Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

PUBLICATION NO. FHWA-HRT-22-091 DECEMBER 2022 

Cooperative Automation Research: 

CARMA Proof-of-Concept TSMO 

Use Case Testing: Traffic Incident 

Management Concept of Operations 
FOREWORD

The Federal Highway Administration’s (FHWA) Cooperative Driving Automation (CDA) 

Program, formerly known as the CARMA Program, is an initiative to enable collaboration for 

research and development of CDA technologies. The CDA Program develops and maintains an 

ecosystem of open-source software tools, which together are known as the CARMA Ecosystem, 

to enable CDA research. The CARMA Ecosystem uses communication between vehicles and 

roadside infrastructure devices to support coordinated movement to improve safety, traffic 

throughput, and energy efficiency of the transportation network. 

In 2015, the FHWA’s Office of Operations Research and Development developed a cooperative 

adaptive cruise control proof-of-concept prototype that was installed in five research vehicles. 

From there, the CARMA Ecosystem further evolved through testing and integration. At the time 

of this writing, the CDA Program is advancing into automated driving systems (ADS) that 

leverage infrastructure to support cooperative automation strategies. This project expands 

CARMA functionality to include transportation systems management and operations (TSMO) 

strategies on surface arterials with intersections.

This concept of operations is the seventh in a series of nine focused on TSMO use cases (UC) 

and capabilities. It focuses on traffic incident management UCs where traffic and incident 

response vehicles are actively managed by CARMA tools through advanced signal operations. 

The intended audience for this report is CDA stakeholders such as system developers, analysts, 

researchers, application developers, and infrastructure owners and operators. 

Brian P. Cronin, P.E. 

Director, Office of Safety and Operations

Research and Development 

Notice

This document is disseminated under the sponsorship of the U.S. Department of Transportation 

(USDOT) in the interest of information exchange. The U.S. Government assumes no liability for 

the use of the information contained in this document.

The U.S. Government does not endorse products or manufacturers. Trademarks or 

manufacturers’ names appear in this report only because they are considered essential to the 

objective of the document. 

Quality Assurance Statement

The FHWA provides high-quality information to serve Government, industry, and the public in a 

manner that promotes public understanding. Standards and policies are used to ensure and 

maximize the quality, objectivity, utility, and integrity of its information. FHWA periodically 

reviews quality issues and adjusts its programs and processes to ensure continuous quality 

improvement.
TECHNICAL REPORT DOCUMENTATION PAGE

1. Report No.

FHWA-HRT-22-091

2. Government 

Accession No.

3. Recipient’s Catalog No.

4. Title and Subtitle

Cooperative Automation Research: CARMA 

Proof-of-Concept TSMO Use Case Testing: Traffic Incident 

Management Concept of Operations

5. Report Date

December 2022

6. Performing Organization Code

7. Author(s)

Larry Head (ORCID: 0000-0001-6424-263X), Jonathan 

Sprinkle (ORCID: 0000-0003-4176-1212), Amir Ghiasi 

(ORCID: 0000-0002-0986-9840), Govindarajan Vadakpat 

(HRSO-20; ORCID: 0000-0001-9060-3216), Sujith Racha 

(ORCID: 0000-0002-5217-0779), Pavle Bujanovic (HRSO10; ORCID: 0000-0001-6589-3207)

8. Performing Organization Report No.

9. Performing Organization Name and Address 10. Work Unit No.

Leidos Inc.

11251 Roger Bacon Drive

Reston, VA 20190

University of Arizona

1127 E James E Rogers Way

Tucson, AZ 85721

11. Contract or Grant No.

DTFH6116D00030L (TO 19-360) 

12. Sponsoring Agency Name and Address

U.S. Department of Transportation

Federal Highway Administration

1200 New Jersey Avenue, SE

Washington, DC 20590

13. Type of Report and Period Covered

Final Report

September 2020–December 2022

14. Sponsoring Agency Code

HRDO

15. Supplementary Notes

The Government Task Managers were Govindarajan Vadakpat (HRSO-20; ORCID: 0000-0001-9060-3216), Pavle 

Bujanovic (HRSO-10; ORCID: 0000-0001-6589-3207), Taylor Lochrane (HRSO-10; ORCID: 0000-0002-1933-

6554), and James Austrich. 

16. Abstract

Cooperative driving automation (CDA) aims to improve the safety, traffic throughput, and energy efficiency of the 

transportation network by allowing vehicles and infrastructure to work together to coordinate movement. The 

CARMA Ecosystem is utilized to research CDA and leverage emerging capabilities in automation and cooperation to 

advance transportation systems management and operations (TSMO) strategies. The objective of this project is to 

develop the CARMA Ecosystem to enhance infrastructure performance, improve network efficiency, strategically 

reduce arterial traffic congestion, and enable CDA participants with further capabilities to interact with road 

infrastructure. The concept of operations (ConOps) discussed in this report focuses on TSMO traffic incident 

management (TIM) use cases where traffic and incident response vehicles are actively managed by CARMA tools 

through signal optimization and signal coordination. The strategies in this ConOps are expected to help reduce travel 

time delay caused by traffic signals, improve safety, and reduce incident response travel time for incident response 

vehicles. The proposed approach for the use cases has two components. First, local traffic signal optimization using 

CARMA Streets to improve safety and reduce delay for one or more incident response vehicle. Second, corridorcoordinated TSMO TIM using CARMA Cloud to optimize signal timing in a corridor. 

17. Key Words

C-ADS-equipped vehicle; signalized intersection; 

cooperative driving automation; CARMA; signal 

optimization, traffic incident management, traffic 

signal preemption

18. Distribution Statement

No restrictions. This document is available to the public 

through the National Technical Information Service, 

Springfield, VA 22161.

http://www.ntis.gov

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages

64

22. Price

N/A

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized.
ii
iii

TABLE OF CONTENTS

CHAPTER 1. INTRODUCTION ................................................................................................ 1

Identification .......................................................................................................................... 1

Document Overview................................................................................................................ 1

Background ......................................................................................................................... 1

Objective ............................................................................................................................. 2

Audience ............................................................................................................................ 2

Document Structure ........................................................................................................... 3

CHAPTER 2. CURRENT SITUATION AND OPPORTUNITIES FOR CHANGES ........... 5

Background and Current Situation ..................................................................................... 5

Opportunities for Change ..................................................................................................... 9

TSMO Stakeholders ............................................................................................................. 15

Transportation Users ......................................................................................................... 15

IOOs .................................................................................................................................. 17

Justification for and Nature of Changes ............................................................................. 18

Organizational and Institutional Changes ......................................................................... 18

Technical and Technological Changes ............................................................................ 20

Operational Policy Changes ............................................................................................. 21

Facility Infrastructure Changes ........................................................................................ 21

CHAPTER 3. OPERATIONAL CONCEPT OF THE PROPOSED SYSTEM .................. 23

TECHNOLOGICAL FRAMEWORK FOR TSMO TIM BAT UCS .............................. 23

TSMO UC EVA-1: Move Over ........................................................................................ 25

TSMO UC EVA-2: Yield the Right-of-Way .................................................................... 26

TSMO UC PREEMPT-1: Single-Intersection, Single Incident Response Vehicle Traffic 

Signal Preemption Requests ............................................................................................ 26

TSMO UC PREEMPT-2: Single-Intersection, Multiple Incident Response Vehicle 

Traffic Signal Preemption ................................................................................................ 27

TSMO UC PREEMPT-3: Multiple-Intersection, Multiple Incident Response Vehicle 

Traffic Signal Preemption ................................................................................................ 28

TSMO UC PREEMPT-4: Queue Clearance ..................................................................... 29

TSMO UC PREEMPT-5: Single-Lane Queue Clearance ............................................... 30

Infrastructure Configuration and Needs ............................................................................ 31

Summary of TSMO Needs and Requirements ................................................................... 33

Performance Metrics and Target Traffic Flow .................................................................. 36

Performance Metrics for Traveler Experience ................................................................. 36

Performance Metrics on Traffic Performance ................................................................. 37

CHAPTER 4. OPERATIONAL SCENARIOS ........................................................................ 39

Scenario 1: Arterial With Multiple Incident Response Vehicles ...................................... 39

Scenario 2: Route-Based Priority With Lane-by-Lane Traffic Signal Preemption and 

Yield the Right-of-Way......................................................................................................... 40

Scenario 3: Work Zone Incident Response ....................................................................... 42

CHAPTER 5. ANALYSIS OF THE PROPOSED SYSTEM ................................................. 45

Summary of Potential Benefits and Opportunities ............................................................ 45
iv

System Validation Plan ....................................................................................................... 45

Simulation Testing ............................................................................................................ 45

Field Testing .................................................................................................................... 46

Summary of Impacts ............................................................................................................ 46

Disadvantages and Limitations ......................................................................................... 46

CHAPTER 6. SUMMARY AND CONCLUSION ................................................................... 49

REFERENCES ........................................................................................................................... 51
v 

LIST OF FIGURES

Figure 1. Table. “Relationship Between Classes of Cooperative Driving Automation 

(CDA) J3216 and Levels of Automation J3016.” ................................................................. 9

Figure 2. Schematic Diagram. CARMA Ecosystem. .................................................................. 11

Figure 3. Diagram. CARMA Cloud components. ....................................................................... 12

Figure 4. Flowchart. Software architecture of MMITSS VSP. .................................................... 13

Figure 5. Flowchart. Software architecture of MMITSS MRP. .................................................. 14

Figure 6. Illustration. Arterial traffic signal corridor with incident response operations. ........... 24

Figure 7. Illustration. TSMO UC EVA-1: Move over. ................................................................ 25

Figure 8. Illustration. TSMO UC EVA-2: Yield the right-of-way. ............................................. 26

Figure 9. Illustration. TSMO UC PREEMPT-1: Single-intersection, single incident 

response vehicle traffic signal preemption. ......................................................................... 27

Figure 10. Illustration. TSMO UC PREEMPT-2: Single-intersection, multiple incident 

response vehicle traffic signal preemption. ......................................................................... 28

Figure 11. Illustration. TSMO UC PREEMPT-3: Multiple-intersection, multiple incident 

response vehicle traffic signal preemption. ......................................................................... 29

Figure 12. Illustration. TSMO UC PREEMPT-4: Queue clearance. ............................................ 30

Figure 13. Illustration. TSMO UC PREEMPT-5: Single-lane queue clearance. ......................... 31

Figure 14. Illustration. Scenario 1: Basic arterial with multiple incident response vehicles. ....... 39

Figure 15. Illustrations. Route-based priority with lane-by-lane traffic signal preemption 

and yielding of right-of-way. ............................................................................................. 41
vi 

LIST OF TABLES

Table 1. Projects associated with this development effort. ............................................................ 2

Table 2. Examples of cooperative signalized intersection features. ............................................. 10

Table 3. Transportation user characteristics and needs. .............................................................. 16

Table 4. Infrastructure needs and responsibilities for road users (i.e., CDA vehicles) and 

IOOs. .................................................................................................................................. 32

Table 5. Exchanges between RSE and vehicles. .......................................................................... 32

Table 6. Operational needs for vehicles and infrastructure in TSMO TIM BAT UCs. ................ 33

Table 7. Functional requirements for vehicles and infrastructure in TSMO TIM UCs. ............... 35

Table 8. Summary of performance metrics for TSMO TIM BAT UC evaluation. ..................... 38

Table 9. Summary of TSMO TIM BAT UCs. .............................................................................. 49
vii 

LIST OF ABBREVIATIONS

ACC standard adaptive cruise control

ADS automated driving system

BAT basic arterial travel

BSM basic safety message

CACC cooperative adaptive cruise control

C-ADS cooperative automated driving system 

CDA cooperative driving automation

ConOps concept of operations 

C-V2X cellular vehicle-to-everything 

DSRC dedicated short-range communication

ETA estimated time of arrival

EVA emergency vehicle alert

EVRT emergency vehicle response time

EVSP emergency vehicle signal preemption

FHWA Federal Highway Administration

GPS Global Positioning System

HRDO Office of Operations Research and Development

Hz hertz

I2V infrastructure-to-vehicle

ID identifier

IEC International Electrotechnical Commission

IEEE Institute of Electrical and Electronics Engineers

IOO infrastructure owner and operator 

ISO International Organization for Standardization

MMITSS Multimodal Intelligent Traffic Signal System

MRP MMITSS roadside processor

MUTCD Manual on Uniform Traffic Control Devices 

NHTSA National Highway Traffic Safety Administration

NTCIP Transportation Communications for Intelligent Transportation System Protocol 

OBU onboard unit 

PREEMPT traffic signal preemption in use cases

RSE roadside equipment

RSU roadside unit 

SPaT Signal Phase and Timing

SRM signal request message

SSM signal status message

STOL Saxton Transportation Operations Laboratory 

TFHRC Turner-Fairbank Highway Research Center

TIM traffic incident management

TSMO transportation systems management and operations 

UC use case

USDOT U.S. Department of Transportation

V2I vehicle-to-infrastructure

V2V vehicle-to-vehicle
viii

V2X vehicle-to-everything 

VSP vehicle-side processor

WSN wireless sensor network
1 

CHAPTER 1. INTRODUCTION

IDENTIFICATION

This document is a concept of operations (ConOps) for a transportation systems management 

and operations (TSMO) use case (UC) on arterials. This ConOps focuses on traffic incident 

management (TIM).

DOCUMENT OVERVIEW

Background 

The Office of Safety and Operations Research and Development (HRSO) performs 

transportation operations research and development for the Federal Highway Administration 

(FHWA). On-site research and development are conducted at the Saxton Transportation 

Operations Laboratory (STOL) established at Turner-Fairbank Highway Research Center 

(TFHRC). HRDO conducts operations research and development based on the transportation 

needs of the United States.

