Study And Application Of Travel Time Prediction Of Cars Based On Artificial Neural Network

The travel time's estimate is an important content in the traffic route guidance system (TRGS). For several decades, many experts and scholars have used various methods to build many estimate models with different precisions.Because the city transportation network is a complicated system, the transportation data of different time are nonlinear. The neural network has to identify to be responsible for the characteristic of nonlinear system. This text discussed the result and accuracy of a few main models in putting forward transportation to flow a short date estimate, model should have the foundation of characteristic, and studied a network according to the BP nerve according to the factors, such as transportation discharge and history route of travel time...etc. The BP nerve network model build up a system that can predict the travel time with vehicle route of travel in the city, and take actual road as an example, carries on an inside theories verification to the model, obtain better result, therefore can be applied to transportation realm according to the transportation parameter of the nerve network estimate method.