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Railway Freight Volume Forecasting by Artificial Neural Network Based on Economic Cycle

Author: ChangBo
Tutor: ChenZhiYa
School: Central South University
Course: Transportation Planning and Management
Keywords: Rail freight Forecast Economic cycle Economic cycle stage parameters Artificial Neural Networks
CLC: U294.13
Type: Master's thesis
Year: 2010
Downloads: 142
Quote: 0
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Abstract


Rail transport is one of the most important mode of transportation in our comprehensive transportation system , has irreplaceable advantages and role in the long-distance transport of bulk cargo , made ??a significant contribution to the stable development of China 's rapid economic . The railway cargo forecasts is the basis for the development of railway development plan , is the premise of the transport business decisions , the scientific cargo forecasts on the development of railway development strategy , give full play to the railway transport capacity has great significance . However, rail transport as a complex dynamic system , its run by multiple factors , the most significant impact from the macro-economic environment . Traditional forecasting methods tend to ignore the impact of macro-economic environment , the insufficient presence of mutation error is large, weak anti-interference ability . Therefore , it is necessary to study the impact of macro-economic environment of the railway cargo forecasts corresponding solution measures to establish a more reasonable model railway cargo forecasts . Firstly, extensively reviewed the relevant literature at home and abroad on the basis of the analysis of the advantages and disadvantages of the method of influencing factors of railway freight as well as various existing railway cargo forecasts . Then , the thesis focuses on analysis of the impact of the economic cycle theory and the economic cycle on the railway cargo forecasts that take full account of the impact of the economic cycle , only mutation in order to reduce errors, improve the predicted anti-jamming capability . And proposed the use of artificial neural networks for railway cargo forecasts , the length of time for a complete economic cycle training samples . On the basis of the economic cycle theory and artificial neural network theory , the paper presents the concept of the parameters of the economic cycle stage , and the establishment of a railway freight volume prediction model based on the economic cycle . The model consists of influencing factors analysis module , the economic cycle analysis module and prediction module . Economic cycle analysis module is the core of the improved model , determination and phasing of accurate economic cycle is a prerequisite for accurate prediction of railway freight volume . Finally, select the Datong-Qinhuangdao line as an example , were used to improve the model and the traditional model Daqin line cargo forecasts , and predict the results of comparative analysis to demonstrate the improvement of the advantages of the model as well as the feasibility of the use of a dynamic network prediction of railway freight .

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CLC: > Transportation > Rail transport > Rail transport management projects > Freight > Organization and management of transport of goods > Investigation and prediction of the flow of goods , cargo flow
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