機動車數(shù)量的快速增加以及城市化進程的擴展加劇了不斷增長的交通需求與城市交通基礎設施供應能力之間的矛盾。作為城市交通面臨的巨大壓力導致的社會問題之一,交通擁堵已經(jīng)成為在交通管理領域中急需解決的嚴重挑戰(zhàn)。能夠緩解交通擁堵的最可行的措施是通過建立智能交通系統(tǒng)（Intelligent Transportation System,ITS）,以提高交通管理和服務效率。智能交通系統(tǒng)由一系列能夠為交通管理者、車輛以及個別出行者提供多種服務的先進技術所組成,從而使交通系統(tǒng)的各個部分能夠更好的協(xié)調,共享有用信息,做出及時正確的決策。交通流預測是在智能交通系統(tǒng)研究和應用中不可缺少的重要功能,如何實時準確地預測未來交通流已經(jīng)成為交通管理科學領域中的一個研究熱點。許多研究人員提出了多種交通流預測模型。然而,關于如何挖掘影響目標交通流的不同因素以及如何將此信息結合在預測模型中的研究仍然不足。為了提高交通流預測模型的性能,必須考慮與目標交通流有關的足夠信息。在交通網(wǎng)絡中相鄰路段上的交通流之間存在明顯的時空相關性。因此,正確估計此類相關性對提高交通流預測結果的準確性至關重要。針對交通流時空相關的非線性以及動態(tài)性分析,... 

【文章頁數(shù)】：158 頁

【學位級別】：博士

【文章目錄】：
Abstract
摘要
Chapter 1 Introduction
    1.1 Research Background
    1.2 Objectives and Significances of the Research
    1.3 Parameters and Characteristics of Traffic Flow
        1.3.1 Traffic Flow Parameters
        1.3.2 Characteristics of Traffic Flow
    1.4 Overview of Related Works
        1.4.1 Traffic Flow Prediction Models
        1.4.2 Spatiotemporal Correlation Analysis
        1.4.3 Traffic Pattern Clustering
    1.5 Main Contents of the Research
    1.6 Structure of the Dissertation
Chapter 2 Nonlinear Analysis of Spatiotemporal Correlation Based on Mutual Information
    2.1 Mutual Information
    2.2 MI-Based Feature Selection Criterion and Search Strategy
    2.3 Predictor Selection Using MI-Based Feature Selection Criterion
        2.3.1 Generation of Features from Traffic Time Series
        2.3.2 Feature Selection Algorithm
        2.3.3 Traffic State Vector
    2.4 Adaption of KNN-Based Prediction Model
        2.4.1 KNN-Based Prediction
        2.4.2 Composition of Traffic State Vector
        2.4.3 Distance Metric and Prediction Function
    2.5 Case Study
        2.5.1 Case Data and Evaluation Measures
        2.5.2 MI Estimation Method
        2.5.3 Results of MI Analysis
        2.5.4 Feature Selection and Traffic State Vector Composition
        2.5.5 Prediction Results
    2.6 Brief Summary
Chapter 3 Traffic Clustering for Dynamic Analysis of Spatiotemporal Correlation
    3.1 Clustering of Spatiotemporal Correlation Matrices
        3.1.1 Spatiotemporal Correlation Matrix
        3.1.2 Traffic Clustering Based on CLARANS
    3.2 Spatiotemporal Correlation Analysis
    3.3 Cluster-Wise Predictor Selection
    3.4 Case Study
        3.4.1 Experimental Settings
        3.4.2 Clustering Results
        3.4.3 Results of Spatiotemporal Correlation Analysis
        3.4.4 Results of Predictor Selection
    3.5 Brief Summary
Chapter 4 Prediction-After-Classification of Traffic Flow
    4.1 Overall Framework
    4.2 Offline Phase
    4.3 Online Phase
        4.3.1 Classification of Current Traffic Pattern
        4.3.2 Regime-Switching Prediction
    4.4 Case Study
        4.4.1 Comparative Prediction Models
        4.4.2 Prediction Results
        4.4.3 Comparison with Other Clustering Methods
    4.5 Brief Summary
Chapter 5 Some Application Models for Traffic Management
    5.1 Mining of Spatiotemporal Correlation in Entire Traffic Network
    5.2 Traffic Flow Prediction over Entire Traffic Network
    5.3 Network Decomposition for Distributed Traffic Management
    5.4 Dynamic Route Guidance with Predictive Traffic Information
        5.4.1 Route Guidance System
        5.4.2 Routing Strategy with Predictive Traffic Information
        5.4.3 Simulation
    5.5 Brief Summary
Conclusion
結論
References
List of Publications During the Doctoral Degree
Acknowledgement
Resume


【參考文獻】：
期刊論文
[1]A two-stage short-term traffic flow prediction method based on AVL and AKNN techniques[J]. 孟夢,邵春福,黃育兆,王博彬,李慧軒.  Journal of Central South University. 2015(02)



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