Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

PUBLICATION NO. FHWA-HRT-22-098 SEPTEMBER 2022

Cooperative Automation Research: 

CARMA Proof-of-Concept TSMO 

Use Case Testing: Transit Management 

(Transit Signal Priority) Concept 

of Operations
FOREWORD

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

Program, formerly known as the CARMASM 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 is a research environment that enables 

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 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 (ConOps), which is the sixth in a series of nine ConOps focused on 

TSMO use cases and capabilities, is focused on transit management. 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 Federal Highway Administration (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. 

Recommended citation: Federal Highway Administration, Cooperative Automation Research: 

CARMA Proof-of-Concept Transportation System Management and Operations Use Case 

Testing: Transit Management (Transit Signal Priority) Concept of Operations (Washington, DC: 

2022) https://doi.org/10.21949/1521927.
TECHNICAL REPORT DOCUMENTATION PAGE

1. Report No.

FHWA-HRT-22-098

2. Government Accession No. 3. Recipient’s Catalog No.

4. Title and Subtitle

Cooperative Automation Research: CARMA Proof-of-Concept TSMO 

Use Case Testing: Transit Management (Transit Signal Priority) Concept 

of Operations

5. Report Date

September 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 (ORCID: 0000-0001-9060-3216), Sujith Racha 

(ORCID: 0000-0002-5217-0779) 

8. Performing Organization Report No.

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS)

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 2021–March

2022

14. Sponsoring Agency Code

HRSO-50

15. Supplementary Notes

The Federal Government Task Managers were Govindarajan Vadakpat (HRSO-50; ORCID: 0000-0001-9060-3216) 

and Taylor Lochrane (HRSO-40; ORCID: 0000-0002-1933-6554). 

16. Abstract

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

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

objective of this project is to advance the CARMASM ecosystem to enable further capabilities for CDA participants to 

interact with the road infrastructure, to enhance infrastructure performance, to improve network efficiency, and 

ultimately, to reduce traffic congestion through transportation systems management and operations (TSMO) 

strategies on arterials. The concept of operations focuses on TSMO transit management (TM) transit signal priority 

(TSP), which investigates active traffic management applied to traffic signal corridors. TSP is a TM tool that helps a 

transit vehicle maintain schedule or headway adherence and reduce travel time delay attributable to traffic signals. 

The proposed approach for TSMO TM TSP has three components: local traffic signal optimization to reduce delay to 

one or more transit vehicles using CARMA Streets, corridor-coordinated TSP to optimize signal timing between 

transit stops using CARMA CloudSM, and a transit dwell hold strategy to schedule the transit vehicle into a “transit 

green band” between transit stops using CARMA PlatformSM. The proposed control framework is expected to 

improve schedule or headway adherence. 

17. Key Words

Cooperative automated driving system-equipped vehicle use, 

signalized intersection, cooperative driving automation, CARMA, 

signal optimization, traffic signal priority, transit signal priority, 

incident management

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

60 

22. Price

n/a

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

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
iii

TABLE OF CONTENTS

CHAPTER 1. SCOPE AND SUMMARY ................................................................................... 1

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

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

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

Objectives ........................................................................................................................... 2

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

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

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

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

Opportunities for Changes ..................................................................................................... 6

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

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

Infrastructure Owners and Operators ................................................................................ 16

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

Organizational/Institutional Changes................................................................................ 18

Technical/Technological Changes .................................................................................... 19

Operational Policy Changes.............................................................................................. 20

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

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

Technological Framework for TSMO Basic Arterial Traffic UC .................................... 23

TSMO UC TSP-1: Adaptation of the Traffic Signal Timing to the Traffic Signal to 

Accommodate Active Priority Requests ........................................................................... 26

TSMO UC TSP-2: Queue Clearance to Allow a Transit Vehicle to Approach a Nearside 

Stop ................................................................................................................................... 28

TSMO UC TSP-3: Consideration of Downstream Congestion When Making the Decision 

to Grant or Deny an Upstream TSP Request .................................................................... 29

TSMO UC TSP-4: Providing a Queue Jump to Allow a Transit Vehicle to Depart a 

Traffic Signal before Other Lanes of Traffic to Allow Transits to Change Lanes if the 

Route Makes a Left Turn at the Downstream Intersection ............................................... 31

TSMO UC TSP-5: Providing a Hierarchical Control System to Address Different Types 

of Transit Service in a System .......................................................................................... 33

TSMO UC TM-1: Bus Lane Intermittent Priority That Allows a Lane Reserved for 

Transit Vehicles to Be Shared with Passenger Vehicles When No Transit Vehicles Are 

Present ............................................................................................................................... 35

TSMO UC TM-2: Eco-Transit Driving That Changes the Transit Vehicle’s Speed Based 

on the SPaT Data That Indicate When the Traffic Signal Will Change to Green ............ 37

Infrastructure Configuration and Needs ............................................................................ 37

Summary of TSMO Needs and Requirements ................................................................... 39

Performance Metrics and Targets Traffic Flow ................................................................ 42

Performance Metrics for Traveler Behavior ..................................................................... 42

Performance Metrics for Traffic Performance .................................................................. 43

CHAPTER 4. OPERATIONAL SCENARIOS ........................................................................ 45
iv

Scenario 1: Arterial System with Transit Operations ....................................................... 45

Scenario 2: Route Priority and Multiple Service Types .................................................... 46

Scenario 3: BLIP ................................................................................................................... 48

Scenario 4: Work Zone in Transit Corridor ...................................................................... 48

Scenario 5: Eco-Transit Driving .......................................................................................... 49

CHAPTER 5. ANALYSIS OF THE PROPOSED SYSTEM ................................................. 50

Summary of Potential Benefits and Opportunities ............................................................ 50

System Validation Plan......................................................................................................... 50

Simulation Testing ............................................................................................................ 50

Field Testing ..................................................................................................................... 51

Summary of Impacts............................................................................................................. 51

Disadvantages and Limitations ............................................................................................ 51

REFERENCES ............................................................................................................................ 53
v 

LIST OF FIGURES

Figure 1. Chart. Overview of SAE International cooperation classes and automation 

levels.(2) ................................................................................................................................. 7

Figure 2. Illustration. CARMA ecosystem.(10) .............................................................................. 10

Figure 3. Diagram. CARMA Cloud components.(11,12,13) ............................................................. 11

Figure 4. Diagram. Software architecture of the MRP.(14,18) ........................................................ 13

Figure 5. Diagram. Software architecture of MMITSS VSP.(14,18) ............................................... 14

Figure 6. Illustration. An arterial traffic signal corridor with transit operations. ......................... 24

Figure 7. Illustration. Time space diagram showing the trajectory of a transit vehicle that 

stops at a transit stop to board and alight passengers. ......................................................... 25

Figure 8. Illustration. CARMA Cloud with a new transit layer and transit objects.(11,12,13) ......... 27

Figure 9. Illustration. Nearside transit stop with standing traffic queue. ...................................... 28

Figure 10. Illustration. Downstream congestion along a transit route. ......................................... 30

Figure 11. Illustration. Queue jump UC. ...................................................................................... 32

Figure 12. Illustration. An arterial with multiple transit routes and types of service. .................. 34

Figure 13. Illustration. BLIP. ........................................................................................................ 36

LIST OF TABLES

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

Table 2. Examples of cooperative signalized intersection features. ............................................... 8

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

Table 4. Infrastructure needs for road users and responsibilities of road users and IOOs. .......... 38

Table 5. Exchanges between RSE and vehicles............................................................................ 38

Table 6. Operational needs for vehicles and infrastructure in TSMO TM UCs. .......................... 39

Table 7. Functional requirements for vehicles and infrastructure in TSMO TM UC. .................. 41

Table 8. Summary of performance measures for TSMO UCs evaluation. ................................... 43
vi 

LIST OF ACRONYMS

ADS automated driving system 

BLIP bus (transit) lane intermittent priority

BRT bus rapid transit 

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

FHWA Federal Highway Administration

GHG greenhouse gas 

GPS global positioning system 

HRSO Office of Safety Operations Research and Development

HD high definition 

Hz hertz

I2V infrastructure-to-vehicle

ID identifier

IOOs infrastructure owners and operators 

MMITSS Multimodal Intelligent Traffic Signal System

NTCIP National Transportation Communications for Intelligent Transportation Systems 

Protocol 

OBU onboard unit 

PRS priority request server

R&D research and development

RSE roadside equipment 

RSU roadside unit 

SPaT signal phase and timing 

SRM signal request message

SSM signal status message

STOL Saxton Transportation Operations Laboratory 

TM transit management

TSMO transportation systems management and operations 

TSP transit signal priority

UC use case

V2I vehicle-to-infrastructure 

V2V vehicle-to-vehicle

V2X vehicle-to-everything 
1 

CHAPTER 1. SCOPE AND SUMMARY

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 transit management 

(TM), specifically transit signal priority (TSP) or TSP for transit vehicles. 

DOCUMENT OVERVIEW

Background 

The Federal Highway Administration (FHWA) Office of Safety and Operations Research and 

Development (HRSO) performs transportation operations research and development (R&D). 

Onsite R&D is conducted at the Saxton Transportation Operations Laboratory (STOL), which is 

located at the Turner-Fairbank Highway Research Center. HRSO conducts operations R&D

based on a national perspective of the transportation needs of the United States. 

In 2015, HRSO 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 a 

CARMA PlatformSM as an advancement of standard adaptive cruise control (ACC) systems by 

utilizing 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.

Under a subsequent task order, HRSO developed CARMA2, a new reference platform to enable 

research capabilities to be easily shared and integrated into industry research vehicles. The 

project advanced CACC functionality and developed a proof-of-concept platooning application 

that enabled leader–follower behavior and allowed vehicles to begin to negotiate with one 

another. The project also developed the Integrated Highway Prototype I, which integrated speed 

harmonization, lane change/merge, and platooning into one trip. 

