# Research on the Optimization Method of Bus Travel Safety Considering Drivers' Risk Characteristics
###### Abstract  
Bus drivers have an important role in ensuring road safety, as their driving circumstances fluctuate due to the combined influence of physiological, psychological, and environmental dynamics, which can cause complex and varied driving dangers. Quantifying and assessing drivers' risk characteristics under various scenarios, as well as finding the best fit with their work schedules, is critical for enhancing bus safety. This research first uses the entropy weight method, which is based on historical warning data, to examine the risk characteristics of bus drivers in various complicated contexts. It then creates an objective function targeted at minimizing the operational risk for a specific bus route. This function uses the quasi-Vogel approach and an improved simulated annealing algorithm to optimize and restructure the scheduling table, taking individual driver risk characteristics into account. Finally, the analysis is confirmed and examined with actual operational data from the Zhenjiang Bus Line 3. The data show that enhanced bus operations resulted in a 7.22% gain in overall safety and a 33.76% improvement in balancing levels. These insights provide valuable theoretical guidance as well as practical references for the safe operation and administration of public buses. urban traffic; public transport safety; bus driver; driving behavior risk; safety enhancement; simulated annealing algorithm +
Footnote †: journal: Medical  
he simplified the original problem to obtain a lower bound solution for practical issues. [PERSON] (2016) and his colleagues were the first to model the driver scheduling problem as a set covering problem (SCP), and based on practical experience, they reduced the computational scale of set partitioning to obtain an approximate solution to the problem. The set covering model has since been widely used. [PERSON] (2016) divided the personnel scheduling problem into multiple stages and solved each stage using the set covering model, achieving an approximate solution that was only 2% away from the optimal solution. Heuristic algorithms, based on the idea of probabilistic optimization, can efficiently search the feasible region of the objective function to obtain approximate solutions. [PERSON] (2016) and his colleagues were among the first to apply heuristic algorithms to the problem of bus driver scheduling, systematically introducing the characteristics and applicability of typical algorithms, such as simulated annealing, genetic algorithms, and greedy algorithms. [PERSON] (2017) and his team designed a driver scheduling method based on an improved genetic algorithm, demonstrating its better computational efficiency, robustness, and suitability for handling large-scale data compared to integer programming models. [PERSON] (2018) and his colleagues divided the driver scheduling problem into a set covering master problem and a resource-constrained shortest path subproblem and used a column generation heuristic algorithm to obtain integer feasible solutions. [PERSON] (2019) and his team designed a heuristic algorithm based on generating initial solutions for the problem of crossline scheduling of bus attendants and utilized a tabu search algorithm to find approximate solutions to the problem. The optimization problem of bus driver scheduling is characterized by large-scale computations and complex constraints, making it difficult to obtain an exact solution within a limited time. Considering that bus schedules are generally fixed, practical problems can be simplified, and heuristic algorithms can be designed to obtain approximate solutions. A reasonable bus driver scheduling plan is a critical factor in ensuring the normal operation of public transportation, and it is also an important aspect for reducing operating costs and enhancing resource utilization efficiency. [PERSON] (2017) et al. focused on the bus driver scheduling problem considering employee vehicles and the corresponding mathematical solution, with examples demonstrating the reduction in bus operating costs.