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.

The severity of freeway traffic accidents is collectively influenced by multiple factors, among which short-term traffic flow characteristics immediately preceding the incident play a particularly critical role. To systematically analyze the impact of short-term traffic flow states on injury severity, this study constructed a random parameter logit model accounting for mean heterogeneity, utilizing historical traffic accident data, ETC gantry transaction records, and meteorological data from Guangdong Province’s South 2nd Ring Expressway, Jiguang Expressway, and Western Coastal Expressway (2021—2022). The model was developed to investigate heterogeneous characteristics of accident contributing factors. A total of 29 potential variables were identified across four domains: road characteristics, environmental conditions, traffic flow features, and crash attributes. Three discrete model specifications were employed to model injury severity: a standard multinomial logit model, a random parameter logit model, and a random parameter logit model that accounts for mean heterogeneity. Comparative analysis of model goodness-of-fit using pseudo-R², akaike information criterion (AIC), and Bayesian information criterion (BIC) demonstrated that the random parameter logit model accounting for mean heterogeneity exhibits superior performance in goodness-of-fit. This specification more accurately captures the heterogeneous characteristics of accident contributing factors. Further analysis based on the average elasticity of variables reveals that, at the 99% confidence level, 22 parameters significantly affect injury severity. Specifically, features such as six-lane bidirectional roads and improved visibility significantly reduce injury severity, whereas longer road rescue handling time, higher average speed and proportion of large trucks, and greater speed differentials between large and small vehicles are associated with increased injury severity. The findings of this study offer valuable insights for improving freeway accident prevention and management strategies.

In order to study the characteristics and risks of lane-changing behaviors of large vehicles, trajectory data of large vehicles in the expressway merging area was collected based on drone shooting and image recognition technology, and lane-changing characteristics and spatiotemporal risks of large vehicles were analyzed. The results indicate that the average value of the lane change duration of large vehicles is 5.28 s, the average value of the first half time is 2.60 s, the average value of the last half time is 2.68 s, and the average value of longitudinal lane change travel distance is 78.12 meters. They all obey Weibull Distribution, and are significantly related to lane-changing speed. The lane change duration and the first half time are significantly related to the distance between the large vehicle and the vehicle in front in the original lane and the distance between the large vehicle and the vehicle in front in the target lane. The first half time is also significantly related to the distance along the lane line. 75.40% of large vehicles start to change lanes within 100 meters before the bottleneck section of the merging area, and the occurrence of lane-changing behavior spreads from the outer lane to the inner lane in turn. The ave-rage distance and relative speed between the large vehicle and the vehicle in front in the original lane are the smallest, which are 22.91 meters and-0.90 m/s, respectively. If the lane change gap is small, the driver will be more inclined to change lane when the lane change gap shrinks slowly or continuously expands. The large vehicle has the highest risk of collision with the vehicle in front in the original lane, and approximately 15.32% of large vehicles change lanes when they are in an unsafe state with the vehicles in front.

Mountainous freeways pose a higher risk for truck accidents due to their complex terrain, variable weather conditions, and constrained road infrastructure. To investigate the factors influencing the severity of truck accidents on mountainous highways and provide a scientific basis for proactive accident prevention and precise traffic safety management, this study employs machine learning methods to construct and analyze classification models for predicting accident severity. A total of 34 features, including collision type, vehicle type, pavement structure, horizontal alignment, vertical alignment, roadside protection measures, road surface conditions, season, and accident time, were selected as input variables. Accident severity, categorized into minor injury and severe injury, was used as the binary output variable. Three machine learning models were developed: Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM). To evaluate the classification performance of these models, accuracy, precision, recall, and F₁-score were used as assessment metrics. Furthermore, to gain deeper insights into the decision-making mechanisms of each model and identify key influencing factors, the study applied the SHapley Additive exPlanations (SHAP) method to interpret the model predictions and quantify the contribution of each input variable to accident severity. The results indicate that the RF model outperforms the DT and SVM models, demonstrating superior performance in terms of accuracy, precision, recall, and F₁-score. SHAP analysis further identifies critical factors influencing the severity of truck accidents on mountainous highways, including rollover, absence of gradient, cement pavement, curves, frontal collisions, accident time (19:00-06:59), and lack of roadside protective measures.

In order to improve the safety level of non-motor vehicle traffic at signalized intersections, based on the survey data of signalized intersections with overlapping phase control in Guangzhou, this study analyzed the influencing factors of non-motor vehicle crossing behavior based on the C5.0 decision tree algorithm. Considering the influence of different periods on the crossing behavior of non-motor vehicles in the signal cycle, the study divided a complete signal cycle into four risk periods according to the risk conflict of non-motorized vehicles crossing the street, namely, the opposite green light risk period, the same direction green light safety period, the same direction green light risk period and the vertical direction risk period. And it divided the crossing behavior into three categories according to the waiting selection of non-motor vehicles at the intersection and whether or not to run red-light, namely, risky, opportunistic and law-abiding. It studied the influencing factors of the three types of crossing behavior by constructing a C5.0 decision tree model and analyzed and evaluated the classification effect of the model. The results show that the overall accuracy of the model classification results is greater than 83.04%, the AUC is greater than 0.880, and the model prediction accuracy is good. The crossing behavior of non-motorized vehicles at signalized intersections with overlapping phase control is mainly significantly related to the traffic environment, while the factors related to the rider’s behavior are less significant. The arrival risk period, non-motor vehicle signal light facilities, conflicting motor traffic flow, number of lanes and crossing risk have significant impacts on the occurrence of risk-taking crossing behavior, among which the arrival risk period is the most important influencing factor. The number of lanes, red-light time and arrival risk period have significant impacts on the occurrence of opportunistic crossing behavior, among which the number of lanes is the most important influencing factor. The conflicting motor traffic flow flow, signal period, number of lanes, crossing area and crossing risk have significant impacts on the occurrence of law-obeying crossing behavior, among which the conflicting motor traffic flow is the most important influencing factor.

With the increasing demand for container transportation and the widespread application of new information technologies, the automation of container terminal operations has become the main development trend in domestic and international ports. It can not only effectively improve the efficiency and safety of terminal operations, but also significantly reduce the demand for human resources and the operational costs. The horizontal transportation system is an essential part of the container terminal handling system and an important link enabling the highly efficient container transportation between the quayside and the storage yard, so its operational reliability and the reasonableness of the scheduling directly affect the operational efficiency of the automated container handling system. The mostly used horizontal transportation equipment in container terminals is the automated guided vehicles (AGVs), which is responsible for horizontal transportation from the front quay crane to the rear yard in automated container terminals. In actual operation process, conflicts and congestion is inevitable when multiple AGVs operate simultaneously. On this basis, this paper used conflict-based search (CBS) to solve the conflict problem arising from the cooperative operation of multi-AGVs at the terminal. The upper layer algorithm searched for conflicts among AGVs, while the lower layer algorithm used the A* algorithm for path planning of AGVs. A load factor was introduced into the heuristic function of the A* algorithm in order to avoid congestion in the path planning and achieve load balancing on terminal roads. Further, a sliding time window conflict resolution (STWCR) based on CBS was adopted to improve computational efficiency for multiple AGVs path planning in the continuous operation scenario of multiple task points at the terminal. Simulation experiments verified that the proposed algorithm in this paper can effectively solve the conflict problem of multiple AGVs path planning at the terminal, while balancing the road network load, alleviating local road congestion, and improving the utilization of road resources. The research results of this paper provide a reference for the optimization of the horizontal transportation system in automated container terminals.

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.