Passenger Car Railway Transportation Demand Forecasting And Transportation Optimization

In recent years.the economy has continued to develop rapidly,people’s quality of life has been significantly improved and consumer demand has continued to expand.As an important means of transportation for people to travel,the market demand for passenger cars is also growing.The continuous expansion of passenger car consumption demand drives the progress of vehicle logistics industry.As one of the transportation methods of vehicle logistics,railway transportation has also achieved rapid development.In the process of development,the inherent attributes of railway transportation make it have the advantages of long distance,large quantity and strong reliability.Based on the advantages of railway and the particularity of vehicle logistics,railway transportation has gradually become the main mode of passenger car logistics.However,with the growing of railway transportion demand and the continuous improvement of railway transportation infrastructure,due to the lack of experience in overall planning for passenger car railway transportation organization,passenger car logistics gradually highlights the problems of long distribution path and high transportation cost in the process of transportation.In this context,how to scientifically and reasonably plan the passenger car railway transportation route based on the existing transportation resources is of great significance for shortening the railway transportation path and optimizing the transportation layout.Based on the above content,this thesis focuses on passenger car railway transportation demand forecasting and transportation optimization.Combined with the related theories of car logistics,transportation demand forecasting and transportation path optimization,this thesis constructs the passenger vehicle demand combination forecasting model and transportation path optimization model.The passenger car transportation demand forecast research mainly applied the combination model of SARIMA and Holt-Winters to predict and estimate the future trend of passenger car demand.In the process of forecasting,this thesis uses the prediction effectiveness index to assign the weight parameters of the combination model,which effectively improves the prediction effect of the combination model.On the basis of demand forecasting analysis,this thesis uses semi open transportation planning strategy to construct an optimization model with minimizing transportation path as the objective function.The preset constraints of the model mainly include the maximum load capacity of the train,the starting point and stopping point of the train,the times of transportation services in the regional distribution center and the distribution service of the vehicle distribution center.Then combined with the forecast content of passenger car demand,this thesis uses the constructed path model to optimize the transportation path in a continuous time period.Based on the content of model optimization and its constraints,this thesis designs an improved genetic algorithm to solve it.The improved genetic algorithm introduces greedy strategy on the basis of the traditional algorithm and greedy strategy effectively improves the optimality of the initial solution of the algorithm,which is conducive to the algorithm to find the global optimal solution.Finally,this thesis makes an empirical analysis of the demand forecasting model and the transportation route optimization model based on the actual demand of passenger cars and the relevant data of railway transportation.The empirical research shows that the combined forecasting method further improves the accuracy of demand prediction compared with the single forecasting method.The transportation route optimization model effectively shortens the distribution route and optimizes the railway transportation layout.Compared with the traditional algorithm,the results of the improved genetic algorithm are better and the transportation path is shorter.