)” (referred to as HC-CVC) alignment combination, this study collected the road alignment (a total of 477 km), the traffic data and accident data from 2011 to 2018 of four interstate roads in Washington, D. C. as training data and test data. According to the characteristics of horizontal and vertical alignment combination, the paper suggested to consider the dislocation value, the horizontal curve radius, the vertical curve radius, the length of horizontal curve and the length of vertical curve as variables to characterize the equilibrium of horizontal and vertical alignment combination. Three machine learning models, namely, Decision Trees, Random Forests and Extremely Randomized Trees, were applied for model training to analyze the influence of HC-CVC combination on the accident rate per 100000000 vehicle kilometers. The prediction and fitting accuracy of Random Forests is the highest among all models. What’s more, sensitivity analysis and numerical analysis based on Random Forests model show that: when the horizontal curve radius is greater than 2.8 km or the vertical curve radius is greater than 58 km, the increase of horizontal and vertical curve radius has little impact on the road safety. At the same time, this paper also studied the correlation between variables and accident and suggested the value range of the variables when the horizontal curve radius is small. The research conclusions can provide reference for the subsequent quantitative optimization design and safety improvement of horizontal and vertical alignment combination.

China’s “14th Five-Year Plan” places higher demands on green transportation development, with emissions from traffic operations being the primary source of carbon emissions in the transportation sector. To investigate the factors influencing carbon emissions of passenger cars on highway curved segments, this study conducted on-site driving tests using OBD-equipped vehicles to collect driving data from typical curved road segments in Guangdong Province, and obtains carbon emission data through the IPCC carbon emission accounting method. Relevant evaluation indicators influencing passenger car emissions were selected based on road alignment, and gray relational analysis was used to calculate the correlations between these indicators. The results indicate that among the geometric alignment elements of horizontal curve sections, indicators such as the proportion of transition curve length and transition curve parameters are significantly correlated with the segmental carbon emission rate. The radius of the circular curve is also significantly correlated within a specific range. For non-geometric factors, indicators such as the standard deviation and mean of acceleration show significant correlations with carbon emissions, and these two indicators are further associated with geometric factors like transition curve parameters and the proportion of transition curve length. Based on the results of the grey relational analysis, eight correlated indicators were selected, and a grey GM(1, N) model was developed to predict the total carbon emissions of passenger cars on horizontal curve sections. The prediction results show an average relative error of 5.10% compared to the actual values. The predictive performance of the model surpasses that of traditional multiple regression models, demonstrating outstanding performance and reliability in scenarios with limited data. The findings of this study can identify key design and operational parameters significantly influencing carbon emissions, providing a theoretical basis for the low-carbon optimization and management of highway horizontal curve sections.