Research On Short-Term Traffic Flow Forecasting Method Based On Kalman

With the rapid development and wide application of intelligent transportation system, there are more and more road traffic flow analysis and processing. As the sign of transport going into the information age, intelligent transportation system will be the direction of development of China’s transportation system. The traffic flow forecast is an important part of the intelligent Transportation system. It is very significant that predicting traffic flow conditions accurately for the future periods to alleviate traffic congestion and effectively use of road resources.There are a lot of traffic flow forecasting models. For example, multiple linear regression model, time series model, neural network model, the historical trend model, Kalman filter model and so on. And this paper is focused on Kalman filtering in traffic flow forecast.This paper deals with chaotic characteristics of the traffic flow and distinguishes the predictability of traffic flow. Here it establishes Kalman filter traffic flow forecasting model in the phase space with the combination of phase space reconstruction theory. And I choose C-C algorithm to calculate of the phase space reconstruction parameters. Besides, in this paper in order to improve the prediction effect of Kalman filter model in the phase space, it brings out that use two weeks corresponding to the time difference or the ratio of the traffic flow instead of the original data to establish phase space difference value regression prediction model and phase space ratio regression prediction model. Then by the experimental simulation of the actual traffic flow, it calculates the model performance index and carries out comparative analysis.This paper compares the prediction model that we established to the traffic flow forecasting model based on BP neural network, and the study shows that the algorithm performance is better than the BP neural network model.Besides, this paper establishes multi-weeks’data phase space ratio regression model by the way of increasing the raw data, and compares to the single-week phase space ratio regression model to analyse its performance. It proves that it is better than others.Finally, this paper studies the multi-data fusion in traffic flow forecast. In this paper, it applies the data fusion theory to the phase space of the Kalman filter traffic flow forecasting model and does some simulations on the actual data. Then it compares the single point of data the phase space of the Kalman filter prediction model to multi point of data the phase space of the Kalman filter prediction model. It shows that multi point of data the phase space of the Kalman filter prediction model is better in the traffic flow forecast.