Design And Implementation Of The Parallel Computing In The Railway Wagon Flow Forecasting Based On Big Data Storm Method

Railway wagon flow forecasting is the basis of the railway wagon flow adjustment.Improving the accuracy of the railway wagon flow forecasting can accelerate wagon turn-round and increase the revenue.Manual forecasting is the main way of the railway wagon flow forecasting.Because of the cost of the manual forecasting is high and the reliability is low,it is difficult to meet the need of railway transport organization.With the development of the big data,there is a new way to forecast the wagon flow.Railway transportation information integration platform is the platform of the railway data.The platform is the basis of using big data technology to forecast the railway wagon flow.We use a new way to build the wagon flow forecasting model and algorithm based on the big data technology.We also complete the implementation of the system and the analysis of the results.The contents of the article are shown below.In the perspective of the model,the traditional railway wagon flow forecasting model can only provide the forecasting result of the railway bureau,and the starting position is not accurate.So we puts forward the railway wagon flow forecasting model based on the railway transportation information integration platform.This model can provide the forecasting result of the wagon,and the result of this model is more accurate than the traditional.In the perspective of the algorithm,because of the amount of the calculation,it is difficult to solve the model directly.So we propose the decomposition-coordination algorithm.This algorithm can get the forecasting result by spliting up the calculation tasks.In the perspective of the implementation of the algorithm,we use the parallel computing method to analyze the platform data.We also design the distribution mechanism of the tasks.In the perspective of the System implementation,we use the Storm to process the wagon flow data.We form a forecasting scheme and complete the design of the system architecture.We also realize the four modules by using the object-oriented language.In the perspective of the results analysis,we collect and analyze the results.The result shows that the method proposed in this article can reduce the calculation time and can also improve the accuracy of the forecasting results.