Risk assessment and hybrid algorithm transportation path optimization model for road transport of dangerous goods

The current risk assessment methods for dangerous goods roads have the problem of being unable to cope with complex road conditions and the influence of multiple factors. This study extends 9 tertiary indicators from three secondary indicators: personnel factors, vehicle factors, and road factors, to evaluate the transportation risk of dangerous goods. After calculating the weights of each indicator, this study improves the parameters of the particle swarm algorithm using the aggregation and foraging behavior of artificial fish, and uses the improved algorithm to solve the optimal solution for the cost of dangerous goods road transportation. After experimental verification, the improved hybrid algorithm has optimized the path transportation time by 13.9 % compared to a single algorithm model. The total risk of simultaneously improving the algorithm was 0.8863, and the total transportation distance was 861 km, both lower than other algorithms. The comprehensive analysis shows that the established model is reasonable, and the designed improved hybrid algorithm can improve the efficiency of the transportation industry, while also contributing to the improvement of the current cost status of dangerous goods road transportation. • Robust transportation risk assessment model developed using 9 tertiary indicators from personnel, vehicle, and road factors. • Particle swarm algorithm with artificial fish foraging behavior to optimize dangerous goods road transportation costs. • Experimental validation shows 13.9 % efficiency improvement in path transportation time with the improved hybrid algorithm. • Significant reductions in total risk (0.8863) and transportation distance (861 km) demonstrate model's efficiency.