## 1 Introduction
In [PERSON] and [PERSON]'s study, a deep learning network model was developed based on vehicle-motion state data which was collected by installing sensors in 43 taxis to identify potential aggressive driving behaviors [12]. Based on the identification of driving behaviors, the researchers developed the concept of driving style. 'Driving style' is defined as the way in which a driving task is accomplished. Acceleration, smartphone data, and fuel consumption can all be used for the identification of driving style. It has been suggested that acceleration, deceleration, cornering, and lane changing can be used to identify driving style. In addition, based on data collected by smartphone sensors, the study concluded that driving styles can be characteristically divided into two categories: typical (nonaggressive) and aggressive [2]. Processingacceleration data through fuzzy logic models, one study proposed four categories of driving style: below normal, normal, aggressive, and very aggressive [13]. However, in most studies, driving styles are divided into three categories: aggressive, normal, and cautious. These three dominant categories have also been applied in this study to classify driving styles. A novel technique to robustly classify driving style is the support vector clustering approach, which attempts to differentiate the variations in an individual's driving pattern and provide an objective driver classification. Driving styles were classified as aggressive, normal, or defensive [14]. It is worth noting that most of the previous research has focused on studies related to passenger cars and their drivers. However, with the continuous development of logistics traffic, the number of professional logistics transport vehicles is increasing. At the same time, the number of traffic accidents related to logistics transport vehicles is increasing each year. Among them, heavy-duty trucks have become the main contributor to fatal vehicle collisions. According to the incomplete statistics available for China, heavy-duty trucks accounted for 32.04% of all types of vehicles involved in criminal traffic accidents between 2016 and 2019. Among all types of vehicles, heavy-duty trucks accounted for more vehicles in the problem categories of speeding, overloading, and unsafe vehicle conditions, accounting for nearly 30% of the total. The Federal Motor Carrier Safety Administration found that, for a traffic accident, on average, the death of a heavy-duty truck driver will lead to the death of six other people (other vehicle drivers, passengers, pedestrians, etc.). At the same time, potentially high-risk areas for traffic accidents cannot be ignored. Some areas are prone to traffic accidents because of their traffic and environmental characteristics. Collisions occurred more frequently at intersections than on other areas of the roadway [15]. When considering turning-off accidents, 42.6% of urban accidents occur at intersections, which means it is necessary to focus on the control of intersections to improve safety and avoid accidents. At signalized intersections, the signal phase also needs to be considered as it creates a dilemma zone for drivers. Right-angle crashes and rear-end crashes caused by the dilemma zone are important factors affecting traffic safety and the efficiency of intersections. At unsignalized intersections, the passing state of vehicles is determined by traffic flow and the driver's own behavioral characteristics [16]. Due to slow heavy-duty-truck acceleration and poor braking performance, the drivers of such vehicles are often unwilling to slow down or even stop when they pass through intersections and other areas so as to maintain a constant speed, creating potential safety hazards. Meanwhile, the abilities of truck drivers could become impaired during extensive periods of driving. Especially in China, truck drivers tend to overestimate their driving ability in emergency situations and may be subject to a greater accident risk due to overconfidence [17].