Study On Urban Bus Dispatching Optimization

With the rapid development of society and economy,traffic problems in many large and medium-sized cities in China,such as traffic road congestion and frequent road accidents,have become increasingly prominent.Giving priority to the development of public transportation is the mainstream strategy for solving urban traffic problems,while public transportation scheduling plan is the core part of urban public transportation operation management.Therefore,the research on the optimization of public transportation vehicle scheduling is the key to ease the pressure of urban traffic and enhance the attractiveness of public transportation.In this context,this paper analyzes the forms,classification and influencing factors of bus scheduling with reference to literature review and on-site investigation.A simulated annealing genetic algorithm is used to solve the problem of departure interval in bus scheduling.Firstly,this paper introduces the domestic and international present situation of urban bus scheduling and the related theory of urban bus scheduling.It points out the existing problems of public transportation scheduling and optimization methods.At the same time,this paper studies the algorithms of urban bus scheduling(genetic algorithm,simulated annealing algorithm and simulated annealing genetic algorithm)and summarizes their characteristics.The algorithm which is suitable for this model is selected: simulated annealing genetic algorithm.All of these provide the basic theoretical support for constructing vehicle scheduling optimization model,and also lay the foundation for the subsequent model solution.Secondly,the paper analyzes the existing problems of bus dispatching system and establishes a mathematical model of bus route dispatching optimization.Under the condition of established bus line network,the reasonable departure interval is determined and provides the evidence for formulating a bus dispatching plan with practical application value.In this paper,we use the minimum of passenger travel costs and bus company operating costs as the model objective function,the departure interval between adjacent vehicles as a decision variable,taking into account the actual operation of the vehicle full load rate,the maximum and minimum departure interval between adjacent trips,the economic benefits of the company,the degree of congestion on the bus and the constraints on the number of bus departures toestablish a portfolio optimization model.Selecting the hybrid algorithm: simulated annealing genetic algorithm,and through the analysis of key technologies to achieve algorithm optimization and model solution.Finally,taking a bus line in Nanjing city as an example,the bus line data is investigated.Then using MATLAB toolbox to simulate experiments with genetic algorithm and simulated annealing algorithm respectively,so as to solve the established optimization model,and determining the bus departure interval under different weights.The simulation results show that the improved simulated annealing genetic algorithm has a faster solution speed and a more accurate global optimal solution than the genetic algorithm.This not only effectively controls the cost of the bus company but also improves passengers' comfort,attracts more passengers to choose public transportation,increases social benefits,and improves passenger travel service quality.It has practical application value.