The Prediction Of Railway Freight Cost Based On Cost Driver And BP Neural Network

Railway freight transportation is one of the most important parts of railway transportation. To extend effective control for cost saving can reduce the total railway transportation cost. On the premise of that is to forecast cost accurately and timely. So choosing a reasonably prediction method can not only provide effective cost information, but also make the company do well in the complicated and volatile economic environment. There are a lot of prediction methods, but not all of them fit for railway transportation cost and the situation of our country's railway transportation. In this paper, we compare the forecast results of some prediction methods to find a method which is more suit our railway transportation.By analyzing the parts of the railway transportation cost and the influencing factors, we can get that the indirect cost has a large proportion in the transportation cost, and between the sample data of the transportation cost has a non-linear relationship. Before estimating the cost we should choose some reasonable distribution targets for the freight cost and consider the non-linear relationship of the cost. Based on the two reasons, we forecast the freight cost with ABC and BP neural network. ABC can consider all the activities in the transportation, and distribute the activities to the cost reasonably. In addition, BP neural network has Strong learning ability and fault-tolerant ability, it can solve the problems which the other methods cannot. Then using MATLAB software trains the network repeatedly to get the forecast results we need.At the last of the paper, the paper estimates a railway company's freight cost with two methods. They are cost driver-BP neural network and grey system. Comparison of the predicted results, we get that the cost driver-BP neural network have an ideal result, it can be an ideal method for the freight cost estimation.