Office of Safety and Operations 

Research and Development 

1

BENEFITS TO TRANSPORTATION1

 Improve Energy Efficiency

The smoothed vehicle 

trajectories minimize, and 

possibly eliminate, speed 

fluctuations and stopping 

times, which, in turn, may improve 

general fuel economy by up to 

30 percent. Less stop-and-go traffic 

would also contribute to smoother 

driving experiences.

 Improve Travel Delays

With the optimized signal 

timing plan and reduced 

stop-and-go traffic at 

signalized intersections, 

road users could experience up to 

65 percent less stop time during 

travel and shorter commute times.

 Maintain Safety

In 2021, more than 

42,000 people were 

killed in motor vehicle 

traffic crashes on U.S. roadways. 

With intersections being potentially 

unpredictable, CDA technology could 

reduce the chances of collisions, 

improving road safety for all users 

with enhanced awareness.

Adaptive Traffic Signal Control Optimization in a 

Cooperative Driving Automation (CDA) Environment 

Automated vehicles can navigate signalized intersections with 

enhanced safety and efficiency by establishing communication with 

smart infrastructure. The functionality developed in this project 

demonstrates CDA’s potential to benefit automated vehicles by 

supporting them as they navigate signalized intersections with 

adaptive settings.(1) As shown in figure 1, vehicles approaching an 

intersection may share status and intent information (such as the 

vehicle’s current location and speed, the intended future path, etc.) 

with a roadside unit (RSU).(2) RSUs use this information to continuously 

estimate the time each vehicle will enter the intersection box and 

optimize the signal timing plan. The signal timing plan optimization 

aims to efficiently serve the incoming traffic and minimize the overall 

travel delay at the intersection. 

Vehicles may also receive signal phase and timing (SPaT) inputs from 

the traffic signals connected to the RSU once in range.(3) SPaT inputs 

communicate to the vehicle current and future phases and intervals 

for the traffic light and how much time remains for those intervals. 

The vehicle can then determine how best to proceed based on the 

intervals. Vehicles can automatically adjust and smooth their 

trajectory to minimize their stopping times by reducing their speed ahead of a yellow light change. They can also 

pass through an intersection during a green interval with a higher speed, within designated limits. This action will 

allow for smoother transitions through intersections, better flowing traffic patterns, fewer delays, less energy 

consumption, and less backward shock-wave propagation.(1)

© Getty Images.

Figure 1. Graphic. Vehicles enterting a signalized 

intersection with adaptive settings.

Source: Federal Highway Administration (FHWA).

1 From a report in progress: S. Soleimaniamiri, X. Li, H. Yao, A. Ghiasi, G. Vadakpat, P. Bujanovic, and T. Lochrane. CARMA Proof-of-Concept TSMO Use Case 3 Algorithm. Washington, DC: 

Federal Highway Administration.
2

EVALUATION OF THE CONCEPT

The research team first conducted 

simulation experiments to evaluate and 

fine-tune the developed algorithms for 

four cooperation classes, as defined by SAE 

International® J3216TM.(4) In scenarios where 

vehicles and infrastructure are equipped 

with CDA technology, the objective of the 

experiment is to minimize travel delay at 

the intersection by optimizing the signal 

timing plan and to achieve smoother 

vehicle trajectories with relatively higher 

speed when the vehicle enters the 

intersection box. This approach results in a 

reduced departure headway between 

consecutive vehicles, thereby increasing 

throughput. The results show that the 

developed algorithms reduce average 

travel delay, fuel consumption, and 

stopping time at signalized intersections 

with adaptive traffic signals.

Figure 2 and figure 3 depict vehicle 

trajectories (space-time motion) for 

human-driven and CDA vehicles, 

respectively, in a selected lane. The optimal cycle length for the human-driven vehicle scenario was selected. The vertical 

axis represents space in meters, while the horizontal axis represents time in seconds. In these graphs, each solid line 

corresponds to the trajectory of a single vehicle, and a change in the line’s slope directly correlates to a change in the 

vehicle’s speed. For example, as the slope of the line increases, the speed of the vehicle also increases and vice versa. 

These visuals highlight crucial observations. First, the optimized signal timing plan in the CDA vehicle scenario can 

efficiently allocate the intersection resources to the incoming traffic and eliminate the queue and backward shockwave propagations at the intersection. Second, unlike human-driven vehicles, which come to a complete stop and 

wait at the signal for a signal change before entering the intersection, vehicles equipped with automation Level 3 

and using Class D(4) cooperation show a smoother flow by slowing down before reaching the intersection.