In 2015, FHWA designed, built, and installed a cooperative adaptive cruise control (CACC) 

proof-of-concept prototype system in a fleet of five vehicles. The CACC system was built on the 

CARMA Platform, as an advancement of standard adaptive cruise control (ACC) systems.It

utilized vehicle-to-vehicle (V2V) dedicated short-range communications (DSRC) to 

automatically synchronize the longitudinal movements of many vehicles within a string. This 

proof-of-concept system was the first in the United States to demonstrate the capabilities of this 

technology with a five-vehicle CACC string.

A subsequent task order sought to develop a new reference platform, CARMA2, using the Robot 

Operating System to enable easy sharing and integration of research outcomes into industry 

research vehicles.

(1)

 The project advanced CACC functionality and led to the development of a 

proof-of-concept platooning application that enabled leader-follower behavior and allowed 

vehicles to begin to negotiate with one another. Additionally, the project led to the development 

of the Integrated Highway Prototype 1, which integrated speed harmonization, lane changing and 

merging, and platooning into one trip. 

Following CARMA2, a current task order is producing the third iteration of the CARMA 

Ecosystem, CARMA3, which enters into the world of automated driving systems (ADSs) with 

SAE International (SAE) Level 3 and above automation.(2) The approach takes advantage of an 

open-source ADS platform, Autoware®, to enable the use of ADS functionality for cooperative 

automation strategies.

In addition to CARMA Platform, CARMA Messenger, CARMA Streets, and CARMA Cloud are 

also being developed. CARMA Messenger represents the capability of moving but not 

automated entities (e.g., first-responder vehicles, pedestrians, and buses) to communicate with 

CARMA-equipped vehicles and infrastructure to improve the performance of the network. 

CARMA Streets represents the infrastructure piece of cooperative driving automation (CDA) at 

conflict areas (e.g., intersections). It provides an interface to roadside units (RSUs), supports 
2 

two-way communication between vehicles and infrastructure, and enables CDA by using edge 

computing to optimize travel through conflict areas. CARMA Cloud further supports regional 

TSMO through the cloud-based management of transportation systems, data exchange, and 

multiple simultaneous remote services. All CARMA software products (i.e., CARMA Platform, 

CARMA Cloud, CARMA Messenger, and CARMA Streets) are open source and are built with 

the goal of benefitting CDA research. Table 1 lists the various projects associated with this 

development. 

Table 1. Projects associated with this development effort.

Task Order Product Title

STOL I T-13005 CARMA Development of a Platform Technology for 

Automated Vehicle Research

STOL II 0013 CARMA2 Development of Connected and Automated 

Vehicle Capabilities: Integrated Prototype I

STOL II 693JJ318F000225 CARMA3 Development of Cooperative Automation 

Capabilities: Integrated Prototype II

STOL II 693JJ319F000369 CARMA IHP2 Cooperative Automation Research: CARMA 

Integrated Highway Prototype II 

Objective

This ConOps extends the research from Prototype II by utilizing CARMA Streets, CARMA 

Platform, and CARMA Cloud to enable further capabilities of CDA participants to interact with 

road infrastructure, including traffic signal controllers. All TSMO TIM UCs in this ConOps 

consider CDA operations on at-grade intersections. This ConOps discusses TSMO TIM Basic 

Arterial Travel (BAT) UCs that focus on active traffic management by incorporating signal 

optimization, signal coordination, and incident response vehicle management. This project 

addresses two high-level objectives: improving safety and improving incident response vehicle 

travel time. This project investigates to what extent these objectives can be achieved for the 

different CDA cooperation classes defined by the SAE standard Taxonomy and Definitions for 

Terms Related to Cooperative Driving Automation for On-Road Motor Vehicles

J3216_202107.(3)

Audience

The intended audience for this ConOps is as follows: 

• U.S. Department of Transportation (USDOT) and cooperative automation program 

stakeholders, including the Federal Transit Administration and Federal Emergency 

Management Agency.

• Academia stakeholders, including faculty, researchers, and students. 

• Private sector stakeholders, including consultant companies and original equipment 

manufacturers.
3 

• System developers, including those who create and support CDA algorithms based on the 

system concepts described in this document. 

• Analysts, researchers, and CDA application developers. 

Document Structure

The structure of this document is generally consistent with the System Operational Concept 

described in Annex A of 2011 International Standard—Systems and Software Engineering—Life 

Cycle Processes—Requirements Engineering ISO [International Organization for 

Standardization]/IEC [International Electrotechnical Commission]/IEEE [Institute of Electrical 

and Electronics Engineers] 29148: 2011.(4) A document conforming to this structure is called a

ConOps in U.S. transportation systems engineering practice, and that title is retained for this 

document. Some sections of this ConOps have been enhanced to accommodate more detailed 

content than is described in the ISO/IEC/IEEE 29148: 2011. Titles of some sections may have 

been edited to capture details more specifically.

Chapter 1 defines the scope of the ConOps. 

Chapter 2 describes the current situation and identifies needs for changes with respect to 

processes and systems to be affected by the ConOps.

Chapter 3 describes the concept for the new TSMO TIM BAT system capabilities and their 

operations and presents detailed descriptions of operational concepts. 

Chapter 4 describes operational scenarios of TSMO TIM BAT at signalized intersections.

Chapter 5 provides an analysis of expected improvements, operational and research impacts, 

validation plans, disadvantages, and limitations. 

The reference section provides a list of reference documents.

5 

CHAPTER 2. CURRENT SITUATION AND OPPORTUNITIES FOR CHANGES

This chapter discusses existing approaches to TIM on signalized arterials using traffic signal 

preemption. TIM is an exception to normal traffic operations. Incident response vehicles are 

traveling to an incident location to save lives—travel time is key, but safety is the highest 

priority. This chapter will highlight the advantages and disadvantages of existing solutions that 

motivate the development of new CDA solutions to address efficiency and safety of incident 

response vehicles at signalized intersections.

BACKGROUND AND CURRENT SITUATION

Various roadway facilities intersect through the roadway network to give commuters access, 

causing conflicts among vehicles from various movement traffic streams. Inappropriate 

operations at conflict areas (e.g., signalized or unsignalized intersections and merging roadways) 

result in unstable traffic flow (i.e., stop-and-go traffic), which may exacerbate travel delay, 

energy consumption and emissions, driving discomfort, and safety risks. Operations of conflict 

movements at a common conflict area may change in the advent of CDA technology. 

Cooperative automated driving system- (C-ADS-) equipped vehicles have communication and 

automation technologies that enable vehicles to coordinate with each other and with 

infrastructure to improve safety and maximize network efficiency.

C-ADS-equipped infrastructure components such as CARMA Streets enable infrastructure to 

actively participate in the coordination of vehicle needs—especially special classes of vehicles, 

including incident response vehicles. C-ADS-equipped vehicles and intersections are part of a 

connected ecosystem that relies on V2V, vehicle-to-infrastructure (V2I), and infrastructure-tovehicle (I2V) communications. In this ecosystem, each component plays a role in helping to 

improve the network. For example, facilities at a common conflict area can be equipped with 

traffic sensors, edge processors, and communication networks (e.g., DSRC systems) to help 

support C-ADS-equipped vehicle coordination. 

A connected ecosystem combined with the current level of vehicle automation provides 

opportunities for traffic flow improvements at common conflict areas. Such improvements may 

produce mobility, increase safety, and improve mobility. These emerging technologies can 

further help to improve the passing sequence of C-ADS-equipped vehicles at an intersection with 

proper coordination (e.g., allowing movements without conflict to occur simultaneously instead 

of allowing only one vehicle at a time to proceed at an intersection) to increase traffic 

throughput. 

Vehicles can be aware of downstream traffic and conflict area conditions to determine the

approximate time they can enter a conflict area. Special classes of vehicles, including incident 

response vehicles (e.g., fire response, ambulance, and law enforcement units), may receive 

preferential treatment by clearing queues or crossing the stop bar earlier than they would have 

without preferential treatment. Among CDA applications related to conflict areas, control 

strategies near signalized intersections have received attention due to increased capabilities to

communicate with traffic signal controllers and receive real-time Signal Phase and Timing 

(SPaT) information. These control strategies usually have two aspects. First, the traffic signal 
6 

timing plan can be optimized to efficiently serve different traffic approaches according to their 

demands. Second, C-ADS-equipped vehicles can be controlled simultaneously to improve safety 

and increase mobility. 

Traffic signal control is responsible for regulating traffic flow in a signalized intersection and for 

improving traffic mobility and safety. Traffic signal control can also be operated to provide 

right-of-way priority to incident response vehicles such as ambulances, fire trucks, and police 

cars. According to the Manual on Uniform Traffic Control Devices (MUTCD), a traffic signal 

controller can alter the regular signal timing and phasing to provide right-of-way for incident 

response.(5) The alternative signal timing and phasing may either extend the currently displayed 

green interval or replace the entire set of signal phases and timing depending on the emergency 

vehicle’s requested signal phase (i.e., approach). An incident response vehicle sends a 

preemption request to a traffic signal controller by using optical, acoustic, special inductive loop, 

or Global Positioning System (GPS) technology.(6) Traffic signal cabinets are equipped with an 

emergency vehicle preemption device to receive the preemption request. In 1970, Long 

developed a preemption system in which incident response vehicles transmit an optical signal 

and a receiver detects the presence of incident response vehicles.(7) The detector then commands 

the signal controller to flash a green light for requested phases.

Incident response vehicles need to respond to emergency incidents as quickly as possible. 

Statistics indicate that slow responses to emergency calls increase casualty rates. About 10 

percent of casualties occur within a few minutes or even seconds after a crash.

(8,9)

 Survival rates 

fall 7–10 percent with every elapsed minute following a crash.

(10)

 Incident response vehicles 

experience delays at intersections due to waiting in long queues and stopping at traffic signals to 

ensure safe right-of-way.

To improve the mobility of incident response vehicles, especially fire response vehicles, an 

emergency vehicle signal preemption (EVSP) system was developed by Gordon, Tighe, and 

Siemens.

(11) The EVSP system provides a wave of green lights to incident response vehicles

when the first responders manually push a button as they depart for an incident. The green wave 

remains active along the response route until the incident response vehicle passes each 

intersection. The traffic signal returns to its normal state after a fixed amount of time. Qin and 

Khan modified the EVSP system to implement a real-time control strategy instead of fixed-time 

phase holds.(12) The real-time control strategy detects the presence of an incident response

vehicle using connected vehicle technology and allocates a maximum green time required for the 

response vehicle to pass through an intersection.

Shaaban et al. proposed an effective signal preemption and path selection strategy.(13) An optimal 

path is selected from the starting location of an incident response vehicle to the destination of the 

emergency call. This system uses V2V and V2I technology to notify other vehicles about the 

incident response vehicle information, which may help to clear any traffic queue before the 

incident response vehicle arrives at the intersection. C.Y. Chen, P.Y. Chen, and W.T. Chen 

claim that the shortest path cannot ensure the earliest arrival time.(13) The incident response 

vehicles can respond to the emergency call more quickly by traveling through the path with 

better traffic conditions. Subsequently, an incident response vehicle path selection algorithm 

which considers current and historical traffic information was developed by C.Y. Chen, P.Y. 

Chen, and W.T. Chen.(14)
7 

Emergency vehicle response time (EVRT) is another traffic signal control method for emergency 

vehicle preemption. The EVRT strategy utilizes connected vehicle technology to predict queue 

length and provide early green lights to clear queues at downstream intersections to improve the 

mobility of emergency vehicles.

(15)

 To predict queue length, the EVRT strategy assumes 100 

percent penetration of connected vehicles. 

Most traffic signal preemption control methods generally serve only one approach to an 

intersection, for one or more emergency vehicles that are traveling on the same path. When 

multiple emergency vehicles simultaneously arrive from different approaches, current traffic 

signal control generally follows first-come-first-serve rules.

(16) However, there are some

mechanisms where one vehicle can override others.

There have been crashes where two incident response vehicles collided in an intersection.

(17,18)

D. Gross and J. Gross proposed a system that provides early warnings of incident response 

vehicle arrival and departure and direction of approach.(19) The incident response vehicle uses a 

transceiver to transmit its travel direction. The transceiver also receives the travel direction 

messages of other incident response vehicles within range of the transceiver. This system allows 

incident response vehicle drivers to determine the possibility of collisions with other incident 

response vehicles. 

Goel, Ray, and Chandra developed an intelligent traffic signal system where a wireless sensor 

network (WSN) is employed to communicate between intersections.(20) The WSN detects the 

incident response vehicle based on its emitted sounds. The sensor sends the information to the 

next intersection over the WSN. If multiple incident response vehicles are approaching from 

different directions, the intersection follows first-come, first-serve rules.

In the United States, it is challenging for traffic engineers to ensure the safe passage of incident 

response vehicles. From 1996 to 2015, an average of 355 fatalities per year occurred in crashes 

related to police vehicle pursuits.(21)

 According to Fahy and Bui et al., from 1997 to 2006 

approximately 94 fatalities resulted from collisions between fire response vehicles and other 

vehicles, pedestrians, and bicyclists. (22,23) The National Highway Traffic Safety Administration 

(NHTSA) released a report that analyzed ambulance crashes from 1992 to 2011.(21)

 The report 

found that ambulances are involved in approximately 6,500 crashes each year. The report also 

found that nearly 60 percent of ambulance crashes occur while the vehicles are in emergency 

use.

