The Study On Fusion Prediction Of Traffic-Flow Volume In Urban Road Based On Integreted Ann

With rapid development of economy and society, the problem caused by traffic is becoming more and more serious. Intelligent Transportation System (ITS), which is widely studied in developed countries, is thought of a potent way to the ground traffic problem today. ITS which is an important symbol of ground transportation system will come in the 21st century. Ground traffic-information, which mainly consists of traffic-flow, is an indispensable element of ITS. It is a key of ITS to predict traffic-flow exactly which is an important step in the research of traffic-flow guidance system. Traffic-flow is a time-varying nonlinear system that is comparatively complex in structure and internal variable. A lot of variables should been input. It only embodies any aspect of system to apply any factor or single model. lt notably increases prediction precision and simulation performance by combining multiple variables, fusing data and models. Accordingly, this paper proposes a fusion-prediction model of traffic-flow in urban road-intersection based on integrated ANN (Artificial Neural Network).Compared with traditional method, this model is not meant to apply single prediction technique or one-sided message, or to fit these together simplly, but meant to widen source of data, to optimize prediction way, and to fuse data and methods soundly. On the one hand, it can evidently increase prediction accuracy; on the other hand, robustness of system is markedly enhanced.With an example of urban road-intersection, how to build the data-fusion prediction model of traffic-flow volume, and how to effectively apply these methods stated in the model, is detailly studied in this paper as follows:1) To meet the need of data-fusion prediction, the features (periodicity, continuity and correlation) of traffic-flow volume are analyzed specislly, which reflect the decisive and influencing factors of traffic-flow.2) It is aimed at enhancing accuracy and robustness that multi-source data model of traffic-flow in road-intersection is built and accordingly data base of prediction is expanded.3) Prediction model of GNN (Gray Neural Network) is put forward according to the periodicity of traffic-flow.4) Prediction method of WNN (Wavelet Neural Network) is put forward according to the continuity of traffic-flow with Wavelet MRA in analysis of traffic-flow volume.5) Neural network method for traffic-flow volume prediction by corresponding road-intersection in road-net is put forward after analyzing correlation in real-time segment of traffic-flow volume between different road-intersections, estimating correlation of traffic-flow volume in road-intersections in corresponding time-segment with fuzzy comprehensive evaluation method.6) Final data-fusion prediction model on FNN (Fuzzy Ncural Nctwork) is stated also with the clustering analysis method of verifying validity in local predictionsThese methods and algorithm is tested by simulation.