Implementation of Analysis, Modeling, and Simulation Tools for Road Weather Connected Vehicle Applications
Project Report
FHWAHOP20060
PDF Version [2.28 MB]
Table of Contents
[ Notice and Quality Assurance Statement ] [ Technical Report Documentation Page ] [ SI Modern Metric Conversion Factors ] [ Abbreviations and Acronyms ]
Executive Summary 
Chapter 1. Introduction 
Chapter 2. Evaluation of Analysis, Modeling, and Simulation Tools for Connected VehicleEnabled Road Weather Management Strategies 

Assessment Framework
Review of Selected Analysis, Modeling, and Simulation Tools
Analysis, Modeling, and Simulation Tools Summary 
Chapter 3. State of the Practice and Demonstration Site Selection 

Connected Vehicle Data Collection
Connected Vehicle Data Use in WeatherResponsive Management Strategies
Analysis, Modeling, and Simulation Tools and WeatherResponsive Management Strategies
Site Analysis and Selection 
Chapter 4. Wyoming Interstate 80 Testbed Case Study 

Testbed Review
WeatherResponsive Management Strategies
Simulation Network Calibration
Simulation Framework
Performance Measures
Analysis, Modeling, and Simulation for Traveler Information Messages
Analysis, Modeling, and Simulation for Connected VehicleBased Variable Speed Limit
Analysis, Modeling, and Simulation for Snowplow PrePositioning
System Parameters
Simulation Results
Summary
Suggestions for Additional Research 
Chapter 5. Chicago Case Study 

Chicago Testbed Network Characteristics
Road Weather Connected Vehicle Application in Road Weather Response
Data Description
Integration of Connected Vehicle Data into Road Weather Connected Vehicle Application
Solving the Dynamic Snowplow Route Problem
Test Results

Chapter 6. Conclusion and Discussion 
Endnotes 
Acknowledgments 
List of Figures
Figure 1. Diagram. Mapping of potential evaluation sites to selection criteria. 
Figure 2. Map. Wyoming Interstate 80 corridor — connected vehicle pilot map. 
Figure 3. Maps. Comparison between the realworld and the Vissim network of Interstate 80, Wyoming connected vehicle pilot corridor. 
Figure 4. Diagram. Detailed simulation framework of Vissim® network, component object model, and application programming interface. 
Figure 5. Equation. Calculation of inverse timetocollision (iTTC). 
Figure 6. Illustration. Early lanechange logic. 
Figure 7. Illustration. Connected vehiclevariable speed limit system using satellite communication. 
Figure 8. Maps. Three snowplow prepositioning scenarios. 
Figure 9. Chart. Timeweighted inverse timetocollision under severe weather scenarios. 
Figure 10. Charts. Performance of inverse timetocollision distribution from applying connected vehicles in different weather scenarios. 
Figure 11. Chart. Total inverse timetocollision of all vehicles, cars, and heavygoods vehicles under different trafficsmoothing rate percentages. 
Figure 12. Map. Network of Chicago testbed. 
Figure 13. Diagram. Analysis framework. 
Figure 14. Diagram. Snowplow framework of current operation (left) and road weather connected vehicle application (right). 
Figure 15. Graph. Snow precipitation measured at ASOS station located at O'Hare International Airport during November 25–26, 2018, snowstorm. 
Figure 16. Charts. Traffic speed estimation error with market penetration rate of 1 percent, 5 percent, and 10 percent. 
Figure 17. Graphs. Traffic flow estimation with connected vehicle for market penetration rates (1 percent, 5 percent, and 10 percent). 
Figure 18. Equations. Objective function and constraints for snowplow routing problem formulation. 
Figure 19. Chart. Level of road capacity affected by snow depth. 
Figure 20. Graphs. Traffic speed of DYNASMART simulation results for three plowing strategies. 
Figure 21. Graphs. Traffic speed of DYNASMART simulation results for three plowing strategies. 
List of Tables