Under a current task order, HRSO is producing the third iteration of CARMASM. CARMA3 takes 

the platform into the world of automated driving systems (ADS) with SAE International® 

Level 3 and above automation.(1) The approach takes advantage of an open-source ADS platform

to enable ADS functionality to be used for cooperative automation strategies. 

CARMA CloudSM, CARMA Messenger, and CARMA Streets are also being developed along 

with CARMA Platform. CARMA Cloud represents the infrastructure piece of cooperative 

driving automation (CDA), whereby vehicles and other entities may communicate with the 

infrastructure to increase the safety and efficiency of the transportation network. 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 the 

infrastructure to improve the transportation network performance. CARMA Streets enables 

vehicles to communicate with the infrastructure at intersections and provides an interface to the 

traffic signal controller to optimize travel through an intersection. CARMA Cloud, CARMA 
2 

Messenger, CARMA Streets, and CARMA Platform are open source and 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 

IHP2

IHP2 = Integrated Highway Prototype II. 

Objectives 

This project extends the research from Integrated Highway Prototype II (IHP2) by utilizing 

CARMA Streets, CARMA Platform, and CARMA Cloud to provide CDA participants with the 

capabilities to interact with road infrastructure, including traffic signal controllers. All TSMO 

UCs in this project consider CDA operations on at-grade intersections. The UC in this ConOps 

focuses on active traffic management that incorporates signal optimization, signal coordination, 

and transit vehicle management. This project addresses three objectives: reduce traffic 

congestion, improve energy efficiency, and increase transit mobility, which includes improving 

on-time performance (e.g., not late, or not behind on a design headway by more than a defined 

threshold). This project investigates the extent to which these objectives can be achieved for 

different CDA cooperation classes, as given by the SAE J3216 standard.(2) This project will be 

supported by a team of CARMA participants for development and testing. This document aims 

to address one of TSMO arterial UCs: TM—traffic signal priority. 

Audience

The intended audience for this ConOps includes the following entities: 

• U.S. Department of Transportation and cooperative automation program stakeholders, 

including the Federal Transit Administration. 

• System developers who will create and support CDA algorithms based on the system 

concepts described in this document. 

• Analysts, researchers, and CDA application developers. 
3 

Document Structure

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

described in “Annex A” of ISO/IEC/IEEE Standard 29148:2011.

(3) 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 detail than is described in Standard 29148:2011; titles of some sections may 

have been edited to capture those details more specifically. 

• Chapter 1 defines the scope of the ConOps. 

• Chapter 2 describes the current situation and identifies the need for changes with respect 

to processes and systems that will be affected by the ConOps. 

• Chapter 3 describes the concept for the new TSMO TM system capabilities and its 

operations, and it presents detailed descriptions of operational concepts. 

• Chapter 4 describes operational scenarios of TSMO TM at signalized intersections.

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

impacts, validation plans, disadvantages, and limitations.

5 

CHAPTER 2. CURRENT SITUATION AND OPPORTUNITIES FOR CHANGES

This chapter discusses existing approaches to TM on signalized arterials using TSP. Transit is 

capable of reducing congestion by moving more people per vehicle.

(4)

 Reducing transit delay 

and making transit more schedule/headway reliable makes TSP a more attractive solution.

(5,6)

This chapter highlights the advantages and disadvantages of the existing solutions because transit 

agencies are now motivating the development of new CDA solutions to congestion and energy 

problems at signalized intersections.

BACKGROUND AND CURRENT SITUATION

Various roadway facilities intersecting through a roadway network to provide access to 

commuters can cause conflicts among vehicles from various movement traffic streams. 

Inappropriate operations of a conflict area (e.g., signalized/unsignalized intersections, merging 

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

delay, energy consumption, emissions, driving discomfort, and safety risks. However, 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 use communication and 

automation technologies to allow them to coordinate with each other and with the infrastructure 

to maximize safety and network efficiency. C-ADS-equipped infrastructure components, such as 

CARMA Streets, enable the infrastructure to be an active participant in the coordination of 

vehicle needs—especially special classes of vehicles, including transit. C-ADS-equipped 

vehicles and intersections are part of a connected ecosystem—which relies on V2V, 

vehicle-to-infrastructure (V2I), and infrastructure-to-vehicle (I2V) communications—in which 

each component plays a role to help 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 an 

opportunity for traffic flow improvements at a common conflict area that may produce mobility, 

safety, and environmental benefits. With these emerging technologies, the passing sequence of 

C-ADS-equipped vehicles at an intersection can be improved with proper coordination (e.g., 

allowing movements without conflict to take place simultaneously at an intersection rather than

only allowing one vehicle to proceed at the intersection at a time) to increase traffic throughput. 

Furthermore, vehicles can be aware of downstream traffic and the conflict area’s conditions so 

they can determine the approximate time they can enter the conflict area. Special classes of 

vehicles, including transit vehicles (e.g., bus rapid transit (BRT), light rail, express, local, and 

streetcars), may receive preferential treatment by clearing queues to allow them to reach a 

nearside stop or cross the stop bar earlier than they would without preferential treatment. Among 

various CDA applications related to conflict areas, control strategies near signalized intersections 

have received increasing attention because they are capable of communicating with traffic signal 

controllers and receiving real-time signal phase and timing (SPaT) information. These control 

strategies usually have two aspects. First, the traffic signal timing plan can be optimized to 

efficiently serve different traffic approaches according to their demands. Second, 

C-ADS-equipped vehicles can be controlled to simultaneously smooth their trajectories 
6 

(i.e., paths vehicles follow in space as a function of time) to minimize fuel consumption, driving 

discomfort, and travel delay. A number of studies have been conducted on these two aspects. On 

the traffic signal side, several studies aim to optimize the signal timing plan to improve the 

traffic efficiency (see references 4, 5, 6, 7, 8, and 9). 

OPPORTUNITIES FOR CHANGES

The emergence of CDA leverages V2V and V2I communications to create opportunities for 

vehicles to share information and cooperate. This feature of CDA creates improvements in

TSMO and provides opportunities to improve TM on arterial roadways. Similar to the levels of 

automation defined in SAE J3016,(1)

 the new standard, SAE J3216,(2) defines classes of 

cooperation. The classes address different capabilities of a C-ADS-equipped vehicle that would 

affect its ability to cooperate with other CDA participants (e.g., vehicles and infrastructure). 

Figure 1 summarizes the cooperation classes. 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 the SAE J3216 standard. However, the effects of 

different cooperation classes defined in SAE J3216 have not been investigated. 
Partial Automation of DDT Complete Automation of DDT

RELATIONSHIPBETWEEN CLASSES OFCOOPERATIVE DRIVING 

AUTOMATION (CDA) J3216AND LEVELS OF AUTOMATION J3016

7 

© 2020 SAE International. 

1

Improved object and event detection prediction through CDA class A and 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 with Level 3, 4, and 5 ADS-operated vehicles.

2Class A and B communications are two of many inputs to an ADS’ object and event detection and prediction

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

DAS = driving automation system; DDT = dynamic driving task; N/A = not applicable; TCD = traffic control

device.

Figure 1. Chart. Overview of SAE International cooperation classes and automation 

levels.(2)

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

No driving 

automation 

(human does all 

driving)

Driver assistance 

(longitudinal OR 

lateral vehicle 

motion control)

Partial driving 

automation 

(longitudinal 

AND lateral 

vehicle motion 

control)

Conditional 

driving 

automation

High driving 

automation

Full driving 

automation

NO COOPERATIVE 

AUTOMATION

E.g., Signage,

TCD

Relies on driver to complete the 

DDT and to supervise feature 

performance in real time

Relies on ADS to perform complete DDT under defined 

conditions (fallback condition performance varies 

between levels)

CDA CLASSES

Type

CLASS A 

STATUS SHARING

CLASS B

INTENT SHARING

CLASS C

AGREEMENT 

SEEKING

CLASS D 

PRESCRIPTIVE

Here I am 

This is 

what I 

plan to do

Let’s 

do this 

together

I will do as 

directed

E.g., Brake lights, 

traffic signal

E.g., Turn signal 

merge

E.g., Hand

signals, merge

E.g., Hand

signals, lane

assignment by

officials

and what 

I see

Potential for improved object and 

event detection1

Potential for improved object and 

event detection1

N/A

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
8 

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., transit 

vehicle that is behind 

schedule) 

Enabling signal timing changes 

based on the approaching 

vehicle

Eco-approach 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 the 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

Negotiations results

Enabling SPaT changes based 

on the approaching vehicle 

Enabling C-ADS-equipped 

vehicles to plan their motions 

and optimize their velocity 

based on the 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.
9 

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

control framework for C-ADS-equipped vehicles, including passenger vehicles as well as transit 

vehicles, at a signalized intersection in the TSMO context. This ConOps serves as part of the 

CARMA framework and distinguishes between the levels of vehicle automation and classes of 

vehicle cooperation.

The CARMA framework is a platform for R&D of emerging automated driving and 

vehicle-to-everything (V2X) communications. Figure 2 illustrates the CARMA ecosystem, 

which is composed of open-source software products, evaluation and testing support tools, and 

an engagement and support community.
10 

Source: FHWA.

Figure 2. Illustration. CARMA ecosystem.

(10)
11 

The CARMA products 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 TSMO systems, including work zones, traffic, weather, traffic control, roads 

and bridges, and landforms. Each TSMO system contains a collection of system objects that 

represent the key entities and capabilities to 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 a powerful capability for the development of CDA 

applications for other transportation needs such as TM. 

Source: FHWA.

Institute of Transportation Engineers. 2020. Traffic Management Data Dictionary (TMDD) Standard v3.1 for the 

Center-to-Center Communications. Washington, DC: ITE. https://www.ite.org/technicalresources/standards/tmdd/3-1/, last accessed February 10, 2022.

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_index.htm, last accessed 

February 10, 2022.

