# Operational Risk Assessment of Engineering Vehicles Considering Driver Characteristics
## 1 Introduction  
Engineering vehicles have larger driving blind spots than ordinary private cars because of their construction and design features. Consequently, identifying and mitigating risks associated with engineering vehicles is inherently more challenging than with ordinary private cars. Engineering vehicles are distinguished by their extended transportation durations, high capacity, and drivers' intensive work schedules. Given the operational interests, these vehicles tend to acquire numerous violations in driving workloads, speeding, and overloading, which greatly affect driving safety. The Transportation Research Board of the U.S. Academy of Sciences has undertaken numerous international research endeavors to examine the causes of accidents involving engineering vehicles and their environmental impact, significantly contributing to transportation safety in the United States [1]. Driver behavioral characteristics are crucial for the safety of engineering vehicles. Driving behavior serves as a reliable indicator for evaluating driver alertness amidst various factors, such as extended driving periods and sleep deprivation [2]. The current research indicates that drivers' driving behavior can be evaluated in two ways: one is through drivers' self-assessment, and the other is by collecting drivers' driving data through on-road driving tests or driving simulators. The influence of drivers' subjective factors on risky driving behaviors and driving accidents has become a popular topic in recent years. These factors encompass demographic variables (e.g., gender, age, marital status, education, and mileage) [3, 4], drivers' safety attitudes (measured via the Driving Safety Attitude Scale), and personality traits [5] (e.g., altruism and anger; assessed using the BigFive Personality Inventory and Eckerson Personality Inventory). The most commonly used driving behavior questionnaire is the Manchester Driving Behavior Questionnaire developed by Reason [6]. This questionnaire is used to measure three types of abnormal driving behaviors: errors, negligence and mistakes, and illegal driving. Drivers of large engineering vehicles often engage in dangerous driving behaviors, such as speeding, fatigue driving, illegal overtaking, and lane jumping, to save time and increase efficiency. The incidence of traffic accidents, ranging from 80% to 90%, can be attributed to factors such as the generally low educational attainment among operators of large vehicles, inadequate safety awareness, and supervisors' limited risk awareness [7]. Research on the travel risks associated with engineering vehicles, partly informed by truck traffic accident data, investigated the factors influencing traffic accidents and proposed targeted prevention and improvement measures [8; 9]. In terms of driving safety risk assessment, scholars have successively proposed various risk assessment models from a single-factor perspective by using multifactor comprehensive evaluation methods that combine quantitative, qualitative, or quantitative-qualitative research. [PERSON] et al. [10] proposed a risk index model to estimate the risk in a multiplicative way. As a result, analyzing the impacts of different factors becomes possible. [PERSON] et al. [11] assessed road traffic risks based on an expert empirical method by comparing the relevant risk indicators in each country and combining the road safety risk indicators in the evaluation of the composite risk index to assess the road safety risk level. This approach enables the monitoring of changes in risks. [PERSON] et al. [12] introduced a distribution model to assess road traffic risk based on road conditions, weather conditions, and traffic conditions. [PERSON] et al. [13] verified the applicability and reliability of the model based on accident history data by analyzing traffic accidents on rural mountainous roads in Malaysia. [PERSON] et al. [14] explored the extent to which the speed limit values of minibuses and trucks affect traffic accidents.