Incident response vehicles alert pedestrians and vehicles using horns, sirens, and flashing lights 

to avoid collisions.(24)

 However, excessive use of lights and sirens during emergency driving may 

cause crashes.(25,26) The lights and sirens can limit the auditory capability of emergency vehicle 

drivers and impede the vision of adjacent road users. Smith, Davidson, and Pfister developed a 

warning system that alerts vehicles and pedestrians to the direction emergency vehicles are 

approaching from.(27)

 Buchenscheit et al. used V2V and V2I communication systems to notify 

nearby vehicles about the presence, location, and speed of emergency vehicles.(28) Bui et al.

suggested that comprehensive emergency vehicle driver training and risk management strategies

may be effective in reducing emergency vehicle crashes.

(25)
8 

Traffic signal preemption cannot directly address some safety concerns. However, it may reduce 

delay and disruption to logical traffic signal control at intersections and along response routes, 

which may improve safety. Current traffic signal preemption generally overrides other traffic 

signal control strategies, such as coordination, to force off current phases and immediately 

display green signal indications ahead of emergency vehicles. One potential scenario occurs 

when other vehicles, such as freight trucks or other heavy vehicles, are also approaching a 

preempted intersection. The operating characteristics of freight trucks are different from 

passenger cars. Heavy vehicle deceleration depends on the mass, tire-pavement friction, braking 

efficiency, and reaction time of the drivers. Gates and Noyce found that, due to low deceleration 

capability, heavy vehicles are 3.6 times more likely to run red lights than passenger vehicles.(29)

Crashes may occur when the traffic signal controller attempts to terminate the current signal 

phase without considering the presence of vehicles in the dilemma zone. Tarko, Li, and 

Laracuente developed a probabilistic algorithm that identifies the optimal green extension for the 

vehicles in the dilemma zone.(30) McCoy and Pesti developed an advanced detection and warning 

system to improve dilemma-zone protection.(31) Zimmerman et al. designed a detection-control 

system, which determines the phase termination time based on the number of vehicles in the 

dilemma zone and the waiting time of vehicles at conflicting phases.(32) None of these systems 

consider the dilemma zone during a preemption event.

Incident response vehicle preemption systems disrupt normal traffic operations to provide rightof-way to incident response vehicles. Studies have analyzed the impact of traffic signal 

preemption on traffic flow. Bullock, Morales, and Sanderson; Nelson and Bullock; and Collura 

and Chang found that preemption systems can negatively impact the arterial travel time of 

responding vehicles. (33,34,35) Paruchuri developed an adaptive preemption of the traffic system 

that considers real-time traffic data to reduce vehicle delay of nonemergency vehicles.(36)

Traffic signal preemption systems aim to reduce travel time for emergency vehicles, but the 

operations and benefits are not always clear. When a vehicle requests preemption, the quick 

change in control priorities can significantly disrupt traffic flow and may create confusion that 

could impact safety. The current approach to preemption makes control decisions that are 

independent of the current traffic situation. This may lead to undesirable outcomes when 

multiple emergency response vehicles are present. For example, if the signal controllers along an 

arterial are changing to create a green pathway for a fire response vehicle without considering 

conflicting traffic, other emergency vehicles on other approaches or perhaps a freight vehicle 

(e.g., a gasoline tanker truck) may be caught in the dilemma zone and may be unable to safely 

stop. 

Das et al. presented a connected emergency vehicle application that ensures smooth progression 

of single or multiple emergency vehicles through corridors, allows safe passage of heavy 

vehicles in the dilemma zone, and improves the overall performance of traffic signals.(37) The 

system is based on the priority model described in Zamanipour et al., which is implemented in 

the Multimodal Intelligent Traffic Signal System (MMITSS) project.(17) MMITSS focuses on 

connected vehicle traffic signal applications based on V2V and V2I wireless communication 

technologies. 
SEEKING

AGREEMENT 

CLASS A

STATUS SHARING

INTENT SHARING

9 

OPPORTUNITIES FOR CHANGE

CDA that leverages V2V and V2I communications to enable vehicles to share information and 

cooperate provides opportunities to improve TIM on arterial roadways. SAE has standardized 

how cooperation between vehicles is regarded. Similar to the levels of automation defined in the 

SAE standard Taxonomy and Definitions for Terms Related To Driving Automation Systems for 

On-Road Motor Vehicles (J3016_202104), the newer standard, SAE J3216_202107, defines the 

classes of cooperation.(1) Vehicles equipped with cooperative automated driving systems 

(CADS) can share their status and driving intent (classes A and B) and seek and enter 

cooperative driving agreements (classes C and D). Figure 1 summarizes the cooperation classes 

in relation to the levels of vehicle automation.

© 2020 SAE.

1. Improved object and event detection and prediction through CDA Class A and Class B status and intent sharing

may not always be realized given that Level 1 and 2 driving automation features may be overridden by the driver at

any time, and otherwise have limited sensing capabilities compared to Level 3, 4, and 5 ADS-operated vehicles.

2. Class A and B communications are one of the many inputs to an ADS’s object and event detection and prediction

capability, which may not be improved by the CDA message.

Figure 1. Table. “Relationship Between Classes of Cooperative Driving Automation (CDA) 

J3216 and Levels of Automation J3016.”(3)

NO COOPERATIVE 

AUTOMATION 

CDA CLASSES 

Here I am 

and what 

I see 

CLASS B This is 

CLASS C 

CLASS D 

PRESCRIPTIVE

what I 

plan todo 

Let's 

do this 

together 

I will do as 

directed 

RELATIONSHIP BETWEEN CLASSES OF COOPERATIVE DRIVING 

AUTOMATION (CDA) J3216 AND LEVELS OF AUTOMATION J3016 

Partial Automation of DDT Complete Automation of DDT 

LEVEL 0 LEVEL 1LEVEL 2 LEVEL 3LEVEL 4LEVELE 5

No driving 

automation 

(human does all 

driving) 

E.g., Signage, 

TCD 

Driver assistance 

(longitudinal OR 

lateral vehicle 

motion control) 

Partial driving 

automation 

(longitudinal 

AND lateral 

vehicle motion 

control) 

Relies on driver to complete the 

DDT and to supervise feature 

performance in real time 

E.g., Brake lights, Potential for improved object and 

traffic signal event detection1

E.g., Turn signal, Potential for improved object and 

merge event detection1

E.g., Hand 

signals, merge 

E.g., Hand 

NI A 

signals, lane 

assignment by 

officials 

Conditional 

driving 

automation 

High driving 

automation 

Full driving 

automation 

Relies on ADS to perform complete DDT under defined 

conditions (fallback condition performance varies 

between levels) 

Potential for improved object and event detection2

Potential for improved object and event detection2

C-ADS designed to attain mutual goals through 

coordinated actions 

C-ADS designed to accept and adhere to a command 
10 

Table 2 shows examples of CDA features relating to cooperative traffic signals at intersections 

and considering different cooperation classes. A number of these examples are taken from SAE 

J3216_202107. However, the effects of different cooperation classes defined in SAE 

J3216_202107 have not been investigated. 

Table 2. Examples of cooperative signalized intersection features.

Feature Class of CDA

CDA Device 

Transmission Mode 

and Directionality

Information 

Exchanged Level of Functionality

Signal 

priority

A) Status 

sharing

One way: 

C-ADS-equipped 

vehicles  RSE

Vehicle location, 

speed, and 

priority status 

(e.g., incident 

response vehicle 

en-route to an 

incident).

Enabling signal timing 

changes based on the 

approaching vehicle.

Ecoapproach 

and 

departure 

A) Status 

sharing

B) Intent 

sharing

One way: RSE

C-ADS-equipped

vehicles

SPaT messages Enabling C-ADS 

equipped vehicles to plan 

their motions based on 

knowledge of a future 

signal phase that would 

otherwise be unavailable.

Tandem 

approach 

and 

departure

C) Agreement 

seeking

Two way: 

C-ADS-equipped 

vehicles  RSE

RSE 

C-ADS-equipped

vehicles

C-ADS-equipped 

vehicles 

C-ADS-equipped

vehicles

SPaT messages

Velocity profile

Negotiation 

results

Enabling SPaT changes 

based on the approaching 

vehicle.

Enabling 

C-ADS-equipped 

vehicles to plan their 

motions and optimize 

their velocity based on 

future (and possibly 

optimized) signal phases 

and the status of the 

other vehicle.

Supporting more 

efficient motion plans 

with increased reliability 

and look-ahead distance 

to reduce energy 

consumption and 

emissions.

RSE = roadside equipment. 

Note: In practice, one-way transmission will typically send the message to multiple CDA devices in the vicinity.
11 

To fill in existing research gaps, this ConOps proposes an edge computing-based cooperative 

control framework for C-ADS-equipped vehicles, including passenger vehicles and incident 

response vehicles, at a signalized intersection in the TSMO context. This ConOps is part of the 

CDA framework and distinguishes between levels of vehicle automation and classes of vehicle 

cooperation. 

The CDA framework is a platform for the research and development of emerging automated 

driving and vehicle-to-everything (V2X) communications. Figure 2 illustrates the CARMA 

Ecosystem, which is comprised of open-source software, tools to support evaluation and testing, 

and an engagement and support community. 

Source: FHWA.

Figure 2. Schematic Diagram. CARMA Ecosystem.(38)

CARMA tools provide a framework for CDA application development. CARMA Cloud provides 

an overall system-level capability to integrate TSMO strategies utilizing information from a 

variety of different systems. Figure 3 illustrates the system objects and layers that represent 

different critical TSMO systems, including work zones, traffic conditions, weather, traffic 

control, roads and bridges, and landforms. Each TSMO system contains a collection of system 

objects, which represent key entities and capabilities that create model layers of information. 

Many of the system objects are created based on standards that have been developed to support 

interoperability and uniformity across the different systems. Utilizing a cloud-based platform for 

this information integration provides capability for the development of CDA applications for 

other transportation needs, such as TIM. 
12 

Source: FHWA.

*Potential new system objects for transit management. 

**Transit communications interface protocol (TCIP). 

***Traffic Management Data Dictionary (TMDD)(39).

****MUTCD(5)

. 

WZDx = work zone data exchange; HD = high definition; Green Book = A Policy on the Geometric Design of 

Highways and Streets.

(40)

Figure 3. Diagram. CARMA Cloud components. 

CARMA Streets is a relatively new addition to the CARMA Ecosystem. It provides roadside 

edge computing that integrates communication between infrastructure and CDA-equipped 

vehicles, as well as other connected but nonautomated vehicles. CARMA Streets provides the 

capability for real-time applications as part of CDA applications. CARMA Platform is a research 

and testing platform for vehicle automation. It has been applied to demonstrate advanced CDA 

capabilities such as CACC, eco-driving, and other automated capabilities. CARMA Messenger is 

a vehicle platform for communicating with nonautomated but connected vehicles. 

In addition to CDA and the CARMA Ecosystem, the Connected Vehicle Pooled Fund has led to 

the development of MMITSS, which provides intelligent and priority-based traffic signal control 

using data from connected vehicles.(41) Figure 4 and figure 5 show the software architecture of 

MMITSS. MMITSS has components that are deployed on the roadside (i.e., roadside processor 

[MRP]), on the vehicle (i.e., vehicle-side processor [VSP]), and on a server that supports data 

archiving and a web-based user interface. The VSP and MRP both have message transceivers 

that encode the protocol for talking to the RSU and onboard unit (OBU) devices, as well as a 
13 

message library that can pack and unpack the Unaligned Packed Encoding Rules (UPER)-

encoded messages in the SAE standard Dedicated Short Range Communications (DSRC) 

Message Set Dictionary (J2735_201603).(42)

 

The VSP hosts a priority-request generator that is responsible for locating the vehicle on the map 

(based on local GPS position and map messaging received by the OBU) and broadcasting a 

signal request message (SRM). The VSP has special lights and a siren manger component that 

can sense the lights and siren circuit on an incident response vehicle such as a fire truck, 

ambulance, or police car. In addition, there is a data-capture component called V2X data client 

that archives data from the other VSP components. Since the VSP does not have a persistent 

internet connection, the V2X data compressor manages the captured data by compressing and 

deleting the oldest data to ensure device storage is adequate. Figure 4 shows the VSP architecture 

of the MMITSS software.

© 2014 Connected Vehicle Pooled Fund Study. Modified by FHWA. 

SSM = signal status message; BSM = basic safety message; HMI = Human-Machine Interface; 

HTML = Hypertext Markup Language; JSON = JavaScript Object Notation; 

CSV = comma-separated values; SNMP = Simple Network Management Protocol. 

Figure 4. Flowchart. Software architecture of MMITSS VSP.(41)

The MRP hosts the algorithms that realize the MMITSS intelligent priority control using the 

connected vehicle data. When a vehicle broadcasts an SRM, the MRP message transceiver 

forwards it to the priority request server, which is responsible for collecting and managing 
14 

requests from multiple vehicles and from the coordination request generator since MMITSS 

implements coordinated traffic signal control as a form of priority. Given a set of active priority 

requests, the priority request solver will solve an optimal scheduling problem to determine the 

desired traffic control schedule. Figure 5 shows the MRP architecture of the MMITSS software.

This schedule is sent to the traffic control interface for implementation on the traffic signal 

controller though National Transportation Communications for Intelligent Transportation System 

Protocol (NTCIP) objects (e.g., call, hold, force off, and omit). The traffic signal controller 

provides SAE SPaT message data that are modified by the MAP-SPaT broadcaster to include the 

MMITSS control schedule.(42) Currently, the trajectory-aware and intelligent traffic signal control 

applications are not utilized in MMITSS. They require a minimum of 20–30 percent market 

penetration of connected vehicles to be effective. 

© 2014 Connected Vehicle Pooled Fund Study. Modified by FHWA.

MMITSS phase 3 development, DOT Dynamic Mobility Application Program.