These graphs demonstrate that the algorithms developed in this research project effectively eliminate stop-and-go 

traffic patterns and backward shock-wave propagations. This feature ultimately enhances the travel experience and 

reduces traffic time for a vehicle navigating a signalized intersection.(4)

After conducting traffic simulation studies, the team performed several levels of proof-of-concept (PoC) testing with 

full-sized FHWA vehicles and infrastructure equipped with CDA on controlled test tracks. The testing included three 

groups of scenarios with different numbers of FHWA vehicles and initialization conditions. Traffic signal configuration, 

vehicle placement, and maximum vehicle speed were modified for each test case to evaluate and test the proposed 

framework and algorithms. The research team designed tests to assess various operational aspects, including 

communication, safety, mobility, and the smoothness of vehicle trajectories. All testing took place at the Turner-Fairbank 

Highway Research Center and evaluated the system performance for critical edge case scenarios, such as multiple 

vehicles simultaneously approaching an intersection and competing to receive a green light interval. After several initial 

testing rounds to verify the implemented algorithms, the U.S. Department of Transportation Volpe National 

Transportation Center led additional testing rounds to validate the algorithms and the findings, helping set a 

foundation for further research and development.

ADAPTIVE TRAFFIC SIGNAL CONTROL OPTIMIZATION IN A CDA ENVIRONMENT 

Time (sec)

Space (meter)

Time (sec)

Speed (meter/sec)

3600 3700 3800 3900 4000 4100 4200 4300 4400 4500

1400

1200

1000

800

600

400

200

0

12.5

10.0

7.5

5.0

2.5

0.0

R

R

G R R R G G G G

Y Y Y Y Y

Figure 2. Graph. Trajectory of human-driven vehicles as they 

approach a fixed-time traffic signal over time.

Source: FHWA.

Figure 3. Graph. Trajectory of automation Level 3, cooperation 

Class D(4) vehicles as they approach an adaptive traffic signal over time.

Space (meter)

Time (sec)

Speed (meter/sec)

3600 3700 3800 3900 4000 4100 4200 4300 4400 4500

1400

1200

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800

600

400

200

0

12.5

10.0

7.5

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Space (meter)

Time (sec)

Speed (meter/sec)

3600 3700 3800 3900 4000 4100 4200 4300 4400 4500

1400

1200

1000

800

600

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0

12.5

10.0

7.5

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R

R

G R R R R G G G G G

Y Y Y Y Y Y

R

R

G R R R G G G G

Y Y Y Y Y

Source: FHWA.
USE CASE ARCHITECTURE

While the algorithms were 

developed and simulations were 

conducted for all different CDA 

cooperation classes defined in SAE 

International J3216,(4) the 

implementation of this use case on 

the CARMASM(5) ecosystem focuses 

solely on cooperation Class D.(4) 

The components of the CARMA 

ecosystem used in this cooperation 

class include CARMA PlatformSM,

(6)

CARMA StreetsSM,

(7) and the Vehicleto-Everything (V2X) Hub.(8) Figure 4 

provides a look at how each aspect 

of CARMA infrastructure works 

together. In this architecture, 

vehicles are equipped with CARMA 

Platform and share status 

information via the RSU(1) using the 

basic safety message (BSM) and 

customized mobility operations messages (MOM) and mobility path messages (MPM). CARMA Streets and the V2X Hub 

reside within the infrastructure and are jointly responsible for processing the information received from vehicles. These 

systems estimate the time vehicles can enter the intersection box and optimize the signal timing plan. CARMA Streets 

also communicates with the traffic signal controller to manipulate the signal timing plan and broadcasts SPaT and 

advisory messages to vehicles.(3) CARMA Platform then controls the vehicle trajectory accordingly to minimize stopping 

time and optimize vehicle energy and fuel efficiency.

RESULTS AND LESSONS LEARNED

The results of analyses from testing show that the PoC 

frameworks meet a set of key objective metrics that the research 

team considered to be related to message processing, 

communication rates, and algorithm logic. These metrics include, 

in particular, vehicle prioritization when optimizing the signal 

timing plan, vehicle trajectory sequencing, estimation of the time 

the vehicles enter the intersection during different signal phases, 

and adherence to specified deceleration and acceleration 

boundaries. One aspect not considered in this test that could be 

considered in future testing is vehicle-to-vehicle communication. 

While the team identified some limitations through data 

collection and analysis, these limitations can be addressed as part 

of future CDA program efforts.

3

ADAPTIVE TRAFFIC SIGNAL CONTROL OPTIMIZATION IN A CDA ENVIRONMENT

STANDARDS

This technology meets the following standards 

as set by SAE International:

 SAE J3216_202107: Taxonomy and 

Definitions for Terms Related to CDA for 

On-Road Motor Vehicles.

(4)

 SAE J3016_202104TM: Taxonomy and 

Definitions for Terms Related to Driving 

Automation Systems for On-Road Motor 

Vehicles.

(11)

 SAE J2735_202007TM: Vehicle-to-Everything 

Communications Message Set Dictionary.

(10)

Source: FHWA.

Figure 4. Graphic. CARMA design and architecture for adaptive traffic 

signal optimization use case for automation Level 3, cooperation Class D. 

