Mending Defective Information And Analysis On Spatial-Temporal Distribution Of Regional Traffic Flow

Traffic flow information electromechanical detected system is an important part intelligent transportation system. The collected traffic flow information is the guarantee of intelligent transportation guidance system, providing important basis. However, the traffic information is usually collected by the sensors buried at the node of traffic grid, however, defective information is always occurred due to the sensor failure of constructing, transmitting or processing and lack of sensor. Therefore, in order to provide complete information to display and explore the law of traffic distribution deeply, the defective information need to be mended.According to the analysis, the defective information can be divided into incomplete information and non-information, which can be repaired by SARBF neural network fitting method and Lagrange interpolation method respectively. The first step of SARBF neural network fitting method to repair the incomplete is selecting the data-complete intersections by autocorrelation analysis in spatial database, then obtain the RBF neural network training sample data of participated intersections to mend the defective data. When the non-information is repaired by Lagrange interpolation method, the interpolation nodes are selected by spatial encoding in GIS.When the spatial and temporal distribution of traffic flow is analysised, the jumping phenomenon happened in fluctuation which is obtained from fitting the complete traffic flux data by B-spline surface fitting method. After drawing soliton equation by analyzing traffic flow model, the urban traffic soliton is searched out, and the conception of positive and negative soliton is proposed, serving for relieving traffic jam, providing a new way to make guidance plan and fully using road resources by repressing peak or filling vale.During the research on the Important Project for Key Subject of Zhejiang Province " Research and Application of the Key Issues in Urban Intelligence Traffic Guidance System " (2006C13100) , these theories and methods above are put into practice and proved by developing Hangzhou traffic information system.