NCHRP Web-Only Document 436

Leveraging Existing Traffic Signal Assets to Obtain Quality Traffic Counts

A GUIDE

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COOPERATIVE RESEARCH PROGRAMS

CRP STAFF FOR NCHRP WEB-ONLY DOCUMENT 436

Monique R. Evans, Director, Cooperative Research Programs

Waseem Dekelbab, Deputy Director, Cooperative Research Programs, and Manager, National Cooperative Highway Research Program

Roberto Barcena, Senior Program Officer

Anthony Avery, Senior Program Assistant

Natalie Barnes, Director of Publications

Heather DiAngelis, Associate Director of Publications

Jennifer Correro, Assistant Editor

NCHRP PROJECT 03-144 PANEL
Field of Traffic – Area of Operations and Control

Philomena (Mena) Lockwood , Virginia Department of Transportation, Richmond, VA (Chair)

Ijaz Ali, Transportation Design and Engineering Services Pvt. Ltd., Abbottabad

Vicky Calderon, Idaho Transportation Department, Boise, ID

William Morgan, Illinois Department of Transportation, Springfield, IL

Chade Saghir, Southeast Michigan Council of Governments, Detroit, MI

Walter During, FHWA Liaison

AUTHOR ACKNOWLEDGMENTS

The research reported herein was performed under NCHRP Project 03-144 by the Texas A&M Transportation Institute, a member of The Texas A&M University System. The research team extends its gratitude to all the transportation agencies and organizations that contributed to this study.

We would like to specifically thank the following agencies for participating in interviews and sharing their experiences with the research team: City of Bentonville, City of Fremont, City of Minneapolis, City of Roseville, City of San Diego, Hennepin County, Maricopa Association of Governments, Minnesota Department of Transportation, North Carolina Department of Transportation, Oregon Department of Transportation, and Pima Association of Governments. Their feedback helped shape our understanding of current practices and guided the direction of our research.

In addition, we are appreciative of many agencies and organizations that generously provided us with data, enabling a richer and more comprehensive validation of different types of signal equipment. These agencies are: City of Phoenix, City of Tempe, City of Tucson, Maricopa County, Pima County, City of Roseville, City of Winnipeg, Martin County, Georgia Department of Transportation, City of Chicago, Montgomery County, Oakland County, Hennepin County, Minnesota Department of Transportation, City of Wilmington, North Dakota State University, Ohio Department of Transportation, City of Medford, Oregon Department of Transportation, Pennsylvania Department of Transportation, Utah Department of Transportation, Virginia Department of Transportation, Washington State Department of Transportation, City of Tucson, Maricopa County, Pima County, City of Winnipeg, City of Chicago, Montgomery County, City of Wilmington, Ohio Department of Transportation, Oregon Department of Transportation, Pennsylvania Department of Transportation, Utah Department of Transportation, and Washington State Department of Transportation.

The research team would also like to thank Orva Agbe (University of Arizona) and Bharat Kumar Pathivada (University of Arizona) for their advice and support during the project.

CONTENTS

List of Figures

List of Tables

List of Acronyms

Chapter 1. Introduction

Background

Scope and Organization

Benefits

Traffic Operations

Traffic Monitoring

Target Audience

Research Project

Technology Comparison

Chapter 2. Inductive Loop Detectors

Introduction

Strengths and Weaknesses

Recommended Practices

Installation and Materials

Calibration and Maintenance

Location, Shape, and Number

Chapter 3. Video-Based Systems

Introduction

Strengths and Weaknesses

Recommended Practices

Installation and Calibration

Equipment Characteristics

Maintenance

Chapter 4. Microwave Radar Sensors

Introduction

Strengths and Weaknesses

Recommended Practices

Installation and Calibration

Maintenance

Chapter 5. LiDAR Sensors

Introduction

Strengths and Weaknesses

Recommended Practices

Installation and Placement

Calibration and Maintenance

Chapter 6. Infrared Sensors

Introduction

Strengths and Weaknesses

Recommended Practices

Installation and Placement

Calibration and Maintenance

Chapter 7. Magnetic Sensors

Introduction

Strengths and Weaknesses

Recommended Practices

Installation and Calibration

Maintenance

Chapter 8. Ultrasonic Sensors

Introduction

Strengths and Weaknesses

Recommended Practices

Installation and Placement

Calibration and Maintenance

Chapter 9. Other Technologies

Push Buttons

Strengths and Weaknesses

Other Sensors

Chapter 10. Data Management

Storage Format

Communications and Storage

Quality and Troubleshooting

Procurement Considerations

Initial Validation

Ongoing Data Quality Checks

System Improvements and Audits

Urban versus Rural Traffic Count Data Collection

Urban Traffic Count Data Collection

Rural Traffic Count Data Collection

References

LIST OF FIGURES

Figure 1. Main Components of Inductive Loop Detectors (Lamas-Seco et al., 2016).

Figure 2. Examples of Short and Long Inductive Loop Shapes (Klein et al., 2006).