HD = high definition; WZDx = work zone data exchange; TMDD = Traffic Management Data Dictionary; MUTCD 

= Manual on Uniform Traffic Control Devices, Green Book = A Policy on Geometric Design of Highways and 

Streets.

Figure 3. Diagram. CARMA Cloud components.(11,12,13)
12 

CARMA Streets is a relatively new addition to the CARMA ecosystem. CARMA Streets

provides roadside edge computing that integrates communication between the infrastructure and 

CDA-equipped vehicles as well as other connected vehicles. CARMA Streets provides the 

capability for real-time applications as part of CDA applications.

CARMA Platform provides 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 connected vehicles.

In addition to the emergence of CDA and the development of the CARMA ecosystem, the 

Connected Vehicle Pooled Fund has led the development of the Multimodal Intelligent Traffic 

Signal System (MMITSS), which provides intelligent priority-based traffic signal control using 

data from connected vehicles.(14,15) Figure 4 and figure 5 show the software architecture of 

MMITSS. MMITSS has components that are deployed on the roadside (called the MMITSS 

roadside processor (MRP) as shown in Figure 4), including a central server that supports data 

archiving and a web-based user interface and components that are deployed on the vehicle 

(called the vehicle-side processor (VSP) (figure 5)). 

Both the VSP and MRP have message transceivers that encode the protocol for talking to 

roadside unit (RSU) and onboard unit (OBU) devices as well as a message library that can pack 

and unpack the SAE J2735 messages.(16)

 The VSP hosts the priority request generator that is 

responsible for locating the vehicle on the map—based on the local global positioning system 

(GPS) position and the SAE J2735 MAP (broadcast of data describing physical intersection 

geometry) message received by the OBU—and broadcasting a signal request message (SRM). 

The VSP has a special lights and siren manager component that can sense the lights and siren 

circuit on an emergency vehicle (e.g., firetruck, ambulance, police car). In addition, a data 

capture component called “V2X data client” archives data from the other VSP components. 

Because 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 the device’s 

storage is adequate.

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

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

forward it to the priority request server (PRS). The PRS is responsible for collecting and 

managing requests from multiple vehicles and from the coordination request generator. The 

reason for this design is because MMITSS implements a 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 that is sent to the 

traffic control interface for implementation on the traffic signal controller though National 

Transportation Communications for Intelligent Transportation Systems Protocol (NTCIP) objects 

(call, hold, force-off, and omit).(17)

 The traffic signal controller provides SPaT data that are 

modified by the MAP-SPaT broadcaster to include the MMITSS control schedule. Currently, the 

trajectory-aware and intelligent traffic signal control (i-Sig) applications are not utilized in 

MMITSS.(7)

 They require 20–30 percent minimum market penetration of connected vehicles to 

be effective. The emergence of CDA, the CARMA ecosystem, and MMITSS provides a strong 

case for creating a new effective TM system for signalized arterials.
13 

Source: FHWA. Adapted from Connected Vehicle Pooled Fund Study, 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 4. Diagram. Software architecture of the MRP.

(14,18)
14 

Source: FHWA. Adapted from Connected Vehicle Pooled Fund Study, MMITSS phase 3 development, DOT 

Dynamic Mobility Application Program.

Figure 5. Diagram. Software architecture of MMITSS VSP.

(14,18)
15 

TSMO STAKEHOLDERS

Stakeholders are people and entities influencing travel in the transportation environment. 

Stakeholders may include transportation users engaged in travel on publicly accessible 

roadways, emergency responders, transit vehicles, transit riders, 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 used to travel 

from one location to another. For TSMO, motorized vehicles, including transit vehicles, whether

human-driven or automated, are the main users of the traffic systems at intersections. 

Transportation users’ needs are as follows: 

• Smooth, low-stress, and fast travel. 

• Reliable travel times. 

• Energy-efficient and safe trips. 

• Accurate information to make optimal decisions about driving tasks (decision support 

systems). 

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

and enhance the following improvements: 

• Smoother, faster, and lower-stress travel: Providing traffic signal priority for transit 

vehicles can reduce traffic signal delay.

(4,5,6) This reduction improves the quality of transit 

service and makes it a more attractive mode alternative for travelers. C-ADS transit 

vehicles may reduce fuel consumption when they are provided with SPaT data that will 

enable them to adjust their trajectories when approaching a signalized intersection, where 

they might be delayed.

• Greater operational efficiency and travel time reliability: Delivering intelligent traffic 

signal priority can improve travel time reliability by reducing traffic signal delay and 

allowing vehicles that are behind schedule or have longer headways than desired to return 

to the designed schedule/headway.(7,8,9)

• Improved traffic safety: Utilizing CDA technology can reduce the number of vehicle 

crashes. The National Highway Traffic Safety Administration estimates the combined use 

of V2V and V2I communications has the potential to significantly reduce unimpaired 

driver crashes.

(19)

Table 3 identifies four categories of transportation users and the characteristics and needs of 

each category.
16 

Table 3. Transportation user characteristics and needs.

Driver

Transportation User 

Category User Characteristics and Needs

Human driving Regular human driver 

• Regular human drivers have neither 

connectivity nor automation capability. 

• Regular human drivers have uncertain 

driver behaviors.

• Needs align with general user needs. 

Human driving Transit vehicle 

human driver 

• Transit vehicle human drivers have 

training as highly skilled commercial 

vehicle operators and generally follow 

agency operating policies. 

• Needs align with fleet operator needs. 

Human driving Connected human 

driver 

• Connected human drivers receive 

additional traveler information and can 

make better informed travel decisions. 

• Needs align with general user needs. 

Automated 

driving 

Nonconnected 

ADS-equipped 

vehicle

• Nonconnected ADS-equipped vehicles 

operate independently, rely on local 

sensor information and automated control 

software, and usually have conservative 

behavior to provide increased comfort and 

safety margins.

• Needs include accurately sensing local 

traffic conditions and actuating control of 

vehicles to ensure safety and travel 

efficiency. 

Automated 

driving 

C-ADS-equipped 

vehicle

• Compared with ADS-equipped vehicles, 

C-ADS-equipped vehicles operate by 

partnering with other CDA participants in 

the traffic stream, including the 

infrastructure, to improve overall traffic 

performance. 

• Needs include relying on the availability 

of other vehicles to perform cooperative 

actions and improving overall system 

safety and efficiency while guaranteeing 

individual vehicle travel experiences. 

Infrastructure Owners and Operators
17 

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 levels. Transit providers can be either public, public–private, or private sector agencies that 

provide a variety of transit services (e.g., BRT, express, local, commuter rail, light rail, streetcar, 

and demand responsive services), so that travelers can meet their travel needs by choosing 

alternatives to personal vehicles.

(4,6,7)

The primary goal of IOOs is safe and efficient traffic management. This management practice

includes monitoring and managing traffic and the factors affecting traffic flow, such as incidents, 

weather, intersections, dissemination of routing information, and other actions that increase 

traffic flow efficiency. IOOs may have the following goals: 

• Reducing recurring congestion. 

• 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. Connected and automated vehicles are considered a 

separate mode by travelers. 

From the perspective of IOOs, TSMO may support and enhance the following advances: 

• Faster realization of efficiency goals: Adopting CDA early at existing intersections may 

enable improved transit operations and give IOOs greater congestion management 

abilities to increase throughput, enhance safety, and improve driver experience. These 

benefits may increase as the number of C-ADS-equipped vehicles using the intersection 

increases compared with the total number of users. However, these benefits may also 

reduce the number of transit vehicles required to serve a transit route. 

• Maximized resource utilization for more efficient solutions: Enhancing reliability and 

improving transit operations by reducing travel time can encourage travelers to switch 

modes and utilize transit to meet their travel needs.

(6,9) Traditional approaches to 

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

constraints and inherent limitations in alleviating transportation problems. CDA 

technologies are operational strategies that offer the potential for innovative solutions to 

congestion and travel time variability at intersections. 

• First-mover advantage acquisition: Managing their vehicle fleets and having close 

partnerships with traffic operations departments/agencies gives transit agencies 

first-mover advantage. If operators currently primed to accommodate C-ADS-equipped 

vehicles on their facilities do not voluntarily test and advance this technology, third 

parties outside the operators’ organizations are likely to 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.
18 

• Organizational evolution to accommodate the future of mobility technology: By 

responding to rapid technological change, organizations 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 (i.e., levels of automation and ubiquitous sensing using automated vehicle 

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

key questions need to be considered: What changes must take place to enable CDA system 

deployment? What additional capabilities and possibilities can be expected, including 

deployment of CDA technology-compatible infrastructure systems? This section discusses the 

nature of those changes.

Organizational/Institutional Changes 

The following organizational/institutional changes can be implemented to enable deployment of 

CDA systems:

• Adopt a systems engineering process approach: Develop a ConOps for the system 

(regional level) and for the corridor in question. A systems engineering process is 

important for developing operational scenarios to accommodate CDA applications on 

intersection facilities.

• Develop a performance management system: Design a performance management system 

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

performance targets for all CDA applications and operational alternatives are being 

achieved. C-ADS-equipped vehicles aligned with agency performance standards and 

holistic data requirements can help transportation agencies leverage data sources across 

the organization. 

• Develop a data collection and management system: Create a system in which data can be 

placed in, or be accessible from, a common data environment. Obtain all relevant data in 

realtime from the various vehicles, onboard sensors, wireless devices, RSUs, roadway 

traffic sensors, weather systems, message boards, and TM systems. 

