Research On Theories And Methods Of Short-term Traffic Flow Forecasting Of Road Network Based On Real-time Data

Special research on traffic science of "National Guideline on Medium-term and Long-term Program for Science and Technology Development(2006-2020)" implemented from 2006 clearly put forward Concepts of "develop a system, solve three hot spots". A system indicated comprehensive transportation system while three hot spots indicated traffic energy and environment, traffic safety as well as traffic congestion of the metropolis. Intelligent transportation systems are considered as one of effective methods to relieve the urban traffic problems such as traffic congestion, air pollution caused by motor vehicle exhausts and traffic accidents. Short-term traffic flow forecasting is not only a core element of intelligent transportation systems but also an important base of traffic information service, traffic control and guidance which can supple travelers with efficient information and help them to choose a optimal path so as to perform path guidance, to save travel time of travelers, to relieve traffic congestion, to reduce air pollution and to save energy. As the development of traffic science and technology, automatic vehicle identification devices set on the roads has improved constantly, which make it possible for us to perform short-term traffic flow analysis and forecasting. Theories and methods of short-term traffic flow forecasting of road network based on real-time data were studied in the dissertation. On the base of traffic flow data analysis, relevance of traffic flows of several road cross-sections in different spots in a road network was considered so as to choose range and object for forecasting. Then theories and methods of short-term traffic flow forecasting of several road cross-sections in a road network were discussed and investigated. Moreover, real-time data were used to test the efficiency of the proposed forecasting model.Firstly, concept, process, characteristic and request of short-term traffic flow forecasting were introduced and researches on short-term traffic flow forecasting up to now were summarized into two kinds of methods which were single road cross-section short-term traffic flow forecasting and multiple road cross-sections short-term traffic flow forecasting. Secondly, traffic flow data were analyzed. In order to solve the problem of data source, preprocessing techniques of traffic flow data were adopted include deletion of erroneous data and supplement of absent data. Matrix for the correlation coefficients of road cross-sections in a road network was examined. Then multidimensional scaling from theory of multivariate statistical analysis was used to associate the relevance of traffic flows data from road cross-sections with a two dimensional chart of derived stimulus configuration. According to the chart, the strong or weak of the relativity of each road cross-section can be distinguished so as to choose the range of road network under research. Thirdly, a short-term traffic flow forecasting model of multiple road cross-sections in a road network was proposed based on state space model in which state space model was combined with time series analysis. EM algorithm was applied to estimate the parameters of the proposed model. Fourthly, a short-term traffic flow forecasting model of multiple road cross-sections in a road network based on support vector regression was proposed. After choosing research range, a forecasting model was set up on the base of algorithm of least squares support vector regression. The process of selecting go-back dimension was combined with the process of using genetic algorithm to select parameters of support vector regression to perform optimization of parameters of the proposed forecasting model. Fifthly, after chaotic time series analysis and phase space reconstruction of multidimensional short-term traffic flow data, a short-term traffic flow forecasting model of multiple road cross-sections in a road network based on chaotic time series analysis was put forward. The parameters of forecasting model was optimized with particle swarm optimization algorithm. Meanwhile, the forecasting models and methods of multiple road cross-sections in a road network proposed in the dissertation were tested by real-time traffic flow data from urban expressway and the results were satisfying.The followings are the main conclusions of this dissertation.1. Based on traffic flow data analysis, traffic flows of multiple road cross-sections in a road network are considered as a whole to perform short-term traffic flow forecasting and theories and methods of short-term traffic flow forecasting of multiple road cross-sections in a road network based on traffic flow data analysis were studied.2. Multidimensional scaling from theory of multivariate statistical analysis was used to associate the relevance of traffic flows data from road cross-sections with a two dimensional chart of derived stimulus configuration. According to the chart, the strong or weak of the relativity of each road cross-section can be distinguished, which supplied research range, object and base of data analysis to short-term traffic flow forecasting models and methods considering the variety of time and space of traffic flow.3. According to characteristic of theories of short-term traffic flow forecasting up to now, traffic flow forecasting was extended from single-cross-section forecasting approach to multi-cross-sections forecasting approach from the aspects of linear system theory, artificial intelligence methods and nonlinear system theory: (1) On the base of choosing research range, state space model was combined with time series analysis to set up a short-term traffic flow forecasting model of multiple road cross-sections in a road network based on state space model. Moreover, EM algorithm was applied to estimate the parameters of the proposed model. (2) On the base of choosing research range, a short-term traffic flow forecasting model of multiple road cross-sections in a road network based on support vector regression was proposed. Algorithm of least squares support vector regression was used to set up the model. The process of selecting go-back dimension was combined with the process of using genetic algorithm to select parameters of support vector regression to perform optimization of parameters of the proposed forecasting model. (3) On the base of choosing research range, a short-term traffic flow forecasting model of multiple road cross-sections in a road network based on chaotic time series analysis was put forward on the base of chaotic time series analysis and phase space reconstruction of multidimensional short-term traffic flow data as well as the optimization of the parameters of forecasting model with particle swarm optimization algorithm.