With the development of smart cities and transport,the continuous improvement of mobile Internet and smart transport infrastructure and data,a new transport operation method in which users order transport services on their mobile phones-dynamic transport has become an important exploration direction for public transport development in several urban cities.However,studies in the field of modeling and algorithms for the dynamic transport problem are limited.Therefore,a dynamic transport problem model and a discrete hierarchical memory Particle Swarm Optimization(PSO)algorithm for dynamic public transport are proposed.This study mainly involves providing the objective function and constraint conditions for dynamic transport problems;providing the form of the solution to the dynamic transport problem and defining the editing distance of the solution;proposing an algorithm for generating high-quality initial solutions for the PSO algorithm using data-driven precomputed path sets;providing the calculation method of particle mutation probability and adaptive convergence coefficient in the PSO algorithm based on the edit distance of the solution;and proposing a hierarchical solution method for PSO in which lower-level particles can be reused and inherited,thereby reducing the performance loss caused by copying and re-initialization within a single time slice and between time slices.Based on a real scene and historical data from Caijiagang Street in Beibei District,Chongqing,a simulation environment is established for the experiments.Experiments have demonstrated that compared to non-hierarchical PSO algorithms,the hierarchical PSO algorithm can reduce computational time by more than 80％through reuse and inheritance,and adaptive parameters and mutation mechanisms can help algorithms converge to additional optimal solutions more stably than traditional public transport systems.Dynamic public transport can increase passenger order acceptance rate by 22％and save passenger travel time by 39.1％under the same capacity constraints.Moreover,the algorithm proposed in this study can meet the needs of transport operators for dynamic public transport scheduling within the area.Compared to non-hierarchical PSO algorithms,the algorithm proposed in this study reduced the calculation time by an average of 85.3％and improved the order acceptance rate by at least 12％while consuming only 80％of the mileage.
关键词
智慧交通/动态公交问题/电召问题/粒子群优化算法/预计算路径集/自适应变异
Key words
smart transport/dynamic public transport problem/Dial-a-Ride Problem(DARP)/Particle Swarm Optimization(PSO)algorithm/precomputed path set/adaptive mutation
维普
万方数据