Office of Operations
21st Century Operations Using 21st Century Technologies

Integrated Modeling for Road Condition Prediction Phase 3 Project Report

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U.S. Department of Transportation - Federal Highway Administration (logo)

U.S. Department of Transportation
Federal Highway Administration
Office of Operations
1200 New Jersey Avenue, SE
Washington, DC 20590
ops.fhwa.dot.gov

October 2020
FHWA-HOP-20-061


Table of Contents

[ Notice and Quality Assurance Statement ] [ Technical Report Documentation Page ] [ SI Modern Metric Conversion Factors ] [ List of Acronyms ]

Executive Summary
Chapter 1. Introduction
Background
Purpose
Scope
Document Overview
Chapter 2. Project Description
Methodology and Approach
Project Tasks and Deliverables
Stakeholder Engagement
Chapter 3. Implementation and Deployment
User Needs
Application Scenarios
Variable Speed Limits
Enhanced Traveler Information
Enhanced Intelligent Signal Controls
Maintenance
Freight
Work Zones
Travelers
Emergency Response
System Description
Data Collection
Forecast Model Components
Data Store
User Interface
Study Area Description and Modeling
Traffic Estimation and Prediction System Traffic Model
Traffic Flow Model
Weather Adjustment Factors
Online Traffic Flow Model Update
Time-Dependent Origin-Destination Matrix
Offline Calibration of the Origin-Destination Matrix
Online Calibration of the Origin-Destination Matrix
Machine Learning-Based Traffic Prediction
Model
Data
Model Calibration
Results
Chapter 4. Evaluation
Introduction
Summary of Findings
Did Integrated Modeling for Road Condition Prediction Have an Operational Impact?
Did Users Consider Integrated Modeling for Road Condition Prediction Information as Useful?
Chapter 5. Analysis, Conclusions, and Recommendations for Further Study
Lessons Learned
Deployment Considerations
Potential Applications
Pavement State Prediction
Travel Time Predictions
Flooding Events
Conclusions
Recommendations for Further Study, Development, and Application
Bibliography

List of Figures

Figure 1. Diagram. Components of the Integrated Modeling for Road Condition Prediction.
Figure 2. Screenshot. User interface of an example map in the Integrated Modeling for Road Condition Prediction system.
Figure 3. Map. Kansas City metropolitan study area of the Integrated Modeling for Road Condition Prediction.
Figure 4. Map. Selected detectors for traffic flow model calibration by identifier code.
Figure 5. Graph. Sensitivity analysis of different weights.
Figure 6. Diagram. Framework of online traffic demand calibration in Traffic Estimation and Prediction System.
Figure 7. Diagram. Proposed machine learning-based prediction model algorithm.
Figure 8. Diagram. Representation of the Integrated Modeling for Road Condition Prediction machine learning-based prediction (MLP) process.
Figure 9. Graph. Prediction of normal case with daily traffic patterns.
Figure 10. Graphs. Predictions of speeds with incident on link.
Figure 11. Graphs. Predictions of speeds with rain and snow weather condition.
Figure 12. Graph. Examples of predictions for special events.
Figure 13. Graphs. Examples of predictions for links without detectors.
Figure 14. Illustration. Integrated Modeling for Road Condition Prediction relationship to other weather-responsive management strategy tools.
Figure 15. Screenshot. Pavement state example — 4:30 a.m., January 22, 2020.
Figure 16. Screenshot. Pavement state prediction example — 7 a.m., January 22, 2020.
Figure 17. Screenshot. Route travel time early in a winter storm.
Figure 18. Screenshot. Route travel time prediction for a winter storm.
Figure 19. Screenshot. Hydrological event alert for July 27, 2017.
Figure 20. Screenshot. Local hydrological event map for July 27, 2017.

List of Tables

Table 1. Characteristics of traffic count data.