动态交通流的随机演化

1 Introduction

1.1 Motivation

1.2 Objectives

1.3 Contributions

1.4 Organization

2 Literature Review

2.1 Introduction

2.2 Historical Development of Traffic Flow Theory

2.2.1 Macroscopic Modeling

2.2.2 Mesoscopic Modeling

2.2.3 Microscopic Modeling

2.2.4 Stochastic Modeling

2.3 Probabilistic Headway/Spacing Distributions

2.3.1 Simple Univariable Distributions

2.3.2 Compositional Distributions

2.3.3 Mixed Distributions

2.3.4 Random Matrix Model

2.4 Summary

3 Empirical Observations of Stochastic and Dynamic Evolutions of Traffic Flow

3.1 Introduction

3.2 Characteristics of Headway/Spacing/Velocity

3.3 Congested Platoon Oscillations

3.4 Time-Frequency Properties

3.5 Summary

4 A Markov Model Based on Headway/Spacing Distributions

4.1 Introduction

4.2 A Markov Model for Headway/Spacing Distributions

4.2.1 Background

4.2.2 Markov-Process Simulation Models

4.2.3 Simulation Results

4.2.4 Discussions

4.3 Asymmetric Stochastic Tau Theory in Car-Following

4.3.1 Asymmetric Stochastic Extension of the Tau Theory

4.3.2 Testing Results

4.3.3 Discussions

5 Stochastic Fundamental Diagram Based on Headway/Spacing Distributions

5.1 Introduction

5.2 Newell's Simplified Model and Its Stochastic Extension

5.3 The Homogeneous Platoon Model

5.3.1 Basic Idea

5.3.2 Summation of Lognormal Random Variables

5.3.3 Average Headway Distribution

5.3.4 Model Validation

5.3.5 Sensitivity Analysis

5.4 The Heterogeneous Platoon Model

5.4.1 Average Headway Distribution

5.4.2 Validation

5.4.3 Boundaries of Congested Flows

5.5 Summary

6 Traffic Flow Breakdown Model Based on Headway/Spacing Distributions

6.1 Introduction

6.2 Nonparametric Lifetime Statistics Approach

6.3 Queueing Models for Breakdown Probability

6.3.1 Backgrounds

6.3.2 Some Previous Models

6.3.3 G/G/1 Queueing Model

6.3.4 Discussions

6.3.5 Model Validation

6.3.6 Summary

6.4 Phase Diagram Analysis

6.4.1 Backgrounds

6.4.2 The Spatial-Temporal Queueing Model

6.4.3 The Analytical Solution for Phase Diagram

6.4.4 Numerical Example

6.5 Discussions

7 Conclusions and Future Work

Appendix A: Linear Stability Analysis of the Higher-Order Macroscopic Model

Appendix B: Linear Stability Analysis of the Multi-Anticipative Car-Following Models

References

Index

随机交通流模型是智能交通系统、交通工程设计、交通管理与控制等领域的应用基础，对丰富现代交通流理论体系具有重要意义。道路交通流具有复杂性、动态性和随机性特征，新一代智能交通系统对交通流理论提出更高要求。本书应用多元异构数据，建立基于车辆轨迹信息的随机交通流模型，揭示交通流复杂动态性和随机演化性的内在机理。本书主要研究内容和成果表现在数据挖掘、微观关联、宏观关联、匝道瓶颈建模等方面。本书适合交通流和交通大数据领域的相关研究人员和学生参考。