Intelligent Transport System Oriented Research On Spatial Data Mining

With the developing of information technology, Intelligent Transport System and Data Mining have become the hot area in research. Based on the development and application of GIS technology, Intelligent Transport System has possessed extremely enormous amount of spatial data. The strategies of making full use of the spatial data and exposing the potential knowledge behind the data for supporting the decision-making, which can make GIS more intelligent, have been a significant commitment of data mining research.Based on the spatial data from Shenzhen Urban Transport Simulation System, adopting Neural Network theory of Data Mining, especially Error Back Propagation Neural Network, the model for short-term traffic flow forecasting has been structured in this research. The model improves the traditional Neural Network algorithm which is totally established in a black box way, by introducing the conception of traffic flow theory. Before training the neural cells, the sample space will be classified into different set. In this way, the BP Neural Network will produce more exact result for traffic flow forecasting, and to some extent, the model also can prevent the slow convergence and local minimum which tend to occur in the BP Neural Network algorithm. At the end, Through the traffic data collected from the real road in SUTSS, the efficiency of the proposed algrithom has been validated.