• Include accurate data from a variety of sources: 

o Real-time traffic data: These data include vehicle speed and location information 

collected and disseminated by vehicles as part of a connected system. They also 

include traditional detection sources (e.g., inductive loop detectors, overhead radar, 

closed-circuit television cameras) that provide traffic data for the system.

o Traffic signal plan data: These data include information on planned SPaT at 

signalized intersections from traffic signal controllers. 
19 

o Traffic signal timing data: These data include the actual SPaT data at signalized 

intersections from traffic signal controllers. The actual data may differ from the 

planned data if the control methods include actuated, adaptive, and priority traffic 

signal controls. 

o Transit system data: These data include information on the current status of transit 

vehicles, including the vehicle occupancy, schedule adherence, lateness, or headway 

delay.

o Weather condition data: These data include information on infrastructure-based road 

weather information systems and third-party weather data feeds, which can 

supplement vehicle-acquired weather data.

o Pavement condition data: These data include information on real-time pavement 

surface conditions (e.g., dry, wet, snowy, iced, salted) that can be provided by 

in-pavement sensors. 

o Crowdsourced data: These data, which can be collected from platforms that have 

large installed user bases, can supplement data from other sources. Examples of 

crowdsourced data include smartphone applications that track a user’s movements.

o Historical data: These data can help improve the accuracy of traffic analysis and the 

prediction of traffic conditions. 

Technical/Technological Changes

The following technical/technological changes can enable deployment of CDA systems: 

• Procure new hardware to support technology: 

o Enable connectivity of infrastructures through a popular CDA communication 

protocol (e.g., DSRC or cellular vehicle-to-everything (C-V2X)).

o Improve computing power of infrastructures by installing other hardware for 

deploying CDA applications.

o Equip vehicles that use the CDA system with communication radios (OBUs, vehicle 

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

and other computational resources to implement the new control software.

• Develop/acquire new software: 

o Make use of the frequently collected and rapidly disseminated multisource data 

drawn from connected travelers, vehicles, and infrastructure. 
20 

o Include a vehicle awareness application (e.g., an OBU installed either by the vehicle 

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

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

receive, and disseminate needed CDA data. 

o Enable systems and algorithms that can generate traffic condition predictions, 

alternative scenarios, and solution evaluations in realtime.

o Contain microscopic and macroscopic traffic simulations.

o Incorporate real-time and historical data.

o Utilize traffic optimization models.

o Encourage the constant evaluation, adjustment, and improvement of traffic 

optimization models (which requires an increase in computational capability and

long-term storage of historical data).

o Evolve and improve the software’s algorithms and methods based on performance 

measurements. 

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

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

Operational Policy Changes 

The operational policies of intersections are generally designed to accommodate traffic 

operations that meet the goals of IOOs. The following key questions can be used to determine 

proper operational policies of intersections: 

• 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?

All stakeholders should have clear expectations and incentives to participate. Improved 

throughput and smoother travel experiences are shared goals between IOOs and CDA

applications. Users can also create agreements or compacts to set expectations, encourage 

investments, and measure performance.
21 

Facility Infrastructure Changes

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

categories of facility infrastructure changes might be needed:

• I2V infrastructure (e.g., RSE) to transmit central information to all vehicles within the 

communication area. If nonequipped vehicles are allowed, traditional dynamic message 

signs are used to convey public traveler information. 

• Roadside sensors (e.g., video cameras, radars, or 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 early 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 might impact traffic operations at 

intersections and, therefore, improve system performance and an individual traveler’s 

experience. Transit fleets are attractive early adopters for CDA technologies.

23 

CHAPTER 3. OPERATIONAL CONCEPT OF THE PROPOSED SYSTEM

This chapter details the operational concept of the TSMO TM TSP UC. The chapter describes 

how automated driving technology can be used in a cooperative manner from when 

C-ADS-equipped transit vehicles enter the communication area of signalized intersections to 

provide traffic signal priority to reduce delays and improve the quality of service. The CARMA 

ecosystem and the MMITSS are key technological advances that enable a CDA approach to 

TSMO TM using TSP. 

TECHNOLOGICAL FRAMEWORK FOR TSMO BASIC ARTERIAL TRAFFIC UC

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

used for the TSMO TM UC. Many TSMO strategies can be used for TM. This framework

focuses on an arterial with several signalized intersections and transit stops, as illustrated in

figure 6. At any time, several transit vehicles might be traveling on the arterial or on the cross 

streets. Each transit vehicle is serving a run on a route that has a defined schedule or headway 

operating plan. All transit vehicles are assumed to be equipped with CDA technologies, and each 

intersection is similarly equipped with an RSU, edge processor, and a traffic signal controller 

that provides SPaT data and is capable of providing priority for transit vehicles when requested. 

CDA-capable transit vehicles have high-definition (HD) maps that will be used to determine the 

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

stop bar. This decision can depend on several factors, including the presence of a nearside transit 

stop, schedule or headway lateness, and vehicle occupancy. If the transit vehicle does send a 

request for traffic signal priority, the RSU will forward the message to the edge processor, where 

it will be considered along with requests from other priority-eligible vehicles. At any single 

intersection, several transit vehicles may have requested priority. 
24 

Source: FHWA. 

Figure 6. Illustration. An arterial traffic signal corridor with transit operations.
25 

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, 

coordination signal timing (cycle length, offset, and phase splits) design is based on general 

passenger vehicle flow along the route. 

Transit vehicles may stop and dwell at the transit stops to allow passengers to board and alight. If 

transit vehicles stop and dwell, they might not progress along with the passenger vehicles and 

could fall behind the traffic signal progression provided by the coordination. Figure 7 illustrates 

this uncertainty in travel along an arterial. The transit vehicle travels along a trajectory until it 

reaches the transit stop. It will either pass the transit stop, if no passengers want to board or 

alight, or it will stop and dwell. The dwell time will depend on the number of boarding and 

alighting passengers and the vehicle type, and possibly, on the schedule/headway compliance. 

Once the vehicle departs the transit stop, it will travel to the next stop and the dwell process will 

repeat. If the total dwell time is too long, the vehicle will have to stop at the signal and wait for 

the next green traffic signal. The shaded area along the transit vehicle’s trajectory illustrates the 

uncertainty in the trajectory. 

Source: FHWA.

Figure 7. Illustration. Time space diagram showing the trajectory of a transit vehicle that 

stops at a transit stop to board and alight passengers. 

TSP is a tool that can be used to provide several potential benefits. The TSMO TSP UCs include 

the following benefits: 

• TSMO UC TSP-1: Adapting the traffic signal timing (e.g., green extension or early 

green) to the traffic signal to accommodate active priority requests. A green extension 

will hold a green signal green longer than it might have been green due to the TSP. An 

early green will provide a green indication earlier than it would have become green 

without TSP. 
26 

• TSMO UC TSP-2: Providing queue clearance to allow a transit vehicle to approach a 

nearside stop. 

• TSMO UC TSP-3: Considering downstream congestion when making the decision to 

grant or deny an upstream TSP request. 

• TSMO UC TSP-4: Providing a queue jump to allow a transit vehicle to depart a traffic 

signal before other lanes of traffic to allow them to change lanes if the route makes a left 

turn at the downstream intersection. 

• TSMO UC TSP-5: Providing a hierarchical control system to address different types of 

transit service (e.g., BRT, express, local, and demand responsive service) in a system. 

In addition to TSP, there are two other TM UCs of interest:

• TSMO TM UC-1: Bus (transit) lane intermittent priority (BLIP), which allows a lane that 

is reserved for transit vehicles to be shared with passenger vehicles when no transit 

vehicles are present. 

• TSMO TM UC-2: Eco-transit driving that changes the transit vehicle’s speed based on 

the SPaT data that indicate when the traffic signal will change to green. 

TSMO UC TSP-1: Adaptation of the Traffic Signal Timing to the Traffic Signal to 

Accommodate Active Priority Requests

Adaptation of the traffic signal timing is one TSP tool that can provide benefits for transit. The 

transit vehicle, using its known location (e.g., GPS), can pinpoint itself on the HD map and send 

an SRM to the RSU that is then forwarded to the CARMA Streets-enabled edge processor. The 

MMITSS PRS, which will be running on the CARMA Streets processor, will receive the request 

and add it to the active request table, which is used to form the signal status message (SSM) that 

is forwarded to the RSU to broadcast as an acknowledgment to the requesting vehicle. The 

MMITSS priority request solver uses the traffic controller configuration and parameters (e.g., 

phases in use, minimum green time, yellow change time, red clearance time, maximum green, 

walk, and pedestrian clear) and current state (active phases and time spent in the current phases)

to formulate an optimal scheduling problem that it solves to determine the best signal timing to 

minimize the delay to the transit vehicle within the signal controller constraints.

If there are multiple transit vehicles requesting priority on the main street and/or cross streets, the 

MMITSS PRS will consider the total (weighted) delay that the signal timing, or schedule, has on 

the collection of requests. Similarly, if the arterial is operated as a coordinated system, MMITSS 

will treat coordination as a form of traffic signal priority and schedule coordination requests 

based on the cycle length, offset, and phase splits. Transit priority requests and coordination 

requests are all considered in the development of the signal timing schedule. The use of the 

weights allows the operating agency to define a preferential policy for different modes and 

operating strategies.
27 

The decision to request priority or to grant the request could depend on several factors, including 

schedule lateness, headway gap, and/or vehicle occupancy. The CDA-capable vehicle may know 

its status in terms of schedule/headway and occupancy. If the vehicle is on schedule/headway or 

if its vehicle occupancy is below a desired threshold, it might not request priority. If the 

CDA-capable vehicle does not have access to its status or the capacity to determine the request 

criterion, the PRS may be able to obtain the transit vehicle status through the TM system. This 

capability would be supported through the addition of a CARMA Cloud layer and data objects 

that can acquire transit data from a TM system. The CARMA Streets-hosted PRS would validate 

any requests through the CARMA Cloud transit layer. Figure 8 illustrates the idea of adding a 

transit layer and transit objects to CARMA Cloud. 

Source: FHWA.

*Potential new system objects for TM. 

TCIP = transit communications interface protocol. 

Figure 8. Illustration. CARMA Cloud with a new transit layer and transit objects.

