Font Size: a A A

Coal Freight Demand Forecast Of Dalian Port Based On BP Neural Network

Posted on:2024-07-14Degree:MasterType:Thesis
Country:ChinaCandidate:S D YangFull Text:PDF
GTID:2531307295454714Subject:Business Administration
Abstract/Summary:PDF Full Text Request
There is an inextricable link between port development and freight volume.The prediction of port coal freight volume can scientifically and accurately provide guidance for the development strategy of port enterprises,and can reflect the economic development status of the region.As a national strategic energy source,the supply of coal is related to the stable development of all aspects of our society.The coal freight problem in Dalian Port also affects the economic development of Dalian and the hinterland around the Bohai Sea.Forecasting the coal freight volume in Dalian Port can help to reasonably plan the development direction and strategy of coal freight in Dalian Port.Effective forecasting methods can also inject new vitality into the entire transportation system and ensure the normal and orderly operation of the transportation industry.Firstly,by consulting relevant literature at home and abroad,the research content and methods are proposed,and the research background and significance of the topic are introduced;Secondly,the grey correlation degree method and related concepts and theoretical basis of the BP neural network algorithm used in this article are introduced,laying a theoretical foundation for subsequent learning;The third is to introduce the coal freight business of Dalian Port and Dalian Port,analyze the factors affecting the coal freight of Dalian Port,and combine the actual situation;The fourth is to use the gray correlation method to select evaluation indicators for weight analysis based on influencing factors,and establish a coal freight forecasting model for Dalian Port,based on BP neural network;The fifth is to use the model in combination with other nonquantifiable factors to predict the coal transportation volume of Dalian Port in 2023,indicating the accuracy of the predicted value;Finally,the strategies and suggestions for developing coal freight transportation in Dalian Port should be put forward based on the characteristics of the port itself.Through weight analysis,it is shown that in the process of changes in coal freight volume at Dalian Port,the proportion of the secondary industry has the greatest impact,followed by the gross domestic product(GDP),followed by the regional GDP of Liaoning Province,the throughput of coal and products at major ports in China,electricity consumption in Liaoning Province,and the throughput of Dalian Port;Through prediction,the coal freight volume of Dalian Port in 2023 has a similar trend with previous annual data.The predicted value in January was the highest,reaching over 800000 tons.Afterwards,the freight volume showed a downward trend until reaching the lowest annual freight volume of 280000 tons in April.Throughout the year,the monthly coal freight volume has remained between 300000 and800000 tons,with an estimated annual freight volume of 6.02 million tons.In addition,considering factors such as the coal transportation market,epidemic situation and policies,port hardware facilities and operational capabilities,and competition and cooperation between ports,the actual freight volume may be lower than the predicted value,with an estimated 5.7 million tons;Finally,in response to the prediction that the increase in freight volume is not significant,this article proposes six development suggestions: changing business ideas and innovating development concepts;Improve logistics services and seize market share;Strengthen cost control to achieve cost reduction and efficiency increase;Establishing an information platform and improving port efficiency;Strengthen talent reserves and attach importance to talent cultivation;Strengthen corporate publicity and leverage brand effectiveness.
Keywords/Search Tags:Grey correlation degree, BP neural network, Prediction of coal freight volume, Port of Dalian
PDF Full Text Request
Related items