Abstract
In view of the lack of consideration of environmental protection performance in the traditional path optimization model, a path optimization model for urban traffic networks from the perspective of environmental pollution protection is proposed. Firstly, the urban real-time traffic condition is expressed by the road traffic state index, and an integer programming model is established to optimize the route with the goal of low carbon and shortest distribution time. Then, a hybrid particle swarm optimization algorithm combined with adaptive disturbance mechanism based on variable neighborhood descent is designed, which can better carry out adaptive disturbance according to the situation that the population falls into local extreme value, and the 2-opt local search method is introduced to improve the quality of solution. Finally, the improved particle swarm optimization algorithm is used to solve the two-objective model to obtain the Pareto front solution set, that is, the path scheme under real-time traffic conditions. The experimental demonstration of the proposed model based on two application scenarios shows that its distribution cost, distribution time, and carbon emission are 1975 yuan, 27 h, and 213 kg, respectively, which are better than other comparison models and have high application value.
Highlights
With the increasingly prominent ecological and environmental protection issues and the implementation of related policies, the carbon emission problem in the urban transportation process has gradually attracted people’s attention [1]. e carbon emissions generated during the driving of a vehicle are affected by factors such as vehicle type, driving time, and speed
From the perspective of environmental pollution protection, green transportation needs to consider the impact of logistics activities on the environment. e main goal is to reduce energy consumption and pollutant emissions [7], the carbon emissions of vehicles. e road traffic index can be used to get the speed of the vehicle and calculate the delivery time and carbon emissions Journal of Advanced Transportation based on this
Model Building. e low-carbon vehicle routing optimization problem considering the urban transportation network can be described as follows: in a distribution center, a group of homogeneous fleets are responsible for the distribution tasks of multiple customers; the maximum load of the vehicle is W, and the demand of customers is Q; the speed at which a vehicle travels varies with traffic conditions, which in turn affects the travel time of the vehicle and the amount of carbon emissions produced; and to solve the vehicle path with the shortest time and the lowest carbon emissions as the goal [19, 20]
Summary
With the increasingly prominent ecological and environmental protection issues and the implementation of related policies, the carbon emission problem in the urban transportation process has gradually attracted people’s attention [1]. e carbon emissions generated during the driving of a vehicle are affected by factors such as vehicle type, driving time, and speed. Reference [16] proposed a vehicle path planning scheme that considers fuel consumption and carbon emissions based on the dynamic traffic network structure. The traditional path planning Dijkstra algorithm is improved, and the vehicle fuel consumption and emission measurement model are combined to realize the vehicle green path planning It did not consider the problems of increased fuel consumption and increased carbon emissions under traffic congestion. In view of the lack of consideration of traffic conditions and low environmental protection in most path planning models, a path optimization model for urban transportation networks from the perspective of environmental pollution protection is proposed. Combining the two goals the lowest carbon emissions and the shortest delivery time, a route optimization model for urban transportation networks is designed. (2) In order to prevent particle swarm optimization (PSO) from falling into the local optimum and losing the best searched solution due to disturbance, the proposed model optimizes PSO, combining the adaptive perturbation mechanism with variable neighborhood descending search as the main body, adopting adaptive neighborhood selection strategy, and applying a variable number of cycles in each neighborhood search, so as to improve the detection ability and search of the solution space efficiency
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