SSM = signal status message; BSM = basic safety message; HMI = Human-Machine Interface; HTML = 

Hypertext Markup Language; JSON = JavaScript Object Notation; CSV = comma-separated values; SNMP = 

Simple Network Management Protocol.

Figure 5. Flowchart. Software architecture of MMITSS Roadside Processor (MRP).(41)
15 

MMITSS implements both traffic signal preemption and traffic signal priority. In MMITSS, the 

flow of information from incident response vehicles is identical to other modes including transit 

and trucks. The vehicle, or another source, generates a request for priority that is added to the list 

of active requests in the MMITSS priority request server. For incident response vehicles 

(referred to as emergency vehicles in MMITSS), the priority request solver will omit phases that 

are not required for serving the active requests. For example, if an incident response vehicle 

requests priority for the major through movement at an intersection and the signal is currently 

serving the side street through movements, phases serving all the turning movements as well as 

the through movement opposing the incident response vehicle will be omitted. 

Omitting these phases limits the flow of vehicles in conflicting movements and provides green 

for the through and left-turn movements to serve the approaching incident response vehicle. If 

more than one incident response vehicle approaches the intersection from different approaches—

for example, during phase two and phase six—and phase four is active, then phases one, three, 

five, and seven will be omitted. Traffic signal priority does not consider omitting phases since it 

is intended for serving transit and trucks, which are considered a normal part of the traffic stream 

rather than exceptions to normal conditions. 

The emergence of CDA, the CARMA Ecosystem, and MMITSS provides a case for creating a 

new and effective TIM system for signalized arterials.

TSMO STAKEHOLDERS

Stakeholders are entities whose actions influence travel in the transportation environment; these 

may include transportation users engaged in travel on publicly accessible roadways, emergency 

responders, transit vehicles, transit riders, bicyclists, pedestrians, and infrastructure owners and 

operators (IOOs). This section discusses transportation users and IOOs and their corresponding 

needs.

Transportation Users

A transportation user is a traffic participant on or adjacent to an active roadway for the purpose 

of traveling from one location to another. For TSMO, motorized vehicles, including incident 

response vehicles, whether human-driven or automated, are the main users of traffic systems at 

intersections. Transportation user needs include the following: 

• Safe trips.

• Smooth, low-stress, and fast travel.

• Reliable travel times.

• Energy efficiency. 

• Accurate information to help them make optimal decisions about driving tasks (i.e., 

decision support systems). 
16 

Integrating CDA technology into TSMO, from the transportation user’s perspective, may support 

and enhance the following benefits:

• Safer travel: Providing traffic signal preemption for incident response vehicles can 

improve safety by reducing the number of vehicles involved in conflicting movements at 

a signalized intersection.

• Greater operational efficiency and travel-time reliability: Traffic signal preemption can 

improve travel time reliability by reducing traffic signal delay and allowing one or more 

incident response vehicles to pass through the intersection more efficiently. 

• Improved traffic safety: Reducing crashes can be a potential benefit of CDA technology. 

NHTSA estimates combined use of V2V and V2I communications has the potential to 

significantly reduce unimpaired driver crashes.

(43)

Table 3 identifies four transportation user categories and the characteristics and needs of each 

category.

Table 3. Transportation user characteristics and needs.

Driving 

Mode

Transportation User 

Category User Characteristics and Needs

Human 

driving

Nonconnected human 

driver 

Nonconnected human drivers have neither 

connectivity nor automation capability, and they have 

uncertain driver behavior. 

Needs align with general user needs.

Human 

driving

Incident response 

vehicle driver

Incident response vehicle operators who are highly 

trained and skilled drivers and are expected to follow 

agency operating policies. 

Human 

driving

Connected human 

driver 

Connected human drivers receive additional traveler 

information and can make better-informed travel 

decisions than nonconnected. 

Needs align with general user needs.

Automated 

driving

Nonconnected 

ADS-equipped 

vehicle 

Nonconnected, ADS-equipped vehicles operate 

independently, relying on local sensor information 

and automated control software, and usually have 

conservative behavior to provide an increased margin

of comfort and safety.

Needs include accurately sensing local traffic 

conditions and actuating control of vehicles to ensure 

safety and travel efficiency.
17 

Driving 

Mode

Transportation User 

Category User Characteristics and Needs

Automated 

driving

C-ADS-equipped 

vehicle 

Similar to ADS-equipped vehicles, C-ADS-equipped 

vehicles partner with other CDA participants in the 

traffic stream, including the infrastructure, to 

improve overall traffic performance.

Needs include availability of other vehicles to 

perform cooperative actions, improving overall 

system safety and efficiency while guaranteeing 

individual vehicle travel experiences.

IOOs

IOOs are traffic participants who provide, operate, and maintain roadways and supporting 

infrastructure for the mobility needs of transportation users. IOOs include public, public-private, 

or private sector entities that operate in accordance with applicable laws at the Federal, State, or 

local level. Incident response vehicles can be from public, public-private, or private sector 

agencies that provide a variety of services, such as fire response, ambulances, police, and towing 

and recovery. 

The goal of IOOs is safe and efficient traffic management. This includes monitoring and 

managing traffic and the factors affecting traffic flow, such as incidents, weather, intersections, 

the dissemination of routing information, and other actions that increase traffic flow efficiency. 

Goals of IOOs may also include the following:

• Reducing congestion that reoccurs. 

• Improving reliability and safety.

• Reducing travel times, fuel consumption, and emissions. 

• Maintaining and increasing use of alternative and emerging transportation modes, such as 

transit or car-sharing options. (CAVs are considered a separate mode by travelers).

From the perspective of IOOs, TSMO may provide the following benefits:

• Faster realization of efficiency goals: Timely adoption of CDA at existing intersections 

may provide faster incident response and greater congestion management abilities to 

increase throughput, enhance safety, and improve driver experience. These benefits may 

increase as the fraction of C-ADS-equipped vehicles using the intersection, out of the 

total number of users, increases. 

• Maximized resource utilization for more efficient solutions: Traditional approaches to 

managing congestion, such as capacity expansion, are increasingly facing funding 

constraints and inherent limitations in alleviating transportation problems. CDA 

technologies can be considered operational strategies that offer potential innovative 
18 

solutions to congestion and travel time variability at intersections. CDA technologies can 

be used to clear traffic congestion to allow incident response vehicles to travel faster and 

more safely.

• First-mover advantage: If operators currently primed to accommodate C-ADS-equipped 

vehicles on their roadways do not voluntarily move to test and advance this technology, 

outside actors may fill that role and dictate the direction of CDA technology 

development. This direction may or may not be in line with a specific agency’s goals or 

organizational capacity. Incident response agencies have an advantage since they manage 

their vehicle fleets and have close partnerships with traffic operation departments and 

agencies.

• Organizational evolution to accommodate the future of mobility technology: 

Organizations that respond to rapid technological change may be more likely to thrive in 

this era of rapid technological enhancement in the transportation field.

JUSTIFICATION FOR AND NATURE OF CHANGES

The transportation industry is moving toward improving safety with ADS by enhancing various 

vehicle technologies (e.g., levels of automation and ubiquitous sensing using automated vehicle 

sensors). As more advanced sensing and computing capabilities are integrated with ADS, a key 

consideration is what changes must take place to enable CDA system deployment and what 

additional capabilities and possibilities can be expected, including the deployment of smarter 

infrastructure systems that are based on CDA technologies. This section discusses the nature of 

those changes.

Organizational and Institutional Changes

The following organizational and institutional changes can be implemented to enable the 

deployment of CDA systems: 

• Adopt a systems-engineering process: A systems-engineering process is important for 

developing operational scenarios to accommodate CDA applications on intersection 

facilities. A ConOps can be developed for the system (regional level) and for the corridor 

in question. 

• Develop a performance management system: C-ADS-equipped vehicles aligned with 

agency performance standards and holistic data requirements can help transportation 

agencies leverage data sources across the organization. A performance management 

system collects and processes relevant data to determine whether system goals and 

performance targets for all CDA applications and operational alternatives are being 

achieved.
19 

• Develop a data collection and management system: All relevant data are obtained—in 

real time—from the various vehicles, onboard sensors, wireless devices, RSUs, roadway 

traffic sensors, weather systems, message boards, incident response management 

systems, and other related systems. These data can be placed in or be accessible from a 

common data environment. 

• Include a rich assortment of data: Tap into rich, accurate data from a variety of sources, 

potentially including the following: 

o Real-time traffic data—Includes vehicle speed and location data collected and 

disseminated by vehicles as part of a connected system. Also includes traditional 

detection sources (e.g., inductive loop detectors, overhead radar, and closed-circuit 

television cameras) that provide traffic data for the system.

o Traffic signal plan data—Includes planned SPaT data from traffic signal controllers at 

signalized intersections. 

o Traffic signal timing data—Includes actual SPaT data from traffic signal controllers 

at signalized intersections. Differs from planned data if control methods include 

actuated, adaptive, and priority.

o Incident response system data—Includes current location and status of incident 

response vehicles. Provides their readiness to respond to new incidents. 

o Emergency dispatch system data—Includes time and location of incidents, response 

units assigned, and other law enforcement and fire/emergency medical services data.

o Weather condition data—Provides infrastructure-based road weather information 

system and third-party weather data feeds that can supplement vehicle-acquired 

weather data.

o Pavement condition data—Provides real-time pavement surface condition data (e.g., 

dry, wet, snowy, iced, and salted) from in-pavement sensors.

o Crowdsourced data—Includes data collected from platforms that have large installed 

user bases. Supplements data from other sources.

o Historical data—Includes historical data used for the improvement of traffic analysis 

accuracy and the prediction of traffic conditions. 
20 

Technical and Technological Changes 

The following technical and technological changes can be implemented to enable the 

deployment of CDA systems. 

Procuring new hardware to support technology, as follows:

• Enabling connectivity of infrastructures at intersections through DSRC or other 

communication technologies, such as cellular vehicle-to-everything (C-V2X) and 

improving the computing power of infrastructures by installing hardware, such as edge 

processors, to support algorithms that enable CDA applications.

• Equipping vehicles that use CDA systems with communication radios (e.g., OBUs and

vehicle awareness devices), cameras, light detection and ranging technology, radar 

sensors, and other computational resources to implement the new control software.

Developing and acquiring new software, as follows:

• Making use of the frequently collected and rapidly disseminated multisource data drawn 

from connected travelers, vehicles, and infrastructure.

• Including a vehicle awareness application (e.g., an OBU installed by the vehicle 

manufacturer or as an aftermarket integrated device); a personal wireless application 

(e.g., smartphone or other handheld device); or an application that can collect, receive, 

and disseminate needed CDA data.

• Enabling systems and algorithms that can generate traffic condition predictions, 

alternative scenarios, and solution evaluations in real time. 

• Containing microscopic and macroscopic traffic simulations.

• Incorporating real-time and historical data.

• Utilizing traffic optimization models.

• Encouraging the constant evaluation, adjustment, and improvement of traffic 

optimization models (this requires an increase in computational capability and the longterm storage of historical data).

• Evolving and improving algorithms and methods based on performance measurements.

• Including emerging communication technology (e.g., DSRC or C-V2X) and software 

elements that enable the developed CDA system to act upon the received information. 
21 

Operational Policy Changes 

The operational policies of intersections are generally designed to accommodate traffic 

operations that meet the goals of operators. Key questions to determine proper operational 

policies of intersections include:

• Who are the stakeholders and users of the system?

• What are the elements and capabilities of the system?

• Where are the affected systems?

• When and where will activities be performed?

• Why are the strategies being used?

• How will the system be operated and maintained?

• How will the performance of the system be measured?

Stakeholders can create agreements or compacts to set expectations, establish incentives to 

participate, encourage investments, and measure performance. Improved throughput and 

smoother travel experience are shared goals between IOOs and CDA applications.

Facility Infrastructure Changes

Depending on the facility type, configuration, operations, and existing equipment, the following 

categories of facility infrastructure changes may be needed:

• I2V infrastructure (e.g., roadside equipment [RSE] including connected vehicle RSUs

[wireless] and edge processing devices) to transmit central information to all vehicles 

within the communication area. If nonequipped vehicles are allowed, traditional dynamic 

message signs convey public traveler information.

• Roadside sensors (e.g., video cameras, radars, and loop detectors) to detect or estimate 

real-time vehicle trajectories of nonequipped vehicles upstream of intersections. 

• Striping and pavement markings. 

• Appropriate signage to convey relevant information to all drivers (both equipped and 

nonequipped). 

For the early stages of CDA deployment, infrastructure equipped with existing communication 

devices offers the opportunity to begin integrating CDA systems into traffic. Due to the enabled 

cooperation capabilities, even a small number of C-ADS-equipped vehicles may impact traffic 

operations at intersections, and therefore improve system performance and the individual 

traveler’s experience. Incident response fleets are attractive candidates as early adopters of CDA 

technologies. 

23 

CHAPTER 3. OPERATIONAL CONCEPT OF THE PROPOSED SYSTEM

This chapter details the operational concept of the TSMO TIM BAT UCs. The chapter describes 

how automated driving technology can be used in a range of cooperative manners to reduce 

delays and improve safety. The discussion covers from when C-ADS-equipped incident response 

vehicles enter the communication area of signalized intersections to when traffic signal 

preemption is provided. The development of the CARMA Ecosystem and MMITSS are key 

technological advances that enable a CDA approach to TSMO TIM of BAT using traffic signal 

preemption (PREEMPT).