(11,12,13)

TSMO UC TSP1 is the most common TSP application and represents a CDA class A 

automation.(2) The transit vehicles send their statuses as SRMs to notify the infrastructure of their 

desire for preferential treatment at the traffic signal.
28 

TSMO UC TSP-2: Queue Clearance to Allow a Transit Vehicle to Approach a Nearside 

Stop

Nearside transit stops are attractive to passengers who are boarding and alighting, especially 

when they are connecting to routes on crossing arterials. Figure 9-A and 9-B illustrate the 

nearside transit stop’s situation and the need to clear the queue of vehicles between the transit 

vehicle and the transit stop. 

Source: FHWA.

A. Bird’s-eye view.

Source: FHWA.

B. Space-time trajectory plot. 

Figure 9. Illustration. Nearside transit stop with standing traffic queue.
29 

An adaptation of the traffic signal timing can be made to clear the queue to allow passengers to 

board and alight during the red interval. In the situation shown on figure 9-A, three vehicles are 

queued as the signal transitions from green to yellow. A short extension of approximately 6 s 

would allow these vehicles to clear and the transit vehicle to reach the stop. Depending on the 

time required for the passengers to board and alight, an early green could reduce the traffic 

signal delay.

To determine the time required to advance the transit vehicle to the transit stop, it is necessary to 

estimate the queue length. If the queued vehicles are CDA vehicles, then this estimation is 

straightforward. If some of the vehicles in the queue are not CDA, then queue estimation 

requires additional functionality. If none of the vehicles are CDA vehicles, then only the transit 

vehicle position and velocity will be available to estimate the queue length and the time required 

to clear the queue and advance the transit vehicle to the transit stop. Additional information that 

can provide useful queue information may be available from a stop bar detector in the queueing 

lane. Infrastructure-based cooperative perception can provide the exact queue lengths. The queue 

clearance for nearside stop is a CDA class A application.(2) The transit vehicle sends an SRM to 

the infrastructure, and the infrastructure must know about the nearside stop. This information 

could be available from the CARMA Cloud transit layer or could be locally configured in the 

CARMA Streets edge processor and the MMITSS PRS.

TSMO UC TSP-3: Consideration of Downstream Congestion When Making the Decision to 

Grant or Deny an Upstream TSP Request

Providing priority for a transit vehicle at one intersection may not benefit the vehicle if there is 

congestion on the downstream arterial that will cause further delay. Figure 10 illustrates this UC. 

Congestion is defined as the failure of a traffic signal to fully discharge a queue during one cycle. 

Downstream congestion will significantly impact transit quality of service. If downstream 

congestion is present, a request for priority at an upstream signal will not improve the overall 

transit route performance and might add additional vehicles to the downstream congestion. 

Knowledge of downstream congestion can be derived from CDA vehicles as well as from more 

traditional performance measures that require infrastructure detection. This information can be 

available to the PRS to determine the value of providing priority through the CARMA Cloud 

traffic layer and the transit layer (to determine the route path and location of the congestion). 
30 

Source: FHWA. 

Figure 10. Illustration. Downstream congestion along a transit route.
31 

TSMO UC TSP-4: Providing a Queue Jump to Allow a Transit Vehicle to Depart a Traffic 

Signal before Other Lanes of Traffic to Allow Transits to Change Lanes if the Route Makes 

a Left Turn at the Downstream Intersection

A queue jump uses lane-by-lane signaling and allows a transit vehicle to move in front of other 

vehicles in other lanes. Figure 11 illustrates the queue jump UC. This treatment allows the transit 

vehicle to change lanes to make a turn (e.g., left turn) at a downstream intersection or to prevent 

a vehicle from making a turn in front of the transit vehicle. The latter case is useful for light-rail 

systems when the transit vehicle operates in the middle of the street and there is a left turn to the 

right of the light-rail tracks. 
32 

Source: FHWA.

Figure 11. Illustration. Queue jump UC.
33 

The queue jump requires lane-by-lane signalization. This process is often used when there are 

exclusive transit-only lanes, but it can also be used in more general situations such as arterial 

streets. If there is a queue in front of the transit vehicle, the vehicles in the queue will be allowed 

to proceed when the lane receives a green signal. Because the vehicles in the queue depart ahead 

of the transit vehicle, they will not prevent the transit vehicle from making a lane change.

TSMO UC TSP-5: Providing a Hierarchical Control System to Address Different Types of 

Transit Service in a System

Many transit systems operate multiple routes and types of service along a corridor, as shown in 

figure 11. In this system, a BRT service utilizes the center lane and only has BRT stops at a few 

locations. The BRT stops are not shown in figure 11; the BRT service is shown to use the center 

lane, and the express and local transit stops are shown on the lane farthest to the right of the 

roadway. An express service includes possible multiple express routes that only include a few of 

the transit stops, whereas several local service routes have frequent stops throughout the system. 

Similar to TSMO UC TSP-1, the arterial traffic signal system may be operated as a coordinated 

system of traffic signals. Transit and traffic operating agencies may agree to an operating policy 

that provides a higher degree of priority for some types of service, such as BRT and express. The 

ability to establish a hierarchical policy provides the transit and traffic management agencies 

with a useful tool for ensuring traveler mobility through the corridor.
34 

Source: FHWA.

Figure 12. Illustration. An arterial with multiple transit routes and types of service.
35 

All the transit vehicles are assumed to be CDA capable and can communicate with the RSUs 

at each intersection. Each vehicle will send an SRM, which either can be based on the type 

of service (e.g., BRT and express may always request), or may be sent if the transit vehicle is 

behind schedule/headway. Similar to TSMO UC TSP-1, the eligibility decision could be made 

at the roadside with information from the TM system through the transit layer in CARMA 

Cloud. The transit and traffic operating agencies may determine that some vehicles or routes 

should not receive traffic signal priority or may only receive priority during certain periods. 

The ability to set a hierarchical policy; define the types of service, routes, and schedule/headway 

lateness threshold; and integrate with coordinated traffic signal operations provide a powerful 

TM capability.

The hierarchical control system is a CDA class A application because the vehicles are only 

sharing their status (desire priority, late or behind headway).(2) 

TSMO UC TM-1: Bus Lane Intermittent Priority That Allows a Lane Reserved for Transit 

Vehicles to Be Shared with Passenger Vehicles When No Transit Vehicles Are Present

Figure 12 shows a lane that is reserved for BRT service utilizing the right lane. Use of an 

exclusive lane provides a high level of priority but is a significant reallocation of roadway 

capacity for services that may be less frequent (e.g., 15- to 30-min headways) or services that 

only operate during defined times of the day. The BLIP concept, illustrated in figure 13, utilizes 

CDA class A/C capabilities to allow nontransit CDA-capable vehicles to share the lane.(12)

Vehicles in the lane ahead of a transit vehicle are notified of the presence of a transit vehicle in 

the lane and are expected to change out of the lane. The notification can be shared between CDA 

vehicles or sent from the infrastructure to the vehicles on the downstream links. Vehicles are 

allowed to reenter the lane behind the transit vehicle.
36 

Source: FHWA.

Figure 13. Illustration. BLIP.
37 

One challenge for the BLIP operation occurs when traffic congestion limits the ability for CDA 

vehicles to change out of the lane designated for transit use. This challenge can be addressed 

using class C cooperative lane changing on capable vehicles. This capability makes the potential 

for BLIP deployment more attractive and feasible. BLIP is a class C CDA application because

the transit vehicle is informing the other vehicles that it is approaching in a transit-designated

lane and asking for their cooperation to change out of the lane.(2) The CDA vehicles can 

cooperate to create lane-change opportunities. 

TSMO UC TM-2: Eco-Transit Driving That Changes the Transit Vehicle’s Speed Based on 

the SPaT Data That Indicate When the Traffic Signal Will Change to Green 

Operating expenses is a key factor in the operation of transit systems. Eco-transit driving is a 

CDA application adapted from the CDA eco-driving application that considers the traffic signal 

state through SPaT data, but it also considers the location of transit stops in making intersection 

approach trajectory decisions. If a transit vehicle is approaching a signalized intersection and the 

SPaT data indicate there will be some delay, the trajectory can be optimized for transit vehicle 

fuel or energy conservation. If there is a transit stop between the transit vehicle’s location and the 

traffic signal, then the vehicle can plan its trajectory to include stopping at the transit stop before 

proceeding to the traffic signal. If the transit vehicle is requesting traffic signal priority, the SPaT 

data will change, based on the adapted signal timing, and the vehicle trajectory will need to be 

revised accordingly. 

INFRASTRUCTURE CONFIGURATION AND NEEDS

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

in developing CDA TM operating policies and procedures. 

A key feature of CDA operations is the dynamic V2I interaction, in particular, the exchange of 

real-time vehicular and roadway information that an ADS-equipped transit vehicle can 

understand and share. This project considers RSE that includes an edge processor and a traffic 

signal controller that can be used to adapt traffic signal timing. The RSE can communicate to 

C-ADS-equipped vehicles, irrespective of the particular communication technologies, by using 

the appropriate protocols. C-ADS-equipped vehicles can also share their statuses and what they 

sense about the surrounding dynamic traffic environment for better static and dynamic world 

models. The two-way information exchange, which includes both cooperative perception and 

cooperative vehicle control, constitutes the foundation of CDA. For TM, intelligent infrastructure 

is also a key part of the automation. CDA participants, vehicles, and infrastructure may use this 

information to improve situational awareness and expand their operational design domains. The 

algorithm for several of these UCs leverages CARMA Cloud-based information to support 

sharing information between the TM system and the traffic signal control system. However, 

many of the UCs can be effective without the Cloud-based information. 

