Research And Application On The Urban Traffic Data Mining

Intelligent Transportation System (ITS) is an integrated transportation and management system with high accuracy and efficiency, which integrates the advanced information technology, communication technology, electronic sensor technology, electronic control technology and computer data processing technology. In this research area, management, integration and mining of massive traffic data are key technologies. Data mining, one of the most powerful data analysis techniques, is an important technique when exploring the common rule from large volume data. The goal of the data mining in ITS is to mine the potential rule behind the traffic data and provide helpful guidance for the design of the ITS. So systems based on data mining can be used to alleviate traffic jam, optimize the traffic road network, and accelerate the traffic development healthily and steadily. The prediction and analysis of the traffic volume, traffic jam and traffic flow distribution are the three important questions of the traffic data mining research, which are significant to the traffic signal management and control, traffic flow inducement and dynamic traffic flow distribution and also play an important role in ITS design and implementation.How to design an efficient mining algorithm is now the key issue of the intelligent traffic data mining research, which involves two following aspects. The first difficulty is to apply the existing data mining algorithm directly to the mass of the traffic flow data for its specific characteristic. The second aspect is that the mining results cannot satisfy the application requirements for the lacking of domain knowledge. Aiming at these problems in the filed of ITS data mining, this thesis proposes the corresponding efficient mining algorithms and applies these algorithms to implement ITS to improve the performance of existing ITS by researching on the intersection traffic flow, traffic flow jam mining and traffic flow distribution pattern mining,. The achievements of this thesis are summarized as follows:1) Design the corresponding algorithm based on combined models through the analysis of the problem at the intersection traffic flow short-term predictionIt is an important premise of dynamic traffic management in ITS to identify and predict the status of the traffic flow instantly and accurately. Traffic volume is one of the main features of the traffic flow; therefore, it is necessary to predict the traffic volume in road network. Aiming at the problem, we propose a traffic volume prediction CITFF(Combined Intersection Traffic Flow Forecast) algorithm, which based on traffic volume sequence partition and neural network model, and divides the traffic volume into different patterns along the volume and time dimension by clustering, and then describes and predicts the traffic flow status according to these different patterns. The experiment results on real data sets demonstrate that our algorithm based on the combination model is much accurate.2) Construct a road traffic flow pattern database and design a traffic flow jam mining algorithmTraffic jam detection is the key technology in ITS research. By the analysis of the traffic flow data, we construct a traffic flow pattern database and propose a traffic flow data description based on the Same-Directed Slope Tree (SDS-Tree). Correspondingly, an efficient traffic flow jam mining algorithm named Detection-CS, which based on the traffic flow pattern database is proposed. Detection-CS(Detection of Continual Stream of Traffic Flow) algorithm extracts the feature of the real-time traffic data and obtains the first k effective feedback through matching the feature with the traffic flow pattern database. Detection-CS then points out the current traffic status According to the feedback. To improve the efficiency, we build a multilayer index structure based on the traffic flow layered information, which can minimize the searching space. This thesis also presents a method to update the traffic flow pattern database to ensure the pattern database efficiently by replacing the seldom-used traffic flow data with the new ones gradually. The experiments on the real data sets show that Detection-CS exhibits higher efficiency and superior accuracy compared with some famous algorithms.3) Analyze the time and space characteristics of the traffic flow data and propose a traffic flow distribution pattern mining algorithmThe traffic flow in the road network has different time and space distribution pattern, so now it is one of the hot topics in ITS to partition real-time and dynastically the traffic area in the road network. We design an Efficient Clustering Algorithm for Spatial Data with Neighborhood Relations (SPANBRE) through clustering the traffic flow data from the loop inductor coils distributing over the road network. SPANBER builds the traffic flow space cluster from bottom up to make the traffic flow data with similar characteristics and space relationship into one cluster. SPANBER can find out the space distribution pattern of the traffic flow. It needs no complex space connecting and combining operation, and the experiment shows SPANBER has high efficiency.4) Design an integrative Intelligent Transportation System based on the data mining techniqueThe study of the traffic flow data mining technology is meaningful to the traffic management and control, traffic flow induction, dynamic traffic allocation. In this thesis, we apply above mining algorithms to implement a comprehensive Intelligent Transportation System based on the data mining technique. It has been put into successful practice in a few projects in several cities from medium to large scale, and also provides an efficient tool for the traffic management.