The Research Of Key Technologies On Downtown Traffic Flow Forecasting

With the development of the science and technology, the issues of traffic jam, traffic safety and traffic environmental pollution have caused many scientists widespread concern. The traffic flow systems have the dynamic characteristics of time change, coupling and non-linear. Its complexity, the visualization, the relationship between the structure and the function have also not been got attention adequately. So this paper has studied the key technologies of traffic flow. Its main contents are as follows:This paper has mainly analyzed data characteristics of massive traffic flow and the temporal-spatial correlation of traffic flow in downtown intelligent transportation network. The research of the complex networks analyzes the main factors which influence the changes of the urban traffic flow. It provides the decision support for traffic flow prediction modeling. The research combine with urban traffic flow characteristics, use the mining techniques of large data to extract and process data as so to mine potential useful knowledge which provide data support for traffic flow prediction modeling. On the one hand, according to the current traffic flow data to dynamically adjust its impact on future pr ediction; On the other hand, through the analysis of the temporal-spatial characteristics for historical traffic flow data, the similar knowledge is used to seek the most assumptions rules of the current traffic flow characteristics. The methods reduce big data, draw main data, mine unknown information, remedy the deficiencies of approach performance in the traditional data processing, take into account both traffic flow prediction precision and traffic flow dynamic change of strong adaptability. To study the traffic flow forecasting of the large-scale network mainly use the two methods to establish the forecasting model of traffic flow.Such as: the model based on knowledge immune and the model based on chaos immune knowledge. The immune algorithm based on knowledge makes use of each advantage with knowledge construction and immune optimization algorithm. The chaos immune algorithm based on knowledge makes use of each advantage with chaotic algorithm and the internal strategy of knowledge immune, so as to dynamically control and adjust the size of the area, in order to accelerate its algorithmic search speed and achieve the efficient forecasting of short-term traffic flow. The simulation results show that the method is feasible and effective.