This research addresses the problem of modeling time-dependent traffic flow with real-time traffic sensor data for the purpose of online traffic estimation and prediction to support ATMS/ATIS in an urban transportation network. The fundamental objectives of this study are to formulate and develop a dynamic traffic flow model driven by real-world observations, which is suitable for mesoscopic type dynamic traffic assignment simulation. A dynamic speed-density relation is identified by incorporating the physical concept in continuum and kinetic models…
Author: Qin, Xiao
Source: University of Maryland
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Contents
Chapter 1: Introduction
1.1 Research motivation and objectives
1.2 Overview of approach
1.3 Dissertation organization
Chapter 2: Background Review
2.1 Introduction
2.2 Overview of traffic flow theories
2.2.1 Microscopic traffic flow theories
2.2.2 Macroscopic traffic flow theories
2.2.3 Mesoscopic traffic flow theories
2.3 Traffic flow modeling in mesoscopic simulated-based DTA system
2.3.1 Introduction of DYNASMART
2.3.2 Traffic simulation component in DYNASMART
2.3.2.1 Link movement
2.3.2.2 Node transfer
2.3.3 Overview of DYNASMART-X
2.3.3.1 Functionality
2.3.3.2 Description of components and modules
2.4 Traffic flow estimation and prediction
2.4.1 Univariate methods
2.4.2 Transfer function model
2.5 Summary
Chapter 3: Dynamic Speed-density Relation Formulation
3.1 Introduction
3.2 Review of transfer function method
3.3 Dynamic speed-density relation
3.3.1 Determination of system input and output
3.3.2 Model formulation
3.3.3 Model estimation approach
3.3.4 Minimum mean square error forecast
3.3.5 Adaptive calibration and forecasting
3.4 Summary
Chapter 4: Standalone Evaluation Numerical Test
4.1 Test description
4.2 Results and analysis
4.2.1 Model effectiveness and robustness
4.2.2 Sensitivity of the rolling horizon scheme
4.3 Summary
Chapter 5: Traffic Flow Modeling in Real-Time DTA
5.1 Introduction
5.2 Different time scales
5.2.1 Continuous dynamic system vs. discrete dynamic system
5.2.2 Model implementation in DTA-type traffic simulation
5.3 Short term correction
5.3.1 Feedback control
5.3.2 Speed-deviation-triggered CCU
5.3.3 Density-speed-deviation-triggered CCU
5.3.4 Adaptive estimation of control factors
5.4 Summary
Chapter 6: Performance Analysis of Real-Time Traffic Flow Model
6.1 Introduction
6.2 Network overview and data description
6.3 Experimental settings, results and discussion
6.3.1 Experimental settings
6.3.2 Model compatibility
6.3.3 Model transferability
6.3.4 Traffic estimation capability with short term correction
6.3.5 Traffic prediction capability
6.3.6 Impact of temporal scale
6.3.7 Impact of spatial scale
6.4 Summary
Chapter 7: Conclusions and Future Research
7.1 Overall conclusions
7.2 Research contributions
7.3 Future research
Bibliography