Route optimization for hazardous materials transportation vehicles based on BFA-ACO under carbon tax policy

Excessive emission of greenhouse gases leads to the increasing greenhouse effect, adversely affecting the global climate. Carbon dioxide is the dominant part of greenhouse gases; reducing its emission is the most important way to solve the climate problem. Aiming at the characteristics of the dangerous goods transportation market and the development of the carbon tax policy, the carbon tax cost and the cost of dangerous goods transportation are introduced, and the characteristics of the dangerous goods transportation and the road traffic network and the road traffic in each period are fully analyzed. The optimization model of the path of the hazardous goods vehicles is established with the optimization objective of the lowest total cost. Then, by analyzing the advantages and disadvantages of bacterial foraging algorithm (BFA) and ant colony algorithm (ACO), the hybrid BFA-ACO algorithm is established by combining the two, and the replication and convergence operations of bacterial foraging algorithm are introduced into the ACO algorithm to improve the convergence speed and global convergence ability of the algorithm. The hybrid algorithm is then used to optimize and solve the path optimization model of hazardous materials vehicles and compared with the classical algorithms Genetic Algorithm (GA) and ACO for solving the path of dangerous materials vehicles. A comparison of the optimization results reveals that optimizing the model by bacterial foraging-ACO algorithm is better than optimizing the model by a single algorithm.