Urban Highway Tunnel Monitoring System And Traffic Data Intelligent Analysis

With the development and construction of the modern city, the number and quality of the urban highway tunnels which have become the traffic arteries easing congestion are improved. It is necessary to pay much attention to these tunnels’ monitoring system and operational safety. This thesis proposes a common tunnel monitoring architecture, integration of heterogeneous multisource, the linkage traffic control scheme and traffic data intelligent analysis methods in order to build an appropriate monitoring system integrating running monitoring, traffic control, disaster warning and maintenance and management and so on. The main works of this thesis are as follows.Firstly, according to our design and implementation experience of multiple tunnel monitoring systems in Wuhan, urban highway tunnel monitoring system’s general architecture, subsystems division and basic functions, implementation and technical methodologies are summarized and analyzed. A new mechanism of multiple subsystems decentralized control and centralized management is proposed.Secondly, the tunnel traffic trend ontology model and knowledgeable representation reflecting basic concepts and semantic model of integrated transport network trend are researched and the established. A general framework for ontology integration is further proposed which used in the global semantic web ontology to provide a unified view of the local check. The framework describing a design space can be used to solve the semantic web ontology application integration issues in order to support integrated transport network management and improve the whole efficiency.Thirdly, various monitoring subsystem data and functionality are analyzed and integrated. XML-based unified data representation and storage methods for all kinds of data are proposed. The multi-source data fusion and data mining linkage control mechanism is explored. At last, the tunnel planning management platform is established.Last but not least, the characteristics of the tunnel traffic data are studied. On one hand, a new Kernel Principle Content Analysis (KPCA) for tunnel for traffic data using two-dimensionality reduction algorithm on the basis of the least square method is proposed and tested to reduce data. On the other hand, the tunnel traffic data stream classification and prediction methods for time-division training samples based on Kernel Support Vector Machine Regression (KSVMR) model for the traffic jam situation are proposed and verified.On the basis of practical requirements and testing outcomes of Wuhan urban highway tunnel projects, the framework and techniques proposed in this thesis are able to solve the practical problems efficiently. The aim of this thesis is to build a common intelligent urban highway tunnel monitoring system in order to ensure the safe environment, comfort driving and efficient management.