Research On Optimization Of Bus Schedule Based On Hybrid Genetic Algorithm

With the rapid economic development in our country and the improvement of national living standard,the number of motor vehicles per capita gradually increased,which makes the urban traffic congestion situation more and more serious.In order to properly solve this problem,the state vigorously develop urban road public transport,the implementation of bus priority strategy.One of the key measures to implement the bus priority strategy is the effective use of existing vehicle resources,based on the actual flow of passengers on the line for the scheduling of bus vehicles,the development of a reasonable bus line schedule.A reasonable bus schedule not only improves the travel experience of passengers and reduces travel time,but also reduces the operating costs of enterprises.Based on the actual situation,this paper conducts an in-depth study on the research results of bus schedule optimization and intelligent algorithm optimization,and makes corresponding improvements according to the shortcomings of the model,gives full play to the advantages of hybrid intelligent algorithm,and applies it to the optimization of bus schedule.The main research contents of this paper include the following aspects.First,the theory related to bus departure schedules and current status of domestic and international is outlined,and the classification of bus schedules and the form of their development are studied in depth.The data obtained from the IC card swipe data of passengers’ bus trips are processed to obtain the data information needed for the study,and then mined to analyze the characteristics of these data.The number of boarding passengers at each station on the route obtained through statistical analysis of the data is used as the data base,and a probabilistic model is used to project the passenger flow situation at the route stations.Second,the current problems of bus departure time formulation are analyzed,and a bus schedule optimization model is established from the perspectives of both passenger interests and enterprise interests,with the optimization objective of minimizing the weighted sum of passenger stop waiting time cost and enterprise operation cost,including constraints such as vehicle full load ratio,departure interval and maximum number of departures.Third,MATLAB2021 Rb is used as the implementation platform,and Genetic Algorithm is used for solving the model.Considering the problem that the genetic algorithm is prone to fall into local optimal solutions,it is improved accordingly.A hybrid genetic simulated annealing algorithm with the introduction of tent mapping population initialization is proposed by introducing tent mapping population initialization and subsequently adding an appropriate domain-based local search mechanism to the framework of the genetic algorithm,and the algorithm is elaborated.Finally,taking a bus route in Shenyang City as an example,the data is analyzed and the optimal departure schedule of the line is calculated.The practicability of the model and the effectiveness of the optimization method are verified.