To provide a deep insight into the significant factors that affect the severity of freeway crashes, this study took the crash data from the Dongguan section of the Guang-Shen Yanjiang Freeway in China from 2014 to 2019 as the research object. Crash severity levels were divided into three categories (i. e., no injury crash, minor injury crash, severe injury or fatality crash). Accounting for spatial correlation among adjacent crashes via conditional autoregressive priors, spatial generalized ordered Probit models with different correlation distance thresholds were developed, where the crash severity was used as the dependent variable and 13 potential influencing factors were used as independent variables. The research results show that there is significant spatial correlation among crashes; the spatial generalized ordered Probit models outperform the generalized ordered Probit model and multinomial Logit model; and the spatial model with 250-meters correlation distance threshold achieves the best performance. The results of model parameter estimation reveal that the type and registered province of vehicles, the time of crash occurrence, curvature of crash location, bridge section, and crash type have significant effects on freeway crash severity. The marginal effects of these factors indicate that: as compared with crashes with cars involved only, the involvement of bus, truck and other type vehicles will increase the probability of severe injury or fatality by 3.27%, 1.53%, and 4.11%, respectively; the involvement of vehicles from other provinces will increase the probability of severe injury or fatality by 1.02%; as compared with those occurring on weekend, spring, and bridge, crashes occurring on weekdays, summer, and non-bridge sections would increase the probability of severe injury or fatality by 0.87%, 2.38%, and 0.08%, respectively; the probability of heavy casualties caused by bicycle accidents is 1.64%lower than that of multi-vehicle accidents; the probability of severe injury or fatality will decrease by 1.54% for per 1 km^-1 increase in horizontal curvature of crash location.

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.

Accident data collected from 2014 to 2019 for the Dongguan section of the Guangshen Yanjiang Expressway in China were utilized to investigate the key factors influencing the severity of road property damage in highway traffic accidents. The spatial correlation among adjacent accidents was addressed using a spatial generalized ordered Probit model, which employed varying association distance thresholds. An XGBoost machine learning algorithm was developed to estimate the model parameters, and the SHAP (SHapley Additive exPlanations) method was employed to elucidate the model outputs. The results show that significant spatial correlations are present within the accident data. The spatial generalized ordered Probit model demonstrated superior performance compared to the conventional generalized ordered Probit model, with the model based on a 200 m association distance threshold yielding the best results. The SHAP method significantly enhanced the interpretability of the XGBoost machine learning model. Parameter estimation revealed that variables such as single-vehicle accidents, passenger cars, lorries, heavy tractors, nighttime occurrences, early morning periods, cloudy conditions, rainy conditions, and bridge locations were significantly associated with the severity of road property damage resulting from traffic accidents.

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.