TECHNOLOGICAL FRAMEWORK FOR TSMO TIM BAT UCS

This section describes the algorithm framework for an active traffic management feature to be 

used for the TSMO TIM BAT UCs. Many TSMO strategies can be used for TIM. This 

framework focuses on an arterial with several signalized intersections and one or more active 

incident response vehicles, as illustrated in figure 6. At any time, there may be one or more 

incident response vehicles en route to an incident. Having active incident response vehicles is an 

exception to the assumption of “normal” traffic operating conditions, and special considerations 

are made for traffic signal operations, including traffic signal preemption.

It is assumed that all incident response vehicles are equipped with CDA technologies. It is also 

assumed that each intersection is equipped with an RSU, an edge processor, and a traffic signal 

controller that provides SPaT data and can provide preemption for incident response vehicles 

when requested. CDA-capable incident response vehicles have high-definition maps used to 

determine vehicle approach, desired time of service, or estimated time of arrival (ETA) at 

intersection stop bar.

The decision to provide preemption can depend on several factors, including the presence of a 

queue. When an incident response vehicle sends a request for traffic signal priority (i.e., 

preemption), an RSU forwards the message to the appropriate edge processor, where it is

considered along with requests from other priority-eligible vehicles. At any single intersection, 

several incident response vehicles may request priority within a short amount of time.
24 

Source: FHWA. 

Figure 6. Illustration. Arterial traffic signal corridor with incident response operations. 

The traffic signals in an arterial network are generally operated in a coordinated mode that 

provides progression for vehicles traveling along the primary direction of movement. Generally, 

traffic analysts design coordinated signal timing (e.g., cycle length, offset, and phase splits) in 

consideration of normal general passenger vehicle flow along the route and not to assist incident 

response vehicles. Traffic signal preemption can alter traffic signal timing significantly. During 

preemption timing, some phases (e.g., movements) may be skipped, shortened, extended, and 

reordered. These changes deviate from the normal traffic signal timing and control, and human 

drivers (SAE Levels 1–2) may require additional response time. The presence of an incident 

response vehicle with active lights and siren can help reduce uncertainty. The goal of the altered 

signal timing is to improve response time and reduce the number of vehicles moving in the 

intersection to enhance safety.

CDA technologies can enhance situational awareness when incident response vehicles are active. 

Two V2V UCs based on the SAE J2735_201603 emergency vehicle alert (EVA) message that 

improve situational awareness are as follows:(42)

• TSMO UC EVA-1: Move over where vehicles are alerted to the presence of an incident 

response vehicle on the roadside and the vehicles are required to provide a safety barrier.

• TSMO UC EVA-2: Yield the right-of-way where an incident response vehicle is 

upstream from vehicles and the vehicles are required to move to the right side of the 

roadway to clear a pathway for the incident response vehicle to travel. This UC can 

improve safety and response time.

Traffic signal preemption is a TSMO tool that can be used to provide several potential benefits. 

The TSMO TIM BAT UCs include the following: 

• TSMO UC PREEMPT-1: Single-intersection, single incident response vehicle traffic 

signal preemption.
25 

• TSMO UC PREEMPT-2: Single-intersection, multiple incident response vehicle traffic 

signal preemption.

• TSMO UC PREEMPT-3: Multiple-intersection along the same route, multiple incident 

response vehicle traffic signal preemption.

• TSMO UC PREEMPT-4: Queue clearance.

• TSMO UC PREEMPT-5: Single-lane queue clearance.

TSMO UC EVA-1: Move Over 

The move-over UC is illustrated in figure 7. CDA-equipped vehicles are alerted to the presence 

of an incident response vehicle on the roadside, and they are required to provide a safety buffer. 

Vehicles are required to move over a lane or to reduce their speed to a threshold below the 

posted speed limit. CDA technology can alert vehicles as they approach an incident response 

vehicle on the roadside and can monitor the safety buffer during the passing event. This is SAE 

Class A and/or SAE Class D cooperative driving behavior for SAE Level 3–5 vehicles. The 

vehicles can be informed about the presence of the incident response vehicle or directed to move 

over or slow their speed. 

Source: FHWA.

Figure 7. Illustration. TSMO UC EVA-1: Move over. 
26 

TSMO UC EVA-2: Yield the Right-of-Way 

The yield-the-right-of-way UC utilizes the EVA message to alert nonemergency connected 

vehicles about an incident response vehicle approaching them in the roadway. When a vehicle is 

aware of an incident response vehicle approaching, the vehicle is required to move to the right 

side of the road and stop to clear a pathway for the incident response vehicle to travel. This UC 

can improve safety and response time. This UC is illustrated in figure 8. This is SAE Class A or 

SAE Class D cooperative driving behavior.(3) Vehicles can either be made aware of the 

approaching incident response vehicle or directed to move to the right.

Source: FHWA.

Figure 8. Illustration. TSMO UC EVA-2: Yield the right-of-way. 

TSMO UC PREEMPT-1: Single-Intersection, Single Incident Response Vehicle Traffic 

Signal Preemption Requests

This UC captures the most basic preemption behavior. A CDA incident response vehicle is 

approaching a signalized intersection (illustrated in figure 9). The vehicle communicates its ETA 

at the intersection, position, and speed. The traffic signal controller terminates phases that are 

currently green, with consideration for minimum green, pedestrian walk, pedestrian clearance, 

yellow change, and red clearance time. The signal will then hold the green indication for the 

incident response vehicle until it passes the stop bar and clears the intersection. The traffic signal 

will then return to normal signal timing. This is SAE Class B cooperative driving behavior. 

When the traffic signal controller becomes aware of the approaching incident response vehicle, it 

will change to green as quickly as possible. The traffic signal preemption behavior can be 

realized using the traffic signal controller’s preemption capability or using the MMITSS 

emergency vehicle priority capability.
27 

Source: FHWA.

Figure 9. Illustration. TSMO UC PREEMPT-1: Single-intersection, single incident 

response vehicle traffic signal preemption. 

TSMO UC PREEMPT-2: Single-Intersection, Multiple Incident Response Vehicle Traffic 

Signal Preemption

Figure 10 illustrates the UC at a single intersection where multiple incident response vehicles are 

approaching, perhaps from different directions. Traditional traffic signal preemption serves the 

requests for preemption on a first-come-first-serve basis. CDA provides the ability to have 

additional information about the number, approach direction, and ETA. MMITSS considers the 

collection of multiple active preemption requests simultaneously and determines the best traffic 

signal control that will reduce the total delay rather than using a first-come-first-serve protocol. 

Single-intersection, multiple incident response vehicle traffic signal preemption is SAE Class B 

cooperative behavior. The traffic signal control uses the information from the vehicles to 

determine the best traffic signal control within the constraints of the traffic signal controller, 

including minimum green, pedestrian walk, pedestrian clearance, yellow change, and red 

clearance times.
28 

Source: FHWA.

Figure 10. Illustration. TSMO UC PREEMPT-2: Single-intersection, multiple incident

response vehicle traffic signal preemption. 

TSMO UC PREEMPT-3: Multiple-Intersection, Multiple Incident Response Vehicle 

Traffic Signal Preemption

Effective traffic signal preemption requires time before an incident response vehicle arrives at 

the intersection to change from the current phase to the desired service phase (i.e., minimum 

green, pedestrian walk, pedestrian clearance, yellow change, and red clearance). Traffic signal 

preemption also requires time for the vehicles to be discharged to create a clear travel path for 

the incident response vehicle. If the route the incident response vehicle will travel is known, the 

traffic signals along the path can be preempted well in advance to create a clear path. This is 

especially true when there are multiple incident response vehicles traveling to an incident. This 

UC is illustrated in figure 11. 
29 

Source: FHWA.

Figure 11. Illustration. TSMO UC PREEMPT-3: Multiple-intersection, multiple incident 

response vehicle traffic signal preemption. 

Route information, location of incident response vehicles, and number of incident response 

vehicles traveling could be acquired from an emergency dispatch system using CARMA Cloud 

to coordinate travel along the route. To implement this coordination control strategy, priority 

requests can be scheduled in the MMITSS priority request server and the arrival times can be 

adjusted based on vehicle progress. MMITSS has a peer-to-peer capability that allows requests 

for priority to be forwarded from an upstream intersection to a downstream intersection. The 

forwarded requests will provide coordination and could be used as confirmation of vehicle 

arrival along a defined route. Multiple-intersection (route), multiple incident response vehicle 

traffic signal preemption is SAE Class B cooperative behavior. 

TSMO UC PREEMPT-4: Queue Clearance

When the transportation network is congested, or even when there is significant traffic demand, 

the existence of queues can be an impediment to incident response vehicles. Traffic signal 

priority and preemption can be used to clear the queues at downstream intersections to create a 

clear travel path for incident response vehicles. This UC is illustrated in figure 12. 

CDA vehicle information can be used to measure queues at intersections along the travel route. 

The time required to clear a standing queue can be accommodated by using MMITSS traffic 

signal priority (a less aggressive approach to traffic signal preemption). The queue can be cleared 

and then preemption can be used to serve the incident response vehicles. MMITSS preemption 

allows traffic signal phases to be omitted if they are not directly used to serve the approaching 

incident response vehicles. Queue clearance using traffic signal priority and traffic signal 

preemption is SAE Class B cooperative behavior. 
30 

Source: FHWA.

Figure 12. Illustration. TSMO UC PREEMPT-4: Queue clearance.

TSMO UC PREEMPT-5: Single-Lane Queue Clearance

Queue clearance is an effective strategy for improving the travel time of incident response 

vehicles. However, queue clearance can also cause additional queuing and congestion at 

downstream signals by releasing a large queue of vehicles that will then travel downstream and 

enter another queue. If this occurs at multiple intersections along a route, the downstream 

intersections can become significantly congested. To address this situation, single-lane queue 

clearance can be used, but requires lane control signals for each of the travel lanes. This is a new 

concept not directly addressed in the MUTCD.

(5) However, research has been conducted on the 

use of lane control signals for daily operations in Harris County, Texas.(44)

Figure 13 illustrates the concept of using lane control for traffic signal operations. Each lane has 

a traffic signal indication. According to the MUTCD, the traffic signals over each lane should be 

required to have supporting signage and lane markings that are clear to the vehicles. CDA 

vehicles are ideal for these advance traffic signal control concepts. As shown on the left side of 

figure 12, at the upstream intersection, the incident response vehicle passes through the 

intersection on a green traffic signal indication on the inside lane while the queue of vehicles in 

the right lane is held. At the downstream intersection, the left-hand lane is served by a green lane 

control indication to clear the queue ahead of the arriving incident response vehicles. 

Traffic signal preemption and traffic signal priority can provide a clear path for the incident 

response vehicle, and traffic congestion can be better managed using lane-based controls. Queue 

clearance using lane-based traffic signal priority and traffic signal preemption is SAE Class B 

cooperative behavior.
31 

Source: FHWA.

Figure 13. Illustration. TSMO UC PREEMPT-5: Single-lane queue clearance. 

INFRASTRUCTURE CONFIGURATION AND NEEDS

This section describes technological and institutional infrastructure and explains the role of IOOs 

in developing the CDA TIM operating policies and procedures. 

A key feature of CDA operations is the dynamic vehicle-infrastructure interactions, particularly 

the exchange of real-time vehicular and roadway information that an ADS-equipped vehicle can 

understand and share. This project considers RSE, an edge processor, and a traffic signal 

controller used to adapt traffic signal timing. The RSE can communicate to C-ADS-equipped

vehicles, irrespective of the particular communication technologies, using the appropriate 

protocols. C-ADS-equipped vehicles can also share their status and raw sensor data or objects 

that are sensed in the surrounding dynamic traffic environment to provide accurate world models 

of static and dynamic objects. The two-way information exchange constitutes the foundation of 

CDA, which includes both cooperative perception and cooperative vehicle control. 

For TIM, cooperative perception is also a key part of the automation. CDA participants, vehicles, 

and infrastructure may use shared perception information to improve situational awareness and 

expand their operational design domain. The algorithms for several of these UCs leverages 

CARMA Cloud to support sharing information between the incident management system and the 

traffic signal control system. However, many of the UCs can be effective without the cloudbased information. 

There are limited user needs relevant to operator-traveler interactions. Travelers are the primary 

beneficiaries but can also be information providers. Traffic operators, working to support the 

infrastructure, are the primary service and information providers. They receive information from 

C-ADS-equipped vehicles, process and analyze the information with all other available 

information, make appropriate changes to traffic signal timing, and send the resulting 

information back to C-ADS-equipped vehicles. Table 4 lists the infrastructure needs of road 

users and IOOs. In this table, road users are C-ADS-equipped and passenger vehicles, such that 

one-way or two-way information exchanges can occur between them and IOOs.
32 

Table 4. Infrastructure needs and responsibilities for road users (i.e., CDA vehicles) and 

IOOs. 

Road Users (C-ADS-equipped vehicles) IOOs

N/A. Monitor traffic conditions.

Inform IOOs of observed traffic conditions. Receive traffic condition information from 

travelers.

Get information on traffic conditions 

downstream. 

Get maps that contain incident response 

vehicle routes and incident locations. 

Get information on incident response vehicle 

status.

Inform travelers of traffic conditions.

Get information on weather conditions. Inform travelers of weather conditions.

Inform IOOs of observed weather conditions. Receive weather condition from road users 

(optional).

Inform IOOs of statuses, intents, and what they 

see.

Estimate queues.

N/A Control traffic signal timing.

Inform IOOs of statuses, intents, and what they 

see.

Optimize signal timing with consideration 

for various incident response vehicle types 

and statuses.

Get SPaT information. Inform travelers of planned SPaT.

N/A Control lane use. 

Get information on accessible and assigned 

lanes.

Inform travelers of accessible lanes and 

assigned lanes.

N/A = not applicable.

