# Research on the Optimization Method of Bus Travel Safety Considering Drivers' Risk Characteristics
###### Abstract
[PERSON] (2018) emphasized the study of a single-line bus driver scheduling model considering mealtime windows, with optimization results improving driver resource utilization and reducing work delays. Some scholars have optimized bus operation safety and reliability by considering bus risk factors as constraints when solving the bus driver scheduling problem. [PERSON] (2017) et al. proposed a scheduling optimization model considering driver reliability, with simulation results showing that the model can reduce driver absenteeism and ensure service levels. [PERSON] (2018) et al. introduced an optimization model for regional bus driver scheduling that considers drivers' familiarity with routes, which can enhance the reliability of scheduling plans while reducing scheduling costs. [PERSON] (2018) et al. presented a reconstruction model for bus driver schedules considering the risk of fatigue driving, with simulation results indicating that the optimized scheduling plan can reduce the incidence of bus collisions by approximately 30%. Existing driver scheduling methods primarily focus on enhancing the economic benefits and service efficiency of bus operations or on solving algorithms for scheduling optimization problems, with less consideration given to bus safety and comfort. There is still a lack of safety-oriented driver optimization models and solution methods. How to propose optimization methods to improve driving safety based on drivers' risk characteristics in different situations has been rarely studied. This paper comprehensively utilizes multisource historical warning data to quantitatively analyze the characteristics of individual drivers. Meanwhile, for all drivers on a specific bus route, it optimizes the scheduling combination from the perspective of improving driving safety and verifies the effectiveness of the method. To help readers better understand the structure and content of this paper, Figure 1 illustrates the technical roadmap of the chapter content. Below is a brief overview of each chapter. Section 2 integrates historical warning data generated by bus drivers during their driving to quantitatively express the driving safety of bus drivers under different driving scenarios. Furthermore, considering the feasibility, fairness, rationality, and safety of driver scheduling, an optimization model for safety-oriented bus driver scheduling is established. Section 3 designs an initialization strategy based on the improved [PERSON]'s approximation method, ensuring that the initial results can better meet the requirements. The basic principle is that if the safety difference between a driver's optimal and second-optimal time periods is significant, this driver should be prioritized for scheduling to reduce the safety loss caused by not being able to assign them to a more favorable position. Conversely, if the safety difference between a driver's optimal and second-optimal time periods is minimal, assigning them to the second-optimal time period will result in very little safety loss. Sections 4 and 5 provide descriptions of the road data and driver data used, and through specific case studies, they verify that the safety optimization method proposed in this paper can effectively enhance the overall safety and risk balance of bus operations.