Research On The Traffic Flow Data Mining In Road Network

The research and application of Intelligent Transportation System has developed rapidly due to the demand on safe, convenient, comfortable and information-based modern transportation. It is important part of the research of Intelligent Transportation System to study different forms and operation rules on traffic flow and establish rapid, stable and effective traffic flow model. With the development of Intelligent Transportation System, mass traffic flow data have been accumulated in Intelligent Transportation System. More and more researchers have started to analyze the information of traffic flow by use of advanced data-mining technique, and discover hidden transportation mode and regulation amongst the information of traffic flow.This paper has made the research on several questions such as traffic flow forecasting, traffic state identification, traffic spatial clustering and real-time inquiry on traffic flow etc. in light of the characteristics of the information of traffic flow and the application demand on new data-mining of Intelligent Transportation System. The research on these questions is of great significance to traffic signal management & control, traffic flow induction, dynamic traffic allocation of Intelligent Transportation System. In general, the main contents and achievements of this paper consist of the following aspects:(1) Based on the characteristics of the transportation and the classic flow-occupancy inverse "V" model, implement polynomial fitting using least-squares algorithm and statistics method on flow curves to detect outliers which are proved to be not accord with practice through the actual implement, then use the moving average model to recorrect the outliers and absent.(2) The traffic flows operated in road network have different distribution models in space, for example, the traffic flow in urban main roads has "line" model and that in busy downtown area has "plane" model etc. it is one of current research issues on Intelligent Transportation System to divide the urban road traffic network into dynamic real-time traffic areas according to the distribution features of operated traffic flow in space. This paper introduces the Spatial Clustering analysis method on traffic flow data from the loop induction coils arranged in road network space to collect the traffic flow data with similar characteristics and spatial relevancy into one category and discover the distribution models of road traffic flow.(3) It is an important content of traffic flow data-mining to forecast up-to-date and accurate short-term traffic flow. The crossroad is intersected by several roads, which is critical component of road network and plays important role in the whole urban road transportation network. The research of short-time traffic flow forecasting in the crossroad may assist to optimize the real-time control and traffic flow induction on road transportation. The neural network model is an important classification forecasting model and different kinds of neural network models have been used for forecasting the short-term traffic flow in road transportation. This paper points out the forecasting method of short-term traffic flow in the crossroads based on relevance analysis and sequence partition by means of BP neural network to increase the accuracy of traffic flow forecasting.(4) For mobile traffic objects in road network, we design an algorithm for the skyline continuous query. The algorithm is divided into two parts: RNASQ (Absolute Skyline Query) and RNCSQ (Continuous Skyline Query). For RNASQ, it is unnecessary to calculate the network distance of all objects from query point, which has good time efficiency. On the basis of RNASQ, we point out RNCSQ (Continuous Skyline Query) in road network. For RNCSQ, continuous Skyline query is transformed into limited independent inquiry among inquiry routine apex and the point of intersection between inquiry object and inquiry point to help judge the division points of continuous segments rapidly and calculate consecutive segments of continuous Skyline query.(5) It is an important content of traffic flow data-mining research to establish unifying, open and extendable data-mining platform of Intelligent Transportation System. This paper introduces a four-layer system structure of ITS data-mining platform, which consists of data layer, data-mining algorithmic tool layer, logical analysis layer and application system layer. This system application platform model is served to establish the data-mining algorithm & analysis function and facilitate the development & configuration of data-mining system so that the clients may utilize the data-mining technique easily based on the needs of practical application. The data-mining system based on this structure has good extensibility and entity independence to facilitate the secondary development. On the basis of extensible system structure of Intelligent Transportation System data-mining application platform, we design an intelligent data-mining platform UTDD (Urban Traffic Data-Mining Development) based on SOA technique to realize the traffic flow data-mining method in this paper.