Based on the proposed control algorithm for specific UCs, the edge processor and intersection 

controller will exchange information for requesting and granting traffic signal priority for 

incident response vehicles, as shown in table 5. 

Table 5. Exchanges between RSE and vehicles.

RSE-to-vehicle Vehicle-to-RSE

Priority status and signal timing Cooperative perception

• Signal status message (SSM) (e.g., SRM 

acknowledgement). 

• SPaT plan.

• Information from other vehicles. 

• SRM (e.g., request for signal priority).

• Current status of incident response 

vehicles.
33 

SUMMARY OF TSMO NEEDS AND REQUIREMENTS

To summarize key features of this TSMO TIM ConOps and inform future development of 

TSMO TIM system requirements, this section describes operational needs and functional 

requirements for C-ADS-equipped vehicles and infrastructure. These needs and requirements are 

specified for different CDA cooperation classes and different components of the proposed 

control algorithm. For all these operational needs and functional requirements, the following 

applies: 

• Static infrastructure data may include high-definition maps, speed limits, and lane 

restrictions. 

• Dynamic transportation system information may include morning, evening, and off-peak 

traffic volumes; road closures; work zones; and traffic control system operations.

• C-ADS-equipped vehicle status and intent data may include vehicle identifier (ID) (e.g., 

license plate or a temporary anonymous ID), vehicle type, location, speed, braking status, 

heading, priority position, desired time of service (i.e., ETA), intersection approach, lane, 

and vehicle role (e.g., active fire response, ambulance, and police). These data sets may 

vary across different cooperation classes.

• RSE advisory data may include acknowledgement information from a vehicle request for 

priority for each C-ADS-equipped vehicle and RSE signal data including SPaT plan. An 

edge processor and RSEs are needed in all cooperation classes because C-ADS-equipped 

vehicles need to receive the SPaT plan. However, they might not be used for transferring 

information from one C-ADS-equipped vehicle to another if V2V communication range 

is sufficient in the control area.

Table 6 lists operational needs for vehicles and infrastructure. 

Table 6. Operational needs for vehicles and infrastructure in TSMO TIM BAT UCs.

Actor ID Operational Need

CARMA Cloud, 

CARMA Platform, 

CARMA Messenger

TSMO TIM BAT

UC-N01

Communicate incident location to incident 

response vehicle(s).

CARMA Platform, 

CARMA Messenger

TSMO TIM BAT 

UC-N02

Store and broadcast vehicle status and intent 

information (e.g., location, speed, and 

route). 

CARMA Platform, 

CARMA Messenger

TSMO TIM BAT 

UC-N03

For incident response vehicle, send SRM to 

RSE once it is within range of RSE.

CARMA Cloud, 

CARMA Streets

TSMO TIM BAT 

UC-N04

CARMA Streets needs to receive static 

infrastructure data (e.g., high-definition

maps, speed limits, and lane restrictions) 

from CARMA Cloud.
34 

Actor ID Operational Need

CARMA Platform, 

CARMA Messenger

TSMO TIM BAT 

UC-N05

Receive static infrastructure data, vehiclespecific advisory data, and SPaT. Receive

priority request status information from 

incident response vehicles. 

RSE TSMO TIM BAT 

UC-N06

Receive vehicle status and intent 

information from C-ADS-equipped vehicles.

RSE TSMO TIM BAT 

UC-N07

Receive SRM from incident response 

vehicles.

RSE–CARMA Streets TSMO TIM BAT 

UC-N08

RSE needs to relay received data to 

CARMA Streets.

RSE–CARMA Streets TSMO TIM BAT 

UC-N09

RSE needs to receive static infrastructure 

data, vehicle-specific advisory data, and 

SPaT from CARMA Streets and broadcast 

this information.

RSE–CARMA Streets–

RSE

TSMO TIM BAT 

UC-N10

CARMA Streets needs to send signal status 

message information to RSE for broadcast to 

incident response vehicles to acknowledge 

receipt of SRM.

CARMA Streets TSMO TIM BAT 

UC-N11

Receives and stores SPaT data.

CARMA Streets TSMO TIM BAT 

UC-N12

Stores data from various sources (e.g., 

incident response vehicles, C-ADS-equipped

vehicles, and traffic sensors).

CARMA Streets TSMO TIM BAT 

UC-N13

Processes data and calculates traffic signal 

preemption-related variables (e.g., 

preemption status, signal adaptation, queue 

length and dissipation estimation, and speed 

advisory). 

CARMA Cloud, 

CARMA Streets

TSMO TIM BAT 

UC-N14

CARMA Cloud needs to relay work zone 

data to CARMA Streets.

CARMA Streets, signal 

controller

TSMO TIM BAT 

UC-N15

CARMA Streets needs to communicate with 

the signal controller to adapt the signal 

timing.

CARMA Streets TSMO TIM BAT 

UC-N16

Aggregates traffic information received 

from individual vehicles and sensors.

CARMA Cloud, 

CARMA Streets

TSMO TIM BAT 

UC-N17

CARMA Streets needs to send aggregate 

traffic information to CARMA Cloud. 

CARMA Cloud, 

CARMA Platform, 

CARMA Messenger, 

CARMA Streets, RSE, 

signal controller

TSMO TIM BAT 

UC-N18

All must have proper cybersecurity 

platforms and strategies to protect them 

from and help them recover from cyber 

threats.
35 

Table 7 lists functional requirements for C-ADS-equipped vehicles, RSEs, and a central 

computer. These requirements are also specified for different cooperation classes.

Table 7. Functional requirements for vehicles and infrastructure in TSMO TIM UCs. 

ID Functional Requirement Cooperation Class

TSMO TIM BAT 

UC-R01

A C-ADS-equipped incident response 

vehicle with at least cooperation Class A has 

an onboard computer with storage and 

computing functions.

A, B, C, and D

TSMO TIM BAT 

UC-R02

A C-ADS-equipped incident response 

vehicle with at least cooperation Class A has 

an alert system with lights and a siren that 

provides statuses to an onboard computer.

A, B, C, and D

TSMO TIM BAT 

UC-R03

A C-ADS-equipped incident response 

vehicle with at least cooperation Class A 

broadcasts its location, speed, and heading. 

The communication frequency is

approximately 10 hertz (Hz) or more.

A, B, C, and D; status 

data only for Class A 

TSMO TIM BAT 

UC-R04

A C-ADS-equipped incident response 

vehicle with at least cooperation Class A 

determines the distance and time to the 

intersection stop bar and sends an SRM 

when traffic signal preemption is desired. 

The vehicle will determine if the lights and 

siren system is active to qualify to send an 

SRM. 

A, B, C, and D

TSMO TIM BAT 

UC-R05

A C-ADS-equipped incident response 

vehicle with at least cooperation Class A 

determines that the lights and siren system is 

active and then broadcasts an EVA message.

A, B, C, and D

TSMO TIM BAT 

UC-R06

Each intersection has an edge processor that 

is capable of running traffic signal 

preemption algorithms (i.e., MMITSS) and 

communicating NTCIP to a traffic signal 

controller.

A, B, C, and D

TSMO TIM BAT 

UC-R07 

The central computer provides incident 

information—such as the location of the 

incident, the location of the assigned 

response vehicle, and the route the vehicle 

will travel to the incident—to the edge 

processor at a signalized traffic intersection.

A, B, C, and D

TSMO TIM BAT 

UC-R08 

The central computer provides downstream 

traffic congestion information to the 

intersection’s edge processor.

A, B, C, and D
36 

ID Functional Requirement Cooperation Class 

TSMO TIM BAT 

UC-R09 

RSE receives status and intent data from 

C-ADS-equipped vehicles with at least 

cooperation Class A within the 

communication range. The communication 

frequency is approximately 10 Hz or more. 

A, B, C, and D 

TSMO TIM BAT 

UC-R10 

RSE broadcasts traffic signal status and 

intent data among C-ADS-equipped vehicles

within the communication range using DSRC 

or C-V2X. The communication frequency is

approximately 10 Hz or more. 

A, B, C, and D; 

optional for when 

CDA communication 

range is too short, and 

data needs to be 

relayed using RSEs. 

TSMO TIM BAT 

UC-R11 

RSE sends vehicle-specific advisory data and 

determined SPaT within the communication 

range using DSRC or C-V2X. The 

communication frequency is approximately 

10 Hz or more. 

A, B, C, and D 

TSMO TIM BAT 

UC-R12 

RSE rebroadcasts EVA messages received 

from incident response vehicles. 

A, B, C, and D 

PERFORMANCE METRICS AND TARGET TRAFFIC FLOW

The effectiveness of TSMO TIM BAT UCs can be evaluated by measuring their capability to

positively impact performance. Performance metrics in this ConOps are presented from two 

perspectives: incident response vehicle travel experience and traffic flow.

Performance Metrics for Traveler Experience

Performance metrics for monitoring and evaluating vehicle operations during the execution of 

these use cases include the following: 

• Safety—Number of crashes or near crashes (conflicts) that incident response vehicles are 

involved in while actively responding to incidents. 

• Incident response travel time—Time for a collection of incident response vehicles to 

travel from their origins to the incident location. In a corridor, this would include the time 

from control corridor entry to the time of corridor exit or of arrival at the incident 

location. 

• Travel time of other road users—Time for a vehicle to travel from an origin to a 

destination in the control corridor. 

• Incident response vehicle queueing delay—Time that an incident response vehicle is 

delayed due to nonincident response vehicle obstruction or queueing. 

• Incident response vehicle traffic signal delay—Time an incident response vehicle is 

stopped at a traffic signal, including time waiting for a traffic queue to clear.
37 

• Nonincident response vehicle traffic signal delay—Time a nonincident response vehicle 

is stopped at a traffic signal, including time in a traffic queue.

• Data exchanges during communication/negotiation—All data exchanges from V2V, V2I, 

and I2V are used to determine if communication and maneuver negotiations took place as 

designed. Data exchanges include the following data types:

o Number of vehicles that request traffic signal priority.

o Number of SRMs that require requests per vehicle.

o Number of attempts needed before a plan is accepted by all affected neighbors. 

o Frequency of packet loss. 

o Latency of message: The time difference between message origination on vehicle A 

and the reading of the message by infrastructure and vice versa. Latency time 

includes the time it takes to compose the message and send it from vehicle A’s 

guidance computer to vehicle A’s OBU, the queuing time on vehicle A’s OBU, the 

radio transmission time from vehicle A to infrastructure, the message constitution and 

queueing time on infrastructure’s RSE, the transformation time from infrastructure’s 

RSE to infrastructure’s computer, the time required for retransmission in the case of 

message loss, and the time for infrastructure’s decomposition and reading.

Performance Metrics on Traffic Performance

Performance metrics on traffic performance evaluate the impact of TSMO TIM BAT UCs on 

traffic flow in corridors. Table 8 summarizes two categories of impacts: throughput (nonincident 

response vehicles) and traffic congestion (level of service).

Throughput

CDA technologies are expected to increase the vehicle throughput of transportation facilities by 

increasing the number of vehicles served. Throughput can be quantified by measuring the 

number of vehicles passing through a corridor per hour and the variability of speeds within a 

facility segment. 

Traffic Congestion

CDA technologies are applied to improve incident response performance. Since traffic signal 

preemption is an exception to normal traffic operations, the impacts of these technologies could 

be significant. Traffic congestion can be measured by level of service, as defined in the Highway 

Capacity Manual.

(45)
38 

Table 8. Summary of performance metrics for TSMO TIM BAT UC evaluation. 

Category Impact Performance Metric

Safety Safety of incident responders is the 

highest priority.

Crashes and conflicts. Surrogate 

safety measures of conflicts can be 

used to characterize the level of 

safety. Some surrogate safety 

measures include time to collision, 

modified time to collision, postencroachment time, and speed 

differential. 

Throughput Increase in number of vehicles that 

can be served in a corridor.

Number of vehicles passing 

through the corridor per hour.

Throughput Potential increase in delay for 

nonincident response vehicles when 

traffic signal preemption is given to 

incident response vehicles.

Average traffic signal delay for 

nonincident response vehicles.

Traffic congestion Potential impact in quality of 

service by shifting priority to 

incident response vehicles.

Level of service.

(44)
39 

CHAPTER 4. OPERATIONAL SCENARIOS

This chapter identifies three TSMO TIM BAT operational scenarios. Each scenario includes one 

or more of the UCs described in chapter 3. The operational scenarios help to increase 

understanding of the impact of the potential deployment of certain TIM UCs in early stages of 

CDA technology development. The first operational scenario considers the use of traffic signal 

preemption and the issue of yielding the right-of-way in a corridor. The second scenario extends 

the first scenario to consider route-based preemption and the use of lane-by-lane traffic signal 

control for queue clearance. The third scenario explores the use of CDA when there is a work 

zone that can cause response delay for incident response vehicles. These scenarios are designed 

to cover all the key features of the proposed control framework and illustrate their potential 

benefits.

SCENARIO 1: ARTERIAL WITH MULTIPLE INCIDENT RESPONSE VEHICLES

This scenario captures incident response operations on a corridor and includes TSMO UC 

PREEMPT-2 (single intersection, multiple incident response vehicle traffic signal preemption) 

and TSMO UC EVA-2 (yield the right-of-way). This scenario is illustrated in figure 14. It is 

assumed that traffic signals in the corridor are operated as a coordinated system and have 

coordination signal timing plans (e.g., cycle length, offset, and phase spits) that have been 

selected based on the traffic volumes for the time of day.

Source: FHWA.

Figure 14. Illustration. Scenario 1: Basic arterial with multiple incident response vehicles. 