A limited set of user needs relevant to operator–traveler interactions is available. Travelers are 

the primary beneficiaries but can also be information providers. Traffic operators, who work on 

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

information from C-ADS-equipped transit vehicles, process and analyze the information with all 

other available information, make appropriate changes to the traffic signal timing, and send the 
38 

resulting pertinent information back to C-ADS-equipped vehicles. Table 4 shows a list of needs 

for road users and IOOs. In this table, road users are C-ADS-equipped transit and passenger 

vehicles, such that a one-way or two-way information exchange can occur between them

and IOOs.

Table 4. Infrastructure needs for road users and responsibilities of road users and IOOs.

Road Users (C-ADS-Equipped Vehicles) IOOs

Get maps that contain transit route and stop 

locations 

Monitor traffic conditions 

Get information on transit vehicle status, 

including schedule/headway lateness and 

vehicle occupancy

Estimate queues

Get information on traffic conditions 

downstream 

Receive traffic condition information from 

travelers

Get information on weather conditions Control traffic signal timing

Get information on accessible/assigned lanes Control lane use 

Get information on passengers requiring extra 

assistance boarding and alighting 

Optimize signal timing considering different 

transit vehicle types and status 

Get SPaT information Inform travelers of the planned SPaT 

Inform IOOs of observed traffic conditions Inform travelers of the accessible 

lanes/assigned lanes

Inform IOOs of observed weather conditions Inform travelers of traffic condition

Inform IOOs of their status, intents, transit 

vehicle occupancy levels 

Inform travelers of weather conditions

Inform travelers of accessible lanes

Based on the proposed control algorithm, the edge processor and intersection controller will 

exchange information for requesting and granting traffic signal priority for transit vehicles, as 

shown in table 5. 

Table 5. Exchanges between RSE and vehicles.

RSE-to-Vehicle

Priority Status and Signal Timing

Vehicle-to-RSE

Cooperative Perception

• SSM (e.g., SRM acknowledgment). 

• SPaT plan. 

• Other vehicles’ information. 

• Infrastructure-based cooperative 

perception such as queue lengths. 

• SRM (e.g., request for signal 

priority).

• Transit vehicle current status, intent, 

etc. 
39 

SUMMARY OF TSMO NEEDS AND REQUIREMENTS

This section describes the operational needs and functional requirements for C-ADS-equipped 

vehicles and the infrastructure. These needs and requirements are specified for different CDA 

cooperation classes and different components of the proposed control algorithm as follows: 

• Static infrastructure data may include HD maps, speed limits, lane restrictions, and transit 

stop locations (by route). 

• A C-ADS-equipped vehicle’s status and intent data may include an identifier (ID) of the 

vehicle (e.g., license plate or temporary anonymous ID), vehicle type, location, speed, 

braking status, heading, priority position, desired time of service, ETA, intersection 

approach, lane, and vehicle role (e.g., transit, BRT, express, local, demand responsive). 

This dataset may vary across different cooperation classes.

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

priority for each C-ADS-equipped vehicle; RSE signal data include the SPaT data. An 

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

vehicles must receive the SPaT data. However, edge processors might not be used for 

transferring information from one C-ADS-equipped vehicle to another if the V2V 

communication range is sufficient in the control area. 

Table 6 provides a list of operational needs. Table 7 provides functional requirements for the 

C-ADS-equipped vehicles, RSEs, and central computer. These requirements are also specified 

for different cooperation classes.

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

Actor ID Operational Need

CARMA Cloud to 

CARMA Platform to

CARMA Messenger

TSMO TM UC-N01 Need to communicate transit schedule delay 

status to transit. 

CARMA Platform to

CARMA Messenger

TSMO TM UC-N02 Need to store and broadcast vehicle status 

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

and route). For transit, also need to include 

schedule information. 

CARMA Platform to

CARMA Messenger

TSMO TM UC-N03 For transit, need to send SRM to RSE once 

it is within the range of the RSE. 

CARMA Cloud to

CARMA Streets

TSMO TM UC-N04 For CARMA Streets, need to receive static 

infrastructure data (e.g., HD maps, speed 

limits, lane restrictions) from CARMA 

Cloud. 

CARMA Platform to

CARMA Messenger

TSMO TM UC-N05 Need to receive static infrastructure, 

vehicle-specific advisory, and SPaT data. 

For transit, also need to include priority 

request status information and 
40 

Actor ID Operational Need

infrastructure-based cooperative perception 

data.

RSE TSMO TM UC-N06 Need to receive vehicle status and intent 

information from C-ADS-equipped 

vehicles.

RSE TSMO TM UC-N07 Need to receive SRM from transit.

RSE to CARMA 

Streets

TSMO TM UC-N08 Need to relay the received data to CARMA 

Streets.

RSE to CARMA 

Streets

TSMO TM UC-N09 Need to receive static infrastructure data, 

vehicle-specific advisory data, and SPaT 

data from CARMA Streets and broadcast 

this information.

CARMA Streets to 

RSE

TSMO TM UC-N10 Need to send SSM to RSE for broadcast to 

transit vehicles to acknowledge receipt of 

SRM. 

CARMA Streets TSMO TM UC-N11 Need to receive and store SPaT data.

CARMA Streets TSMO TM UC-N12 Need to store data from various sources 

(e.g., transit, C-ADS-equipped vehicles, and 

traffic sensors).

CARMA Streets TSMO TM UC-N13 Need to process data and calculate TSPrelated variables (e.g., priority status, signal 

adaptation, queue length and dissipation 

estimation, speed advisory). 

CARMA Cloud to

CARMA Streets

TSMO TM UC-N14 Need to relay work zone and incident data 

to CARMA Streets.

CARMA Streets to

signal controller 

TSMO TM UC-N15 Need to communicate with the signal 

controller to adapt the signal timing.

CARMA Streets TSMO TM UC-N16 Need to aggregate traffic information 

received from individual vehicles and 

sensors.

CARMA Cloud to

CARMA Streets

TSMO TM UC-N17 For CARMA Streets, need to send the 

aggregate traffic information to CARMA 

Cloud. 

All TSMO TM UC-N18 Need to have proper cybersecurity platforms 

and strategies to protect and recover from 

cyber threats.
41 

Table 7. Functional requirements for vehicles and infrastructure in TSMO TM UC. 

ID Functional Requirement Cooperation 

Class(1,2)

TSMO TM UC-R01 A C-ADS-equipped transit vehicle, with at 

least cooperation class A, has an onboard 

computer with storage and computing 

functions.

A and above.

TSMO TM UC-R02 A C-ADS-equipped transit vehicle, with at 

least cooperation class A, broadcasts its 

location, speed, and heading. The 

communication frequency is approximately 

10 Hz or more.

A and above (status 

data only for class A). 

TSMO TM UC-R03 A C-ADS-equipped transit vehicle, with at 

least cooperation class A, determines the 

distance and time to the intersection stop bar 

and sends an SRM when traffic signal priority 

is desired. The vehicle may determine that it is 

not late or behind the desired headway and no 

priority is needed.

A and above.

TSMO TM UC-R04 A C-ADS-equipped transit vehicle, with at 

least cooperation class A, knows where transit 

stops are located and does not send an SRM 

until it has passed the transit stop, or it does 

send an SRM if the stop is a nearside stop.

A and above.

TSMO TM UC-R05 An edge processor at each intersection runs TSP 

algorithms (MMITSS) and communicates

NTCIP to a traffic signal controller.(14,17)

A and above.

TSMO TM UC-R06 The central computer provides transit 

information, such as vehicle lateness and 

occupancy, to the edge processor at a 

signalized traffic intersection.

A and above.

TSMO TM UC-R07 The central computer provides downstream 

traffic congestion information to the 

intersection edge processor.

A and above.

TSMO TM UC-R08 RSE receives status and intent data from 

C-ADS-equipped transit vehicles, with at least 

cooperation class A, within the communication 

range. The communication frequency is 

approximately 10 Hz or more.

A and above.

TSMO TM UC-R09 RSE broadcasts the traffic signal status and 

intent data among C-ADS-equipped vehicles 

through DSRC or C-V2X communications 

within the communication range. The 

communication frequency is approximately 

10°Hz or more.

A and above (optional 

for when CDA 

communication range 

is not enough and 

needs relay with the 

RSEs).
42 

ID Functional Requirement Cooperation 

Class(1,2)

TSMO TM UC-R10 RSE sends vehicle-specific advisory data and 

the determined SPaT through DSRC or C-V2X 

communications within its communication 

range. The communication frequency is 

approximately 10 Hz or more. 

A and above. 

Hz = hertz. 

PERFORMANCE METRICS AND TARGETS TRAFFIC FLOW

The effectiveness of TSMO UCs can be evaluated by measuring the capability of positively 

impacting performance. Performance metrics are presented for traveler behavior and traffic flow. 

Performance Metrics for Traveler Behavior 

Performance metrics for evaluating vehicle operations during execution of this situation include 

the following traveler behaviors: 

• Transit trip travel time: The time a traveler takes to travel from a desired origin to 

destination using a transit service. In a corridor, this time would include the time from 

when a traveler enters the corridor to when the traveler exits the corridor (or the time 

from when a traveler enters the corridor to the end of the traveler’s trip). 

• Passenger vehicle travel time: The equivalent time for a traveler to make the same trip 

using a passenger vehicle rather than transit service. 

• Transit schedule/headway lateness: The ability of transit vehicles to achieve a desired 

schedule/headway plan. 

• Transit traffic signal delay: The amount of time a transit vehicle is stopped at a traffic 

signal, including the time in a traffic queue (e.g., when there is a nearside stop). 

• Passenger vehicle traffic signal delay: The amount of time a nontransit vehicle is stopped 

at a traffic signal, including time spent in a traffic queue. 

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

and I2V that determine whether communication and/or the 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 per vehicle request. 

