Methods Of Traffic Flow Analysis Based On Similarity

To solve traffic congestion, improve traffic flexibility, has been becoming a subject of global concerns because of worldwide transportation systems facing unprecedented challenges in recent years. Traffic stream analysis is the basis of intelligent transportation systems and has great significance for traffic organization and control. This dissertation focuses on time series similarity problems and its application in traffic flow analysis.The basic ideas of the papers are as follows:finding the potential pattern of traffic flow through analyzing the characteristics of similarity, achieving assessment of traffic flow, forecast and analysis of the future trends according to the relationship between the detection data and models, are based on historical data. The dissertation studies and discusses the following four questions:the discovery of pattern of time series similarity, the detection and cleaning method of qutlier data of traffic flow, the methods of traffic flow forecasting, traffic status discrimination and evaluation methods.Then, this dissertation verifies the feasibility and reliability of all analytical methods in practical applications through analyzing sampled data of actual traffic flow.The main works and contributions in the dissertation are as follows.1. Idea about time series decomposition is present. The flow series are decomposed into benchmark series and deviation series. Benchmark reflects common property of class members and deviations reflects differences of members, and they reveal the essence of similarity. The dissertation also gives quantitative checking methods of peak traffic flow and relevant parameter calibration algorithm and discussion about traffic flow similarity and traffic flow model through validation of actual data.2. The dissertation proposes traffic flow forecasting methods based on similarity. Specifically, it designs two types of prediction algorithm working for short-term and medium-term prediction separately. The long-term prediction MFBS completes long-term quantitative prediction of traffic flow. At the same time, the dissertation verifies the feasibility of two types of algorithms through actual data, and compares actual data with three traditional short-term prediction methods, the results show that MFBS algorithm can reach the precision as traditional short-term prediction do.3. The dissertation proposes identification and evaluation methods of the state of trafflic flow based on similarity. Status identification method (SFT) can complete finding crowded state and judging causes, and it is more effective than traditional identification methods. Three kinds of evaluation methods based on road load, the changes of crowded state, and the peak characteristics changes achieve evaluation of traffic flow trends from the micro-and macro-level separately. The practical feasibility of corresponding methods is validated by actual data.4. The dissertation proposes checking methods of outlier data based on simliarity and designs recovery algorithms to recover outlier data for static systems and real-time systems. The feasibility of recovery algorithm is validated by actual data.