In this scenario, four incident response vehicles are active in the corridor. One of the incident 

response vehicles has just exited the intersection on the left and is headed toward the intersection 

on the right; it is requesting traffic signal preemption. Two other incident response vehicles are 

approaching the intersection on the left; both are requesting traffic signal preemption. The fourth 

incident response vehicle is approaching the right intersection from the bottom and requesting 

preemption. Vehicles on the arterial are being alerted to the presence of the incident response 

vehicles and are moving to the right lane to clear a path. 
40 

Scenario 1 captures several key operating characteristics, as follows:

• Multiple incident response vehicles may issue priority requests at any intersection at any 

given time. 

• Traffic signals operate as a coordinated system.

• Incident response vehicles alert other vehicles to their presence.

• Other vehicles yield the right-of-way to incident response vehicles. If the other vehicles 

are C-ADS vehicles, this communication is SAE Class D cooperation; otherwise, it is 

SAE Class A cooperation.

Scenario 1 will allow the performance of the system to be evaluated to better understand the 

following:

• Benefits for incident response operations of the TSMO TIM, such as incident response 

vehicle travel time and impact or delay to nonincident response vehicles.

• Impact of number of active incident response vehicles on system performance.

• Impact of conflicting incident response vehicle requests for preemption (e.g., main 

arterial and cross streets).

• Impact of providing traffic signal preemption in a coordinated system of traffic signals.

Scenario 1 highlights several important features. First, the ability to use CDA data to make 

preemption signal timing decisions with an algorithm that can accommodate multiple active 

incident response vehicles is valuable. Next, the EVA message is valuable in its ability to alert

other vehicles to the presence of incident response vehicles so they can clear a path. Both UCs 

can improve incident response vehicle safety and response time. 

SCENARIO 2: ROUTE-BASED PRIORITY WITH LANE-BY-LANE TRAFFIC SIGNAL 

PREEMPTION AND YIELD THE RIGHT-OF-WAY

Scenario 2 depicts an incident on the egress leg of the intersection on the right. In figure 15-A at 

time 1, there are four active incident response vehicles. One incident response vehicle has just 

exited the left intersection in the center lane, which had provided a green indication when the 

incident response vehicle requested traffic signal preemption. Two other incident response 

vehicles are approaching the left intersection. They are each requesting traffic signal preemption. 

The incident response vehicle approaching from the left will receive the center lane green 

indication, because the signal was already in that state, even though the incident response vehicle 

approaching from the bottom is closer and would have arrived sooner. The fourth incident 

response vehicle is approaching the intersection on the right from the bottom. It is requesting 

traffic signal preemption and receiving a green signal indication on the inside lane. 

In figure 15-B at time 2, the incident response vehicle approaching the left intersection from the 

left is clearing the intersection. The traffic signal has changed to the yellow interval as it 
41 

transitions to provide traffic signal preemption for the incident response vehicle arriving from the 

bottom. The incident response vehicle that approached the intersection on the right from the 

bottom has cleared the intersection. The traffic signal has already transitioned to the green 

interval for the vehicle approaching from the left, which is requesting traffic signal preemption. 

The queue on the inside lane of the left approach to the intersection on the right will have 

sufficient time to clear before the incident response vehicle arrives. In both intersections, all 

movements that are not serving incident response vehicles are in a red interval to prevent 

vehicles from entering the response routes of the incident response vehicles.

Source: FHWA.

A. Time 1. 

Source: FHWA.

B. Time 2. 

Figure 15. Illustrations. Route-based priority with lane-by-lane traffic signal preemption 

and yielding of right-of-way. 

TSMO UC EVA-2 (yield the right-of-way) supports the CDA capability of directing (SAE Class 

D cooperation) C-ADS vehicles to yield the right-of-way to incident response vehicles and 
42 

alerting other CDA vehicles to the presence of active incident response vehicles. TSMO UC 

PREEMPT-3 (multiple-intersection (route) and multiple incident response vehicle traffic signal 

preemption) and TSMO UC PREEMPT-5 (single-lane queue clearance) create response 

“tunnels” for each of the incident response vehicles.

Scenario 2 captures several key operating characteristics, as follows:

• Multiple vehicles may request traffic signal preemption along an incident response route 

at the same time.

• The traffic signals should operate as a coordinated system.

• Lane-by-lane traffic signal indications will allow the creation of incident response 

“tunnels” that can accommodate queue clearance and reduce the number of vehicles with 

conflicting movements in an intersection.

• Incident response vehicles can alert other vehicles to their presence so the other vehicles 

can yield the right-of-way. If the other vehicles are C-ADS vehicles, this is SAE Class D 

cooperation; otherwise, it is SAE Class A.

This scenario will allow researchers to evaluate traffic performance to better understand the 

following:

• Benefits of TSMO TIM on incident response operations. 

• Impacts of TSMO TIM on other road users.

• Impact of number of active incident response vehicles on system performance.

• Impact of conflicting incident response vehicle requests for preemption (e.g., main 

arterial and cross streets).

• Impact of providing traffic signal preemption in a coordinated system of traffic signals.

• Benefits of lane-by-lane traffic signal control to provide queue clearance.

Scenario 2 highlights several important features. First, the ability to use CDA data to make 

preemption signal timing decisions with an algorithm that can accommodate multiple active 

incident response vehicles is valuable. Next, the ability to control traffic flow on a lane-by-lane 

basis to reduce the number of potential conflicts and the flow of queued vehicles to downstream 

intersections is valuable. Finally, the EVA message’s ability to alert other vehicles to the 

presence of incident response vehicles so they can move out of the way is also valuable. These 

UCs can improve incident response vehicle safety and response time.

SCENARIO 3: WORK ZONE INCIDENT RESPONSE

Work zones generally have delays due to reduced speed and lane availability. In this scenario, 

TIM can be used to help incident response vehicles travel through the work zone faster. With the 
43 

awareness of a work zone on the route to an incident, upstream intersections can be used to meter 

the volume of traffic entering the work zone ahead of one or more incident response vehicles. 

This metering may reduce the queue entering the work zone. Many work zones consist of a 

single lane, but some work zones may have more than one lane. When there is more than one 

lane, TSMO UC EVA-2 could provide improvements to incident response vehicle travel time. 

Scenario 3 enables work-zone information to be available for TIM in arterial corridors.

Scenario 3 will allow researchers to evaluate traffic performance to better understand the 

following:

• Use of traffic signal priority or preemption to meter flow of traffic into a work zone.

• Use of EVA in work zones with multiple travel lanes to direct C-ADS vehicles to move 

to the right (SAE Class D cooperation) and alert CDA vehicles to the presence of the 

incident response vehicles so they can also move to the right (SAE Class A cooperation). 

45 

CHAPTER 5. ANALYSIS OF THE PROPOSED SYSTEM

This chapter provides an analysis of the benefits, advantages, limitations, and disadvantages of 

TSMO TIM BAT UCs on signalized arterials using scenario 1, which was discussed in chapter 4. 

A high-level system validation plan is also discussed.

SUMMARY OF POTENTIAL BENEFITS AND OPPORTUNITIES

CDA technologies enable mobility applications that are not achievable by individual ADSoperated vehicles. They do so by sharing information that can be used to increase the safety, 

efficiency, and reliability of the transportation system. This information may also serve to 

accelerate the deployment of driving automation in on-road motor vehicles. CDA aims to 

improve the mobility of travelers in transportation systems. This is accomplished, for example, 

by sharing information about incident response vehicle status (location, destination, and route); 

estimating queues and traffic congestion; and providing preferential treatment on signalized 

arterials. Cooperation among diverse participants and perspectives in traffic, especially at 

conflict areas (e.g., intersections, merging roadways), can improve safety, mobility, situational 

awareness, and operations.

For the TSMO TIM BAT UCs, an integrated control framework is proposed to efficiently 

manage traffic on signalized arterials. Vehicle capabilities, including generating and sending 

SRMs, can be processed on CDA-capable vehicles using CARMA Platform. This information 

can be shared with infrastructure using CDA-enabled wireless communications, regardless of the 

particular technology. Infrastructure, using CARMA Streets, can provide preemption and priority 

traffic signal control that specifically accommodates incident response vehicle requests. 

Information about incident response vehicle location, speed, destination, and route can be made 

available through CARMA Cloud through integration with incident response management and 

dispatch systems. For this analysis, only CARMA Platform and CARMA Streets are considered.

SYSTEM VALIDATION PLAN

This section describes methods to validate the developed algorithms and software systems for the 

TSMO TIM BAT UCs. Validation testing helps ensure that, once developed, the TSMO TIM 

BAT UC system can meet the operational needs for scenario 1 listed in table 8. 

Simulation Testing

Simulations can be designed to test the developed signal control algorithm for the TSMO TIM 

BAT UCs using the performance metrics, identified in chapter 3, of incident response vehicle 

and infrastructure behavior and traffic system performance. Different types of simulation can be 

used and combined for testing purposes. 

Traffic simulators offer the possibility to scale up evaluation to an intersection corridor or 

network level (as compared to a limited number of vehicles and amount of roadway length for 

ADS simulators) to study CDA’s impacts on transportation system performance. For this 

purpose, these impacts are measured using traffic performance metrics such as safety, efficacy, 

stability, and sustainability. Traffic simulators can evaluate various scenarios, such as traffic 
46 

demand, TIM, and intersection geometry (including near-side bus stops). Usually, the CDA 

control algorithms will be simplified from real software and parsimonious to calibrated and 

validated CDA behavioral models and algorithms that are implementable for large-scale testing.

Field Testing

To ensure the developed algorithm can be reliably and easily implemented into CARMA 

Platform, proof-of-concept tests will be conducted on a closed-test track. This can be 

demonstrated on site at a signalized intersection that is typical of anywhere in the United States. 

Depending on participation by partners, multiple CARMA vehicles loaded with necessary 

feature groups can be instructed to run loops on the test track to represent continuous driving. 

The operational scenarios discussed in chapter 4 can be tested. The purpose of the testing can be 

to verify the software, collect vehicle behavior performance measures, and validate if the 

software meets the requirements. Data collected from the test track can be used not only to 

calculate vehicle behavior performance metrics, but also to calibrate traffic simulation CDA 

behavior models. This may enable better evaluation of CDA’s traffic impacts using validated 

simulation models. 

SUMMARY OF IMPACTS

The proposed control strategy for the TSMO TIM BAT UCs can impact the research and 

operations of future transportation systems management. From a research perspective, the 

proposed control strategy offers an approach to efficiently manage transportation systems at 

signalized intersections and reduce any adverse effects, such as improving safety while reducing 

excessive delay. The benefits of the TSMO TIM BAT UCs can be demonstrated using CDA 

incident response vehicles that send SRMs to infrastructure when responding to incidents. The 

infrastructure components accept the SRMs and adapt the traffic signal timing, within the 

structure of the traffic signal controller, to provide benefits to the incident response vehicles.

From an operations perspective, the proposed control strategy for the TSMO TIM BAT UCs

presents changes to how TSMO is conducted at signalized intersections. Intelligent 

transportation system infrastructure systems would need to be upgraded to accommodate CDA 

system needs, such as RSE services and supporting information technologies. Agencies would 

need to evaluate and build up capabilities for operating such emerging systems. The conventional 

process of transportation system performance monitoring and reporting could be improved with 

the prevalence of C-ADS-equipped vehicles and advanced sensors. Conventional strategies for 

TSMO that agencies may already be familiar with can be enhanced by CDA technologies. 

Disadvantages and Limitations

The proposed control strategy for the TSMO TIM BAT UCs provides insights into CDA 

operations at signalized intersections but may face limitations, such as the following: 

• Providing traffic signal preemption for incident response vehicles can impact the 

performance of other traffic. Incident response is considered an exception to normal 

operations, but the impact of shifting the capacity should be understood and mitigated if 

excessive. 
47 

• Simulating incident responses can be challenging. Many scenarios can be considered, and 

it is challenging to select those that are common because incident response is an 

exception to normal operations. Ideally, safety and mobility will be improved without 

significant negative impact.

49 

CHAPTER 6. SUMMARY AND CONCLUSION

CDA aims to improve the safety, traffic throughput, and energy efficiency of the transportation 

network by allowing vehicles and infrastructure to work together to coordinate movement. The 

FHWA CDA Research Program is utilizing the CARMA Ecosystem to research CDA and 

leverage emerging capabilities in automation and cooperation to advance TSMO strategies. 

The objective of this project is to advance the CARMA Ecosystem to enhance infrastructure 

performance, improve network efficiency, reduce traffic congestion through TSMO strategies on 

arterials, and enable further capabilities for CDA participants to interact with the road 

infrastructure. This ConOps focuses on TSMO TIM use cases, which investigate active traffic 

management strategies applied to traffic signal corridors. TSMO TIM is a traffic management 

tool that can help improve safety and reduce travel time delay caused by traffic signals for 

incident response vehicles. The proposed approach for TSMO TIM has two components: First, 

local traffic signal optimization to improve safety and reduce delay to one or more incident 

response vehicles using CARMA Streets. Second, corridor-coordinated TSMO TIM to optimize 

signal timing in a corridor using CARMA Cloud. The proposed control framework is expected to 

improve safety and reduce incident response travel time.

CDA technologies can enhance situational awareness when incident response vehicles are active. 

Table 9 summarizes the TSMO TIM BAT UCs. The UCs are classified as SAE Class B because 

the incident response vehicle in each UC shares its intent and requests cooperative behavior from 

infrastructure and other CDA vehicles.

Table 9. Summary of TSMO TIM BAT UCs.

TSMO TIM BAT 

UCs Description

SAE CDA 

J3216_202107 Class 

Behavior

EVA-1 Vehicles are alerted to the presence of an 

incident response vehicle on the roadside and 

are required to provide a safe barrier.

Class B

EVA-2 Vehicles yield the right-of-way to incident 

response vehicles to provide safer travel and 

reduce response time.