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

o Frequency of packet loss. 
43 

• Message latency: Time difference between message origination on vehicle A to reading 

of message by infrastructure and vice versa. Latency time includes the time it takes to 

compose the message, the time it takes to send it from vehicle A’s computer to vehicle

A’s OBU, the queuing time on vehicle A’s OBU, the radio transmission time from 

vehicle A to the infrastructure, the message constitution and queueing time on the 

infrastructure’s RSE, the transformation time from the infrastructure’s RSE to the 

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

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

Performance Metrics for Traffic Performance

As shown in table 8, performance metrics for traffic performance evaluate the following impacts 

of TSMO UCs on traffic flow in corridors: traveler throughput (transit and nontransit travelers), 

traffic congestion (level of service), and sustainability. 

Table 8. Summary of performance measures for TSMO UCs evaluation. 

Category Impact Performance Measure

Traveler throughput Increase in the number of 

travelers who can be served (e.g.,

passengers per vehicle)

Number of travelers passing 

through the corridor per hour 

Vehicle throughput Increase in traffic flow volumes Number of vehicles passing 

through the corridor per hour 

Throughput Smoothness of traffic flow Variability of speeds within traffic 

stream—for transit vehicles and 

nontransit vehicles 

Potential increase in delay for 

nontransit vehicles when priority 

is given to transit vehicles

Average traffic signal delay for 

nontransit vehicles 

Sustainability Impact on greenhouse gas 

emissions

Level of carbon dioxide, nitrogen 

oxide, and particulate matterequivalent emissions 

Reduction in energy consumption Amount of energy consumed 

Traffic congestion Potential impact in quality of 

service by shifting priority to 

transit vehicles

Level of service (per Highway 

Capacity Manual)

(20)

Traveler Throughput and Transit

CDA technologies are expected to increase the throughput of travelers for transportation 

facilities by increasing the passenger-per-vehicle densities. Throughput can be quantified by 

measuring the number of travelers passing through a corridor per hour and the variability of 

speeds within a facility segment by mode (transit vehicles and nontransit vehicles). Transit 

vehicles stop at transit stops, which increases their travel time variability, but reducing the 

number of stops at traffic signals will improve their travel time variability. 
44 

Traffic Congestion 

CDA technologies are applied to improve transit performance. This performance could impact 

other traffic traveling in the corridor. Vehicles traveling along with the transit vehicles may 

experience benefits, whereas those vehicles traveling across the transit vehicle routes may be 

negatively impacted. Traffic congestion can be measured by the level of service as defined in the 

Highway Capacity Manual.

(20)

Traveler Throughput and Nontransit

CDA technologies are expected to improve the throughput of transit vehicles but could have a 

negative impact on nontransit vehicles that travel in a corridor. 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 by mode (transit vehicles and nontransit vehicles). Transit 

vehicles stop at transit stops, which increases their travel time variability, but reduced stops at 

traffic signals will improve their travel time variability.

Sustainability

If CDA technologies improve the transit quality of service, then more travelers are expected to 

choose transit as their mode of travel. The mode shift to transit is expected to reduce greenhouse 

gas (GHG) emissions. In addition, smoother operations associated with CDA and TSP could lead 

to lower GHG emissions and energy consumption. Improved travel operations may induce travel 

demand, which could result in higher overall travel volume and increased GHG emissions and 

energy consumption, depending on the greatest modal growth. GHG emissions and energy 

consumption calculation usually relies on data previously obtained by simulation or on observed 

data. For the proposed UC, emissions and fuel consumption can be calculated using the speed 

profiles of transit and nontransit vehicles (trajectories) at high temporal resolution obtained by 

the simulation platform. The proposed performance measures include carbon dioxide, nitrogen 

oxides, particulate matter emissions, and the amount of energy (volume) consumed.
45 

CHAPTER 4. OPERATIONAL SCENARIOS

This chapter identifies important TSMO TM operational scenarios to enhance TSMO in arterial 

corridors, where there is transit service. The operational scenarios also help with understanding 

the impact of early deployment of CDA participants and the UCs discussed in chapter 3 to 

address the needs of transit operations. Five operational scenarios are described. Each scenario 

may include one or more of the UCs discussed in chapter 3. The first operational scenario 

considers the basic use of traffic signal priority at each individual intersection in the corridor. 

This scenario is a CDA version of the common existing approaches to TSP. The second scenario 

extends the basic scenario to consider a corridor with multiple types of transit service and 

multiple routes through the corridor. The third scenario explores the use of CDA for BLIP. The 

fourth scenario considers the use of TSP, when there is a work zone that can cause a 

nonscheduled delay to the transit service. The fifth scenario considers the application of CDA 

transit eco-driving to reduce energy consumption and emissions. These scenarios are designed to 

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

SCENARIO 1: ARTERIAL SYSTEM WITH TRANSIT OPERATIONS

This scenario represents basic TSP operations on a corridor and includes the application of UCs 

TSMO UC TSP-1 (signal timing adaptation) and TSMO UC TSP-2 (queue clearance) for cases 

when there is a nearside stop. This scenario is illustrated in figure 6 and figure 9. The traffic 

signals in the corridor are assumed to be operated as a coordinated system and have coordination 

signal timing plans (e.g., cycle length, offset, and phase spits) that were selected based on the 

traffic volumes for the time-of-day period. 

In this scenario, transit vehicles operate in both directions based on a fixed schedule (e.g., with a 

transit vehicle every 10 min) on a major arterial and on the cross streets with a fixed schedule 

(e.g., with a transit vehicle every 15 min). As each transit vehicle enters the range of an RSU, it 

will check to see whether it is within the extent of the HD map and on an approach to a 

signalized intersection. If it is, it will form an SRM based on its speed and distance to the 

intersection stop bar. 

The RSU will forward any received SRMs to the CARMA Streets edge processor, where the 

MMITSS PRS resides. First, the PRS will determine whether there are any transit stops between 

the vehicle’s current location and the intersection stop bar by using transit route information 

obtained from CARMA Cloud. If there is no transit stop, the PRS will check to determine 

whether the vehicle is behind schedule by using information obtained from CARMA Cloud. If 

the transit vehicle is not more than a configurable parameter late (e.g., 3 min), the PRS will label 

the SRM “on schedule” and not add it to the collection of active requests. If the transit vehicle is 

more than a determined threshold late, the PRS will add the SRM request to the collection of 

active requests and solve the optimal scheduling problem to determine the best signal timing 

(including coordination) to serve the set of active requests. If there is a stop, the PRS will 

determine the transit vehicle is a nearside stop. If the transit vehicle is not a nearside stop, or if 

the estimated queue does not extend beyond the transit stop, the PRS will hold the SRM in a 

pending state until the transit vehicle passes the transit stop.
46 

Once the transit vehicle passes the transit stop, assuming the schedule lateness test showed a late 

vehicle, the PRS will add the SRM to the collection of active requests, and the optimal 

scheduling problem will be solved (including coordination) to determine the best traffic signal 

timing. Once the transit vehicle crosses the intersection stop bar, the transit vehicle will send a 

cancel SRM so that the transit vehicle’s request can be removed from the collection of requests 

being considered. 

This basic transit priority scenario in an arterial system captures several key operating 

characteristics as follows: 

• Multiple vehicles may be requesting priority at a single intersection at a time. 

• The traffic signals are operated as a coordinated system. 

• Only vehicles that are later than a configurable threshold will receive traffic signal 

priority. 

This scenario will allow performance to be evaluated to better understand the following effects: 

• Benefits of TSMO-TSP on transit operations, including transit travel time, travel time 

variability, and the impact on nontransit vehicles. 

• Impact of schedule/headway (e.g., transit frequency) on system performance. 

• Impact of conflicting transit route requests (e.g., main arterial and cross streets). 

• Impact of providing priority in a coordinated system of traffic signals. 

• Benefit of queue clearance to get transit vehicles to the transit stop locations. 

This scenario highlights several needs. First, the route and run information are required to 

determine whether a transit vehicle is behind schedule. Either the transit vehicle needs to 

make this determination (whether it is behind schedule) and not send an SRM, unless the 

transit vehicle is late or the system needs to track the transit vehicles on the route and make 

an association with the route and run on the schedule, or the transit vehicle needs to send 

additional information to the SRM, with changes to the standard proposed to the SAE 

committee, to identify the route and run. Part of this information is available in the optional 

RequesterDescription field in the SAE J2735 SRM, which allows a string to contain information 

about the transit route but not the run.(16) If the transit vehicle has to make the decision 

about lateness or whether to include the route and run information, CARMA Platform will 

require an interface to the transit vehicle management system to obtain the vehicle status

and/or information. 

SCENARIO 2: ROUTE PRIORITY AND MULTIPLE SERVICE TYPES

This scenario extends the basic TSP system in scenario 1 to include a mixture of transit routes 

(TSMO UC TSP-4) and types of service (TSMO UC TSP-5) that will result in more frequent and 

less periodic transit services in the arterial system. It also provides the system with the ability to 

create a hierarchical priority policy to favor one type of service, or traffic signal coordination, 

over others. In addition, this scenario, illustrated in figure 10, considers the value of providing 

priority at an intersection if there is congestion downstream, where there will be significant delay 

regardless (TSMO UC TSP-3). Overall, this scenario represents a more systematic consideration 

of TSP as an integral part of an arterial traffic system.
47 

The mixture of transit routes (and schedules/headway) creates a situation, illustrated in figure 12, 

where there will likely be more frequent and less periodic requests for priority and the need for 

different priority treatments, such as a queue jump to allow a transit vehicle to change lanes to 

make a left turn at a downstream intersection (figure 11). More frequent transit service may be 

accompanied by more requests for traffic signal priority and potentially more disruption to the 

basic coordinated service for the nontransit vehicles. 

The consideration of transit service type and the creation of a hierarchical policy for different 

service types and/or routes provide a powerful TSP tool for transit and traffic management 

agencies to manage the higher frequency by assigning a higher priority value to certain modes. 