Figure 3. Example of an Inductive Loop for Bicycles Located on the Shoulder (Source: Oregon DOT).

Figure 4. Example of Benchmark and Loop Motorized Counts from Advance and Stop-Bar Detectors at Various Approaches in Washington.

Figure 5. Bicycle Detector Pavement Marking (FHWA, 2023).

Figure 6. Video-Based Sensors (Pictures (d) and (e) Courtesy of Cubic Corporation).

Figure 7. Example of a Virtual Detector Layout Used in a Video-Based Sensor (Wu et al., 2021).

Figure 8. Examples of Occupied (Green) and Unoccupied (Black) Virtual Detectors in a Video-Based System (Pudasaini et al., 2023).

Figure 9. Examples of Occupied (Green) and Unoccupied (Yellow) Virtual Detectors in a Video-Based System.

Figure 10. Tracking Vehicles from Video (Li et al., 2020).

Figure 11. Example of CCTV-Based System Identifying and Classifying Vehicles (Fedorov et al., 2019).

Figure 12. Example of Automatic Pedestrian Detection in a Video-Based System (Ling et al., 2010).

Figure 13. Example of Motorized and Non-Motorized Traffic Detection in a Video-Based System.

Figure 14. Examples of Occlusion Due to Large Vehicles Blocking Smaller Vehicles.

Figure 15. Examples of Rain Affecting Visibility and Quality of Video.

Figure 16. Examples of Building and Vehicle Shadows at Different Times of Day.

Figure 17. Examples of Tree Shadows in the Right Lane (Chen and Hu, 2020).

Figure 18. Examples of Sun Glare Reducing Visibility and Quality of Video.

Figure 19. Examples of Vehicle Headlights Reducing Visibility and Quality of Video.

Figure 20. Example of Vehicle Reflection Caused by Sunlight.

Figure 21. Example of Dirty Lens Obstructing Part of Camera View.

Figure 22. Examples of Fixed Objects Blocking the Cameras’ Field of View.

Figure 23. Examples of Poor Contrast Between Vehicles and Road.

Figure 24. Hourly Benchmark Volumes versus Signal Volumes Across All Study Intersections Equipped with Video-Based Systems.

Figure 25. Hourly Benchmark Volumes versus Signal Volumes by Agency.

Figure 26. (a) and (b) Camera Installed by TTI to Count Non-Motorized Traffic at University Drive and Church Avenue in College Station, TX; (c) Aerial Map of the Intersection (Source: Google Maps).

Figure 27. Vehicle Detection with Microwave Radar (Klein et al., 2006).

Figure 28. Radar Sensors Installed on a Traffic Signal Mast Arm, San Antonio, TX.

Figure 29. Radar Sensor Installed on a Traffic Signal Pole, San Antonio, TX.

Figure 30. Benchmark Volumes versus Radar Sensor Volumes by Lane Across All Study Sites.

Figure 31. Examples of LiDAR Sensors (Pictures (a) and (b) Courtesy of UDOT).

Figure 32. Example of Two Undetectable Areas (Adapted from Zhao et al., 2020).

Figure 33. Examples of Thermal Sensors.

Figure 34. (a) and (b) Examples of Magnetometers; (c) and (d) Control Cabinet and Hardware; (e) Example of Two Magnetic Sensors Installed in the Right-Turn Lane of an Intersection (Pictures Courtesy of Oakland County).

Figure 35. Magnetic Sensor Components and Installation Characteristics (Neudorff et al., 2003).

Figure 36. Vehicle Detection System: (a) Processing Unit; and (b) and (c) Two Ultrasonic Sensors (Stiawan et al., 2019).

Figure 37. (a) and (c) Pedestrian Push Buttons; (b) Pedestrian Signal Actuation Data by Time of Day (Shah et al., 2020).

Figure 38. Scatterplot of All Push-Button Data Validated in NCHRP 03-144.

Figure 39. Example of Push-Button Scatterplot with Low Pedestrian Volumes.

Figure 40. Example of Push-Button Scatterplot with High Pedestrian Volumes.

Figure 41. Communication Tools for Cabinets: (a) Cellular Router, and (b) Fiber Optic Converter.

Figure 42. Illustration of a Cloud Communications Environment (Balke et al., 2023).

LIST OF TABLES

Table 1. Signal Technology Comparison.

Table 2. Strengths and Weaknesses of Inductive Loop Detectors.

Table 3. Strengths and Weaknesses of Video-Based Systems.

Table 4. Strengths and Weaknesses of Microwave Radar Sensors.

Table 5. Strengths and Weaknesses of LiDAR Sensors.

Table 6. Strengths and Weaknesses of Active Infrared Sensors.

Table 7. Strengths and Weaknesses of Passive Infrared Sensors.

Table 8. Strengths and Weaknesses of Magnetic Sensors.

Table 9. Strengths and Weaknesses of Ultrasonic Sensors.

Table 10. Strengths and Weaknesses of Pedestrian Push Buttons.

Table 11. Strengths and Weaknesses of Different Data Storage Formats.

LIST OF ACRONYMS