(See references 3, 4, 5, 6, 7, 8, 9, and 10).
TITLE OF TECHBRIEF

4

For more information, please contact the CDA Program at CDA@dot.gov

FHWA-HRT-24-025

HRSO-40/12-23(200)E

ADAPTIVE TRAFFIC SIGNAL CONTROL OPTIMIZATION IN A CDA ENVIRONMENT

CONCLUSIONS

This test case proved the benefits of CDA application in signalized intersections with adaptive settings and helped 

provide a better understanding of its advantages. Potential for future work remains high. In particular, the developed 

framework can be significantly improved by completing further research in the following areas:

TO LEARN MORE

V2X HUB

https://github.com/usdotfhwa-OPS/V2X-Hub

CARMA PLATFORM 

https://github.com/usdotfhwa-stol/carma-platform

CARMA STREETS

https://github.com/usdotfhwa-stol/carma-streets

 Large-Scale Testing

Applying vehicle-tovehicle communications 

in an adaptive traffic 

signal environment 

allows for higher scale deployments, 

which would result in increased 

confidence and reliability when 

quantifying individual systems. 

 Mixed-Traffic Environment

Extending this use 

case to test in a 

mixed-traffic 

environment, where only part of 

the traffic is equipped with CDA 

technology, would further research 

that will accelerate industry 

deployment.

 More Dynamic Situation

Using more complex 

components (lane 

changes, multiple vehicles, 

vulnerable road users, 

presence of incidents) will help 

improve the technology.

References

1. Soleimaniamiri, S., X. S. Li, H. Yao, A. Ghiasi, G. Vadakpat, P. Bujanovic, T. Lochrane, J. Stark, S. 

Racha, and D. Hale. 2022. FHWA Cooperative Automation Research: CARMA Proof-of-Concept 

Transportation System Management and Operations Use Case 3–Traffic Signal Optimization with 

CDA at Signalized Intersections. Report No. FHWA-HRT-22-052. Washington, DC: Federal Highway 

Administration.

2. American Association of State Highway and Transportation Officials (AASHTO), Institute of 

Transportation Engineers (ITE), and National Electrical Manufacturers Association (NEMA). 

2020. National Transportation Communications for ITS Protocol Object Definitions for Roadside 

Units (RSUs). NTCIP 1218 v01.38. Washington, DC: American Association of State Highway and 

Transportation Officials. 

https://www.ntcip.org/file/2021/01/NTCIP-1218v0138-RSU-toUSDOT-20200905.pdf, last 

accessed May 23, 2023.

3. Intelligent Transportation Systems Joint Program Office. n.d. Signal Phase and Timing (SPaT).

Washington, DC: USDOT. https://www.itskrs.its.dot.gov/sites/default/files/doc/07_SPaT%20

Challenge_FINAL%20508%20VERSION_06_23_21.pdf, last accessed October 24, 2023.

4. SAE International. 2020. Taxonomy and Definitions for Terms Related to Cooperative Driving 

Automation for On-Road Motor Vehicles. SAE J3216_202107. Warrendale, PA: SAE International. 

https://www.sae.org/standards/content/j3216_202107/, last accessed October 19, 2020.

5. USDOT. n.d. “CARMA” (webpage). https://its.dot.gov/cda/, last accessed May 30, 2023.

6. FHWA. n.d. “carma-platform” (software and configuration files in GitHub repository). 

https://github.com/usdot-fhwa-stol/ carma-platform, last accessed April 12, 2023. 

7. FHWA. 2023. “carma-streets” (software and configuration files in GitHub repository). 

https://github.com/usdot-fhwa-stol/carma-streets, last accessed April 12, 2023.

8. FHWA. 2023. “V2X-Hub” (software and configuration files in GitHub repository). 

https://github.com/usdot-fhwa-OPS/V2X-Hub, last accessed April 13, 2023.

9. AASHTO, ITE, and NEMA. 2019. National Transportation Communications for ITS Protocol Object 

Definitions for Actuated Signal Controllers (ASC) Interface. NTCIP 1202 v03A. Washington, DC: 

AASHTO, ITE, and NEMA. https://www.ntcip.org/file/2019/07/NTCIP-1202v0328A.pdf, last 

accessed May 2019.

10. SAE International. 2020. V2X Communications Message Set Dictionary. SAE J2735_202007. 

Warrendale, PA: SAE International. https://www.sae.org/standards/content/j2735_202007, 

last accessed June 21, 2022. 

11. SAE International. 2021. Taxonomy and Definitions for Terms Related to Driving Automation 

Systems for On-Road Motor Vehicles. J3016_202104. Warrendale, PA: SAE International. 

https://www.sae.org/standards/content/j3016_202104, last accessed April 30, 2021.

Recommended citation: Federal Highway Administration. Adaptive Traffic Signal Control 

Optimization in a Cooperative Driving Automation (CDA) Environment (Washington, DC: 2023) 

https://doi.org/10.21949/1521465