For example, to encourage travelers to use BRT, TSP can be used to help reduce traffic signal 

delays and help create a service that competes with, or exceeds, other modes of travel. Because

BRT has dedicated lanes, it is feasible the travel time experienced by a BRT traveler could be 

less than that experienced by a traveler in a passenger vehicle or other transit modes impacted by 

congestion. Similarly, it may be desired to provide an express service with a higher degree of 

priority. An express service generally includes fewer stops than a standard local transit service. 

Providing TSP to help express transit vehicles should reduce traffic signal delay and improve 

travel time.

Although transit vehicles are a normal part of traffic operations, providing TSP shifts some of the 

intersection capacity away from nontransit vehicles to improve transit performance. In a system 

that is near or at operating capacity, this shift could induce the onset of congestion sooner than if 

no TSP had been provided. Hence, the value of TSP needs to be considered throughout an 

arterial corridor. If a vehicle is granted priority at an upstream traffic signal only to be delayed 

due to downstream congestion, the single intersection benefit may not translate into an overall 

trip benefit. TSMO UC TSP-3 considers this downstream impact on the decision to grant priority 

at upstream traffic signals.

The route priority and multiple types of transit scenarios in an arterial system capture several key 

operating characteristics as follows: 

• Multiple vehicles may request priority at a single intersection at a time. 

• Traffic signals are operated as a coordinated system. 

• Only vehicles that are later than a configurable threshold will receive traffic signal 

priority. 

• More frequent priority requests may be made because of multiple routes and service 

types. 

• A hierarchical policy for considering priority requests can be used to address value and 

frequency of results, with more preference given to highly valued types of service. 

• Consideration of downstream traffic congestion can be used to influence the decision to 

grant an upstream request to ensure reassignment of capacity is valued. 
48 

This scenario will allow performance to be evaluated to better understand the following effects: 

• Benefits of TSMO-TSP on transit operations, such as transit travel time, travel time 

variability, and impact on nontransit vehicles, including different types of transit services.

• Impact of schedule/headway (e.g., transit frequency) on system performance. 

• Impact of conflicting transit route requests (e.g., main arterial and cross streets). 

• Impact of providing priority in a coordinated system of traffic signals. 

• Benefit of queue clearance to get transit vehicles to the transit stop locations. 

• Benefit of having an overall system performance from both transit and nontransit views. 

This scenario highlights several needs. In addition to the schedule/headway lateness identified in 

scenario 1, route information for each vehicle is required to determine whether special service, 

such as a queue jump, is needed. This information can be provided in the SAE J2735 SRM.(16) 

SCENARIO 3: BLIP

This scenario includes the basic TSP UC and utilizes the V2V and V2I CDA capability to 

provide a lane that is utilized for transit but allows other CDA vehicles to occupy the lane until a 

transit vehicle approaches. This operation can benefit both transit and nontransit vehicles by 

creating additional capacity.(21) Transit vehicles in the BLIP lane would likely be BRT service 

vehicles and would ask each traffic signal for priority treatment to help ensure a reduced trip 

travel time.

This scenario will allow performance to be evaluated to better understand the following factors: 

• Ability of TSP to reduce transit vehicle delay at traffic signals. 

• Potential corridor capacity improvements by allowing use of the BLIP lane when transit 

vehicles are not present. 

SCENARIO 4: WORK ZONE IN TRANSIT CORRIDOR

Work zones generally have significant delays attributable to reduced speed and lane availability. 

In this scenario, TSP can be used to help a transit vehicle that has traversed a work zone to get 

back on schedule or headway downstream from the work zone. Also, if there is a work zone 

downstream from a transit vehicle, TSP can help a transit vehicle use the upstream intersections 

to meter the volume of traffic that enters the work zone ahead of a transit vehicle. This metering 

could reduce the queue entering the work zone. This scenario will allow work zone information 

to be available for TM in arterial corridors.

This scenario will allow performance to be evaluated to better understand the following factors: 

• Use of TSP to meter flow of traffic into a work zone. 

• Ability to help a transit vehicle delayed in a work zone to get back on schedule/headway. 
49 

SCENARIO 5: ECO-TRANSIT DRIVING

Reducing operating costs while maintaining a high level of service is important to transit 

agencies. Combining TSP and eco-transit driving to allow vehicles that will be delayed at 

intersections to reduce energy consumption can result in better travel times. If the TSP at a signal 

cannot provide sufficient priority to allow a vehicle to progress through the intersection without 

stopping, the vehicle can use the eco-transit driving capability (adapted from eco-driving) to 

reduce energy consumption. 

This scenario will allow performance to be evaluated to better understand the following qualities: 

• Benefit of TSP to reduce traffic signal delay to transit vehicles. 

• Benefit of eco-transit driving to reduce energy consumption and emissions. 

• Benefit of transferring eco-driving to eco-transit driving as a CDA capability.
50 

CHAPTER 5. ANALYSIS OF THE PROPOSED SYSTEM

This chapter provides an analysis of the benefits, advantages, limitations, and disadvantages of 

TSMO TM UCs on signalized arterials. Scenario 1 from chapter 4 has been selected for analysis. 

The chapter also discuss a high-level system validation plan.

SUMMARY OF POTENTIAL BENEFITS AND OPPORTUNITIES

CDA technologies enable mobility applications that are not achievable by individual 

ADS-operated vehicles. CDA technologies do so by sharing information that can be used to 

increase the safety, efficiency, and reliability of the transportation system, and that may 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 improvement can be 

accomplished, for example, by sharing information about transit vehicle status (lateness, 

occupancy) and estimations of queues and traffic congestion and by providing preferential 

treatment on signalized arterials. Cooperation among multiple participants and perspectives in 

traffic, especially at conflict areas (e.g., intersections, merging roadways), can improve safety, 

mobility, situational awareness, and operations. 

For TSMO TM UC, an integrated control framework is proposed to efficiently manage traffic on 

signalized arterials. Vehicle capabilities, such as determining status (lateness, occupancy) and 

forming SRMs, can be processed on CDA-capable vehicles with CARMA Platform. SRM can be 

shared with the infrastructure with CDA-enabled wireless communications, regardless of the 

particular technology. By using CARMA Streets, the infrastructure can provide adaptive signal 

timing that specifically accommodates transit vehicle requests and needs. By integrating with 

TM systems, CARMA Cloud could provide information about vehicle status, transit routes, and 

transit stop locations. For the purpose of this first investigation analysis, only CARMA Platform 

and CARMA Streets are considered.

SYSTEM VALIDATION PLAN

This section describes system validation methods to validate the developed algorithms and 

software systems for TSMO TM UC. The purpose of the validation testing is to ensure the 

developed TSMO TM UC system can meet the operational needs listed in table 6 for scenario 1. 

Simulation Testing

Simulations can be designed to test the developed algorithm for TSMO TM UC by using the 

performance metrics identified in chapter 3 of transit 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 the evaluation up to an intersection

corridor/network level (compared with the limited number of vehicles and length of the roadway 

for ADS simulators) to study the CDA impact on transportation system performance (as 

measured by traffic performance metrics such as safety, efficacy, stability, and sustainability). 

Traffic simulators can evaluate different scenarios such as traffic demand, TSP, and intersection 
51 

geometry, including nearside bus stops. Usually, the CDA control algorithms will be simplified 

for calibrated/validated CDA behavioral models/algorithms that are implementable for 

large-scale testing.

Field Testing

To ensure the developed algorithm can be reliably and easily implemented into CARMA 

Platform, a set of proof-of-concept tests will be conducted on a closed test track. This test can be 

demonstrated onsite at a signalized intersection. Multiple CARMA vehicles loaded with 

necessary features can be instructed to run loops on the test track to represent continuous driving, 

as shown in figure 13. The operational scenarios discussed in chapter 4 can be tested. The 

purpose of testing can be to verify the software, collect vehicle behavior performance measures, 

and validate whether 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. Field testing will provide data to create a validated evaluation 

model of CDA’s traffic impacts by using simulation.

SUMMARY OF IMPACTS

From a research perspective, the proposed control strategy for TSMO TM UC offers an approach 

for efficiently managing transportation systems at signalized intersections and reducing 

disutilities, such as excessive delays and emissions. The benefits of TSMO TM UC can be 

demonstrated by using CDA transit vehicles that send SRMs to the infrastructure when transit 

vehicles are behind schedule/headway. 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 transit vehicles. From an operations perspective, the proposed control strategy for 

TSMO TM UC presents changes to how TSMO is conducted at signalized intersections. 

Intelligent transportation systems infrastructure would need to be upgraded to accommodate the 

CDA system needs, such as RSE services and supporting information technologies. Agencies

would need to evaluate and build capabilities for operating such emerging systems. The 

conventional processes of monitoring and reporting transportation system performance could be 

revolutionized with the prevalence of C-ADS-equipped vehicles and advanced sensors. 

Conventional strategies for TSMO, which agencies are already familiar with, may be enhanced 

by CDA technologies. 

DISADVANTAGES AND LIMITATIONS

The proposed control strategy for TSMO TM UC provides insights into CDA operations at 

signalized intersections, but it may also present the following limitations: 

• Traffic signal priority for transit vehicles can impact the performance of nontransit 

traffic. The shifting of traffic signal timing to benefit transit vehicles will likely have a 

negative impact on other traffic.
52 

• Simulation of transit operations in terms of schedule adherence is challenging. Most 

simulation studies give every transit vehicle priority at every intersection. Prioritization 

may increase the negative impacts on nontransit vehicles and overestimate the benefits to 

transit vehicles. Ideally, late transit vehicles will benefit, but most transit vehicles should 

be on schedule/headway and not need TSP help.
53 

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HRSO-50/09-22(WEB)E Recycled

Recyclable

Recommended citation: Federal Highway Administration,

Cooperative Automation Research: CARMA Proof-of-Concept 

Transportation System Management and Operations Use Case Testing: 

Transit Management (Transit Signal Priority) Concept of Operations 

(Washington, DC: 2022) https://doi.org/10.21949/1521927.