Class B

PREEMPT-1 Single-intersection, single incident response 

vehicle traffic signal preemption.

Class B

PREEMPT-2 Single-intersection, multiple incident response 

vehicle traffic signal preemption.

Class B

PREEMPT-3 Multiple-intersection (route), multiple incident 

response vehicle traffic signal preemption.

Class B

PREEMPT-4 Queue clearance (approach). Class B

PREEMPT-5 Single-lane queue clearance. Class B
50 

The CDA Program team has created two V2V UCs based on the SAE J2735 EVA message.(41)

These UCs, which improve situational awareness, are as follows:

• TSMO UC EVA-1: Vehicles are alerted to the presence of an incident response vehicle 

on the roadside and are required to provide a safety barrier. This UC can improve safety. 

• TSMO UC EVA-2: Vehicles are required to yield the right-of-way and move to the right 

side of the roadway to clear a pathway for incident response vehicles approaching from 

the rear. This UC can improve safety and response time.

Traffic signal preemption is a TSMO tool that can provide potential benefits. The TSMO TIM 

UCs include the following:

• TSMO UC PREEMPT-1: Single-intersection, single incident response vehicle traffic 

signal preemption.

• TSMO UC PREEMPT-2: Single-intersection, multiple incident response vehicle traffic 

signal preemption.

• TSMO UC PREEMPT-3: Multiple-intersection, multiple incident response vehicle traffic 

signal preemption.

• TSMO UC PREEMPT-4: Queue clearance.

• TSMO UC PREEMPT-5: Single-lane queue clearance.
51 

REFERENCES

1. Open Robotics. 2021. “ROS - Robot Operating System” (web page).

https://www.ros.org/, last accessed September 9, 2022.

2. SAE. 2014. Taxonomy and Definitions for Terms Related To On-Road Motor Vehicle 

Automated Driving Systems. J3016_202104. Warrendale, PA: SAE. 

https://www.sae.org/standards/content/j3016_202104/, last accessed July 6, 2022.

3. SAE. 2020. Taxonomy and Definitions for Terms Related To Cooperative Driving 

Automation for On-Road Motor Vehicles. J3216_202107. Warrendale, PA: SAE. 

https://www.sae.org/standards/content/j3216_202107/, last accessed May 24, 2022. 

4. IEEE. 2011. 2011 International Standard—Systems and Software Engineering—Life 

Cycle Processes—Requirements Engineering. ISO/IEC/IEEE. 

https://ieeexplore.ieee.org/document/6146379/references#references, last accessed May 

6, 2022. 

5. FHWA. 2012. Manual on Uniform Traffic Control Devices for Streets and Highways: 

2009 Edition. Washington, DC: FHWA. https://mutcd.fhwa.dot.gov/pdfs/2009r1r2/pdf_i

ndex.htm, last accessed February 10, 2022. 

6. Nelson, E. J. and D. Bullock. 2000. “Impact of Emergency Vehicle Preemption on 

Signalized Corridor Operation: An Evaluation.” Transportation Research Record 1727, 

no. 1: 1–11. 

7. Long, W. H. 1970. Traffic Signal Remote Control System. U.S. Patent 3,550,078. Filed 

March 16, 1967. Issued December 22, 1970. 

8. Fogue, M., P. Garrido, F. J. Martinez, J. C. Cano, C. T. Calafate, and P. Manzoni. 2012. 

“Automatic Accident Detection: Assistance Through Communication Technologies and 

Vehicles.” IEEE Vehicular Technology Magazine 7, no. 3: 90–100. 

9. Li, J., C. Qiu, L. Peng, and T. Z. Qiu. 2018. “Signal Priority Request Delay Modeling and 

Mitigation for Emergency Vehicles in Connected Vehicle Environment.” Transportation 

Research Record 2672, no. 18: 45–57. 

10. Asaduzzaman, M. D. and K. Vidyasankar. 2017. “A Priority Algorithm To Control the 

Traffic Signal for Emergency Vehicles.” Presented at the 2017 IEEE 86th Vehicular 

Technology Conference (VTC Fall). IEEE: Toronto, Canada. 

11. Gordon, R. L., W. Tighe, and I. T. S. Siemens. 2005. Traffic Control Systems Handbook. 

Report No. FHWA-HOP-06-006. Washington, DC: FHWA. 

12. Qin, X., and A. M. Khan. 2012. “Control Strategies of Traffic Signal Timing Transition 

for Emergency Vehicle Preemption.” Transportation Research Part C: Emerging 

Technologies 25: 1–17. 
52 

13. Shaaban, K., M. A. Khan, R. Hamila, and M. Ghanim. 2019. “A Strategy for Emergency 

Vehicle Preemption and Route Selection.” Arabian Journal for Science and Engineering

44, no. 10: 8905–8913. 

14. Chen, C. Y., P. Y. Chen, and W. T. Chen. 2013. “A Novel Emergency Vehicle 

Dispatching System.” Presented at the 2013 IEEE 77th Vehicular Technology Conference 

(VTC Spring). Dresden, Germany: IEEE.

15. Noori, H., L. Fu, and S. Shiravi. 2016. “A Connected Vehicle Based Traffic Signal 

Control Strategy for Emergency Vehicle Preemption.” Presented at the 95th Annual 

Meeting of the Transportation Research Board. Washington, DC: Transportation 

Research Board.

16. He, Q., K. L. Head, and J. Ding. 2014. “Multi-Modal Traffic Signal Control With 

Priority, Signal Actuation and Coordination.” Transportation Research Part C: Emerging 

Technologies 46: 65–82. 

17. Zamanipour, M., K. L. Head, Y. Feng, and S. Khoshmagham. 2016. “Efficient Priority 

Control Model for Multimodal Traffic Signals.” Transportation Research Record 2557, 

no. 1: 86–99. 

18. NBC Universal Media: NBC Bay Area. 2017. “Two Emergency Vehicles Collide in 

Vallejo, Injuring Pair of First Responders” (website). https://www.nbcbayarea.com/news/

local/two-emergency-vehicles-collide-in-vallejo-sending-pair-of-first-responders-tohospital/33425, last accessed June 21, 2020. 

19. Gross, D. and J. Gross. 2001. Emergency Vehicle Traffic Signal Pre-Emption and 

Collision Avoidance System. U.S. Patent 6,326,903 B1. Filed January 26, 2000. Issued 

December 4, 2001. 

20. Goel, A., S. Ray, and N. Chandra. 2012. “Intelligent Traffic Light System To Prioritize 

Emergency Purpose Vehicles Based on Wireless Sensor Network.” International Journal 

of Computer Applications 40, no. 12: 36–39. 

21. Reaves, B. 2017. Police Vehicle Pursuits, 2012–2013: Law Enforcement Management 

and Administrative Statistics Special Report. Report No. NCJ 250545. Washington, DC: 

U.S. Department of Justice, Office of Justice Programs.

22. Fahy, R. F. 2008. U.S. Firefighter Fatalities in Road Vehicle Crashes 1998–2007. 

Quincy, MA: National Fire Protection Association. https://citeseerx.ist.psu.edu/viewdoc/

download?doi=10.1.1.405.4563&rep=rep1&type=pdf, last accessed February 10, 2022. 

23. Bui, D. P., S. Balland, C. Giblin, A. M. Jung, S. Kramer, A. Peng, M. C. P. Aquino, S. 

Griffin, D. D. French, K. P. Porter, and S. Crothers. 2018. “Interventions and Controls To 

Prevent Emergency Service Vehicle Incidents: A Mixed Methods Review.” Accident 

Analysis and Prevention 115: 189–201. 
53 

24. Bachelder, A. D., and C. F. Foster. 2008. Emergency Vehicle Traffic Signal Preemption 

System. U.S. Patent 7327280 B2. Filed March 24, 2004. Issued February 5, 2008. 

25. Bui, D. P., C. Hu, A. M. Jung, K. M. Pollack Porter, S. C. Griffin, D.D. French, 

S. Crothers, and J. L. Burgess. 2018. “Driving Behaviors Associated With Emergency 

Service Vehicle Crashes in the U.S. Fire Service.” Traffic Injury Prevention 19, no. 8: 

849–855. 

26. Hsiao, H., J. Chang, and P. Simeonov. 2018. “Preventing Emergency Vehicle Crashes: 

Status and Challenges of Human Factors Issues.” Human Factors 60, no. 7: 1048−1072. 

27. Smith, M. R., J. Davidson, and H. Pfister. 1987. Emergency Vehicle Warning and Traffic 

Control System. U.S. Patent 4704610A. Filed December 16, 1985. Issued November 3, 

1987. 

28. Buchenscheit, A., F. Schaub, F. Kargl, and M. Weber. 2009. “A VANET-Based 

Emergency Vehicle Warning System.” Presented at the 2009 IEEE Vehicular Networking 

Conference (VNC). Tokyo, Japan: IEEE. 

29. Gates, T. J. and D. A. Noyce. 2010. “Dilemma Zone Driver Behavior as a Function of 

Vehicle Type, Time of Day, and Platooning.” Transportation Research Record 2149, no. 

1: 84–93. 

30. Tarko, A., W. Li, and L. Laracuente. 2006. “Probabilistic Approach to Controlling 

Dilemma Occurrence at Signalized Intersections.” Transportation Research Record 1973, 

no. 1: 55–63. 

31. McCoy, P. T. and G. Pesti. 2003. “Improving Dilemma-Zone Protection of Advance 

Detection With Advance-Warning Flashers.” Transportation Research Record 1844, no. 

1: 11–17. 

32. Zimmerman, K., J. A. Bonneson, D. Middleton, and M. M. Abbas. 2003. “Improved 

Detection and Control System for Isolated High-Speed Signalized Intersections.” 

Transportation Research Record 1856, no. 1: 212–219. 

33. Bullock, D., J. M. Morales, and B. Sanderson. 1999. “Impact of Signal Preemption on the 

Operation of the Virginia Route 7 Corridor.” Presented at the ITS [Intelligent 

Transportation Society] America 9th Annual Meeting and Exposition: New Thinking in 

Transportation. Vancouver, British Columbia, Canada: ITS America. 

https://trid.trb.org/view/689818, last accessed May 24, 2022. 

34. Nelson, E. J., and D. Bullock. 2000. “Impact of Emergency Vehicle Preemption on 

Signalized Corridor Operation: An Evaluation.” Transportation Research Record 1727, 

no. 1: 1–11. 

35. Collura, J., and J. Chang. 2001. “Traffic Signal Preemption and Priority: Technologies, 

Past Deployments, and System Requirements.” Presented at the ITS 2001: Intelligent 
54 

Transportation Society of America's 11th Annual Meeting and Exposition. Washington, 

DC: ITS America.

36. Paruchuri, V. 2017. “Adaptive Preemption of Traffic for Emergency Vehicles.” Presented 

at the 2017 United Kingdom Simulation (UKSim) Academy of Mathematics and Systems 

Science (AMSS) 19th International Conference on Computer Modelling Simulation. 

Cambridge, United Kingdom: UKSim and AMSS.

37. Das, D., N. V. Altekar, K. L. Head, and F. Saleem. 2021. “Traffic Signal Priority Control 

Strategy for Connected Emergency Vehicles With Dilemma Zone Protection for Freight 

Vehicles.” Presented at the 2021 Transportation Research Board Annual Meeting. 

Washington, DC: Transportation Research Board. 

38. FHWA CDA Program. "CARMA Ecosystem" (Confluence web page). https://usdotcarma.atlassian.net/wiki/spaces/CRMECO/pages/1093468253/CARMA+Ecosystem, last 

accessed August 31, 2022. 

39. Institute of Transportation Engineers. 2020. Traffic Management Data Dictionary

(TMDD) Standard v3.1 for the Center-to-Center Communications.

40. AASHTO. 2018. A Policy on Geometric Design of Highways and Streets. Washington, 

DC: AASHTO.

41. University of Virginia School of Engineering and Applied Science. 2022. “Connected 

Vehicle Pooled Fund Study” (website). https://engineering.virginia.edu/cv-pfs, last 

accessed September 9, 2022. 

42. SAE. 2016. Dedicated Short Range Communications (DSRC) Message Set Dictionary. 

J2735_201603. Warrendale, PA: SAE. 

https://www.sae.org/standards/content/j2735_201603/, last accessed May 24, 2022. 

43. Thomas, B. 2016. “Proposed Rule Would Mandate Vehicle-to-Vehicle (V2V) 

Communication on Light Vehicles, Allowing Cars To 'Talk' To Each Other To Avoid 

Crashes.” Washington, DC: NHTSA. https://one.nhtsa.gov/About-NHTSA/PressReleases/ci.nhtsa_v2v_proposed_rule_12132016.print, last accessed June 17, 2022.

44. Voigt, A. P., and M. W. Goolsby. 1998. Evaluation of Changeable Lane Assignment 

System for Daily Operation. Report No. TX00/2910 1. College Station, Texas: The Texas 

A&M University System. https://static.tti.tamu.edu/tti.tamu.edu/documents/2910-1.pdf, 

last accessed May 6, 2022.

45. Transportation Research Board. 2016. Highway Capacity Manual, Sixth Edition: A Guide 

for Multimodal Mobility Analysis. Washington, DC: National Academy of Sciences. 

https://www.trb.org/Publications/hcm6e.aspx, last accessed May 6, 2022. 

HRDO/12-22(WEB)E Recycled

Recyclable

Recommended citation: Federal Highway Administration,

Cooperative Automation Research: CARMA Proof-of-Concept TSMO 

Use Case Testing: Traffic Incident Management Concept of Operations

(Washington, DC: 2022) https://doi.org/10.21949/1521920.