Study On Traffic Flow Forecast Model Based On Neural Network

With the economical development and the expansion of the city scale, the problem of the transportation congestion is outstanding day by day. At present, two ways are used to effectively alleviate the traffic congestion. Increasing the road network's density is one of the most effective means. The other is optimize the utility of the road network through the reasonable regulation of traffic flow, which is studying and applying intelligent transportation system (ITS) .the key of it is to forecast the traffic flow.This article has designed three kinds of forecast projects and take the single intersection road traffic flow forecast as an example, In the August 8th and 10th from 6:00 a.m. to 8 :40 2006, We measured the actual traffic flow of the Road intersection between Red flag street and XianFeng Road by all directions. The BP network model was trained and simulated. We have selected a better solution as a practical model by the swatch measured.Because the speed of BP network's convergence is too slow and enters local minimum point easily, the article provides advanced BP algorithm through adjusting study rate to improve the performance of the forecast model. The algorithm may adjust the study rate according to the forecast precision. It is the innovation.Since the study rate and the conceal level numbers have great influence on the forecast precision of network. This article has researched the most superior BP neural network forecast model using the genetic algorithm. This is anther innovation.This algorithm changing the BP network the study rate and the concealment level unit number, optimizes overall performances of forecast model using the simulation result. The study indicates, the forecast model which using the genetic algorithm has the merits of high forecast precision and short restraining time , and can be applied as the actual forecast model.