Implementation of Analysis, Modeling, and Simulation Tools for Road Weather Connected Vehicle Applications

Project Report

FHWA-HOP-20-060

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 Vehicle-Enabled Road Weather Management Strategies
	Assessment Framework Background on Existing Platforms and Related Features Functional Requirements Assessment of Tools against Above Requirements Review of Selected Analysis, Modeling, and Simulation Tools Special Purpose Tools and Platforms 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 Weather-Responsive Management Strategies Analysis, Modeling, and Simulation Tools and Weather-Responsive Management Strategies Site Analysis and Selection
Chapter 4. Wyoming Interstate 80 Testbed Case Study
	Testbed Review Weather-Responsive Management Strategies Traveler Information Messages Connected Vehicle-Based Variable Speed Limit Snowplow Pre-positioning Simulation Network Calibration Simulation Framework Performance Measures Analysis, Modeling, and Simulation for Traveler Information Messages Early Lane-Change Advisory Forward Collision Warning Analysis, Modeling, and Simulation for Connected Vehicle-Based Variable Speed Limit Analysis, Modeling, and Simulation for Snowplow Pre-Positioning System Parameters Simulation Results Traveler Information Messages Variable Speed Limit Snowplow Pre-positioning Summary Suggestions for Additional Research
Chapter 5. Chicago Case Study
	Chicago Testbed Network Characteristics Road Weather Connected Vehicle Application in Road Weather Response Analysis Modeling Framework of Road Weather Connected Vehicle Application Road Weather Connected Vehicle Application in Snowplow Operation Data Description Data Sources and Description Integration of Connected Vehicle Data into Road Weather Connected Vehicle Application Integration of Traffic Data for Real-Time Traffic Estimation Prediction Real-Time Traffic Speed Real-Time Traffic Flow Solving the Dynamic Snowplow Route Problem Mathematical Model Heuristic Algorithm Test Results Performance Comparison Speed Comparison Flow Comparison
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 real-world 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 time-to-collision (iTTC).

Figure 6. Illustration. Early lane-change logic.

Figure 7. Illustration. Connected vehicle-variable speed limit system using satellite communication.

Figure 8. Maps. Three snowplow pre-positioning scenarios.

Figure 9. Chart. Time-weighted inverse time-to-collision under severe weather scenarios.

Figure 10. Charts. Performance of inverse time-to-collision distribution from applying connected vehicles in different weather scenarios.

Figure 11. Chart. Total inverse time-to-collision of all vehicles, cars, and heavy-goods vehicles under different traffic-smoothing 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

Table 1. Mesoscopic simulation tools assessment based on identified requirements.

Table 2. Microscopic simulation tools assessment based on identified requirements.

Table 3. Agency connected vehicle weather data collection state of practice.

Table 4. Agency connected vehicle road weather-responsive management strategies state of practice.

Table 5. Site analysis summary.

Table 6. Car-following parameters used for clear, snowy, and severe weather in this study.

Table 7. Speed limit determination of three weather scenarios.

Table 8. The inverse time-to-collision reduction for cars and heavy-goods vehicles under three traffic-smoothing rate scenarios.

Table 9. Results of base case, case 1, and case 2 in severe weather.

Table 10. Network characteristics for Chicago testbed.

Table 11. Average relative error of traffic flow estimation with connected vehicle.

Table 12. Average absolute error of traffic flow estimation with connected vehicle.

Table 13. Pre-determined variables and given values in the optimization problem.

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