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Contents
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of this document (193 pages) are available for anonymous download.
List of Figures
List of Tables
Introduction
Motivation and Research Objective
Literature Review
Advances in Dynamic Traffic Management Systems
Evaluation Methods
Field tests
Simulation based evaluations
Traffic Simulation Tools
Thesis Outline
Evaluation Framework
Process for Evaluation and Design Refinement
Traffic Simulation Laboratory
Traffic Flow Simulator
Network components
Travel demand
Driving behavior and vehicle movement
Traffic Management Simulator
Surveillance System
Control and Routing Devices
Simulation Output and Measures of Effectiveness
System Integration
Software Architecture
Software Elements
Distributed Implementation
Graphical User Interface
Microscopic Traffic Simulator
Overall Design
Network Representation
Nodes:
Links:
Segments:
Lanes:
Toll Plazas
Traffic Surveillance and Control Devices
Surveillance Sensors
Traffic Control Devices
Incidents
Travel Demand
Vehicle Characteristics
Maximum acceleration rate:
Maximum deceleration rate:
Normal deceleration rate:
Desired speed:
Speed distribution across lanes:
Target speed:
Vehicle Routing
Route Choice Model
Route Switching Model
Properties and Extensions
Vehicle Movements
Reaction Time
Vehicle Loading
Acceleration Rate
Car following
Free flowing regime:
Emergency regime:
Car-following regime:
Merging
Event Responding
Traffic signals and signs:
Incidents:
Connection to downstream link:
Courtesy yielding:
Lane Changing
Lane Change Decisions
Gap acceptance
Nosing and Yielding
Probability of nosing:
Feasibility of nosing:
Simulation Output
Sensor Readings
Measures of Effectiveness (MOE)
Graphical User Interface
Traffic network:
Traffic sensors:
Traffic controls and incidents:
Animation of vehicle movements:
Validation
Traffic Management Simulator
Framework and Overall Structure of TMS
Route Guidance
Reactive Route Guidance
Predictive Route Guidance
Rolling Horizon Model
Rolling horizon length
Rolling horizon step size
Guidance Resolution
Computational delay
Network State Estimation
Network State Prediction
Dynamic OD prediction:
Traffic prediction:
Guidance generation
Traffic Control
Static Controllers
Pretimed Controllers
Offset:
Timing Table:
Traffic Adaptive Controllers
Signal records:
Phase records:
Detector records:
Metering Controllers
Cycle length:
Limiting parameters:
Regulators and desired state:
Queue detectors:
Step size for updating metering rate:
Incident Management
Response Plan
Situation code
Device type
Affected region
Signal/Sign state
Incident Detection Time
Selection and Activation of Response Plan
Mesoscopic Traffic Simulator
Network Representation
Traffic Cells and Traffic Streams
Capacity Constraints
Traffic Dynamics
Speed-Density Model
Cell-Following Model
Step 1:
Step 2:
Vehicle Characteristics
Vehicle Routing
Input and Output
Computational Tests and Validation
Case Study
The Network
Sensor Data
OD Flows
Path Generation and Vehicle Routing
Specification of Route Choice Model
Path Table
Historical Link Travel Times
Calibration of MITSIM and MesoTS and Validation of MesoTS
Value of Real-Time Route Guidance
Scenarios
Measures of Effectiveness (MOE)
Results
Computational Performance
MITSIM:
MesoTS:
SIMLAB:
Conclusion
Conclusion
Research Contribution
Future Work
Evaluation of alternative dynamic traffic assignment (DTA) models:
Evaluation of various traffic management strategies:
Operator training:
Laboratory travel behavior studies:
Network state estimation:
OD estimation and prediction:
Capacity translator:
Fuel consumption and emissions models:
Hybrid simulation:
Distributed and parallel simulation:
References
Abbreviation
Calculation of Time-Dependent Shortest Paths
Simulation Parameters
Vehicle Characteristics
Driver Behavior
Random Number Generator
Statistics for Evaluating Simulation Models
Examples of Data Files
Network Database
Time Dependent OD Trip Tables
Vehicle Trip Table
Vehicle Path Table
Incidents
Acknowledgments
Qi Yang
Wed Feb 26 19:17:06 EST 1997