Expressway merging areas are characterized by frequent lane changes, complex driving environments, and intense traffic conflicts, making them high-risk zones for traffic accidents. Accurately understanding the relationship between vehicle operating status and traffic safety in these merging areas can provide a foundation for realtime accident risk prediction and the development of effective traffic safety control strategies. This study is based on vehicle trajectory data from the German ExiD dataset. By analyzing the changes in the relative positions of mainline outer-lane vehicles during the process from a vehicle entering the acceleration lane to merging into the mainline, the merging patterns in expressway merging areas were classified. To systematically describe the safety risks associated with merging, this study introduced the Time to Collision theory and developed a risk representation framework. This framework includes two levels of indicators: (1) a merging moment risk indicator based on two-dimensional TTC, which evaluates potential conflict at the moment of merging; and (2) a merging process risk indicator based on collision exposure time, which reflects the accumulated risk throughout the merging process. For model development, four machine learning algorithms—XGBoost, LightGBM, GBDT, and Random Forest—were used to build a classification model for merging risk. In addition, SHAP was applied to interpret the model and analyzed the key factors influencing merging risk. Experimental results show that the XGBoost-based risk identification model for expressway merging areas outperforms other models, achieving an overall accuracy of 95.52%. It also demonstrates superior performance in terms of accuracy, precision, recall, and F1-score. Furthermore, comparison among models indicates that incorporating merging duration and urgency significantly improves risk identification accuracy. SHAP analysis further reveals that merging risk is closely related to several factors, including the average and maximum speed differences with the leading vehicle on the mainline, the average distance to the leading vehicle, merging duration, the standard deviation of longitudinal acceleration during merging, and the speed of the merging vehicle.

In order to overcome the difficulty in estimating the indirect carbon emissions from new energy vehicles due to the energy consumption in a large-scale expressway network, this paper proposes a new carbon emission calculation method of new energy vehicles based on the toll data in expressway ETC network. Firstly, the traffic flow data of new energy vehicles on expressways were cleansed and processed. Then, according to the distribution of toll gantries, road sections were defined and segmented. On this basis, the energy consumption of different types of new energy vehicles was analyzed. Furthermore, a carbon emission quantification model of new energy vehicles in the operational stage was established. Finally, by taking Guangdong Province as an example, the carbon emissions of new energy vehicles on expressways were calculated, with its spatial distribution characteristics being also analyzed. The results show that (1) Category 1 pure electric passenger cars are the main source of carbon emissions from new energy vehicles on expressways, accounting for 74.20% of the total carbon emissions of new energy vehicles, followed by Category 1 pure electric trucks, whose carbon emissions account for 14.18% of the total, and Category 1 plug-in hybrid passenger cars contribute 11.62% of the total; (2) the cities in the Pearl River Delta urban agglomeration are the main contributors to high carbon emissions from new energy vehicles, and the less developed regions in eastern, western and northern Guangdong account for less than 13% of the total carbon emissions from new energy vehicles; (3) the road segments with high carbon emissions from new energy vehicles mainly locate in the transportation hubs and inner-city expressway rings of developed cities, and Guangzhou metropolitan area as well as Shenzhen metropolitan area as the center has a radiating effect on the expressway network of surrounding cities; and (4) in less developed areas, due to the low density of expressway network and relatively lagging infrastructure, the penetration rate of new energy vehicles is low, the corresponding carbon emissions from new energy vehicles are generally low.