Exploratory Advanced Research . . . Next Generation Transportation Solutions

Next-Generation Smart Traffic Signals

RHODESNG With IntellidriveSM—the Self-Taught Traffic Control System

memory and processors, including add-on 

processor boards for legacy hardware, now 

offer more powerful computing resources at a 

reasonable cost. Detector technology is also 

presenting new possibilities, including small, 

wireless, individual detectors. Finally, the presence of high-bandwidth communications networks in more locales creates new communications possibilities for transportation agencies. 

This EAR project supports the development 

of the next-generation architecture and algorithms—RHODESNG—that can harness the power 

of these technologies.

Self-Adapting Controllers—

the Online Learners

The goal of this EAR project is an intelligent 

system that continuously adapts its operations 

to changing traffic conditions via high-speed 

communications with vehicles and infrastructure. This system uses self-adaptive algorithms 

to integrate the position, speed, and queue data 

received from vehicles and infrastructure sensors and transmitters, accurately perform highspeed computations, make predictions, and 

continuously adjust its critical parameters based 

on incoming data—a strategy of monitor, learn, 

predict, and respond optimally. In addition, data 

collected through the system can be used for 

regional and other planning functions. 

Key Challenges 

Among the challenges the project has faced are 

incompatibilities that must be resolved when 

interfacing a system with existing hardware, 

software, and communications systems. Such 

problems arise frequently during field implementation. Another technical challenge is incorporating IntelliDriveSM components into the 

RHODESNG system—these technologies are 

under constant development and innovations 

continue to be introduced. 

The Exploratory Advanced Research Program Fact Sheet

The Promise of New Technologies

Although adaptive signal control has demonstrated economic and customer satisfaction 

benefits—reducing travel time, delays, and 

stops—and has been used abroad for more than 

three decades, most jurisdictions in the United 

States still use fixed-length, time-of-day traffic 

control systems. A major barrier to wider adoption of “smart” traffic control systems has been 

cost: initial investments in signal control hardware, communication networks, and comprehensive traffic studies, as well as the cost of 

periodic updates to adjust systems to changing 

traffic conditions. Both conventional and adaptive systems require periodic traffic studies and 

recalibration.

Improvements in technologies associated 

with adaptive traffic control have paved the way 

for a next generation of adaptive systems that 

may spur broader implementation. Hardware 

Can a self-calibrating signal control 

system lead to wider adoption of 

adaptive traffic control systems? The focus 

of “Next Generation of Smart Traffic Signals,” an Exploratory Advanced Research 

(EAR) Program project, is a system that

—with little human intervention—continuously monitors, learns, predicts, and responds to traffic demands and conditions 

with optimal signal timing for prevailing 

conditions. Launched by the Federal Highway Administration (FHWA) in 2007, the 

study is being conducted by Arizona State 

University in Phoenix. 
Future Efforts

Continued development is focused on the 

integration of IntelliDriveSM capabilities with 

RHODESNG. After installation on a hardware 

platform for continuous testing, RHODESNG with 

IntelliDriveSM will undergo software- and hardware-in-the-loop evaluations and field evaluation at the Maricopa Proving Grounds under 

real-world conditions. (The City of Tucson and 

Maricopa County are participating in this project.) 

The results of the evaluations and details of the 

architecture and interfaces will be reported. The 

simulation model and data sets compiled during 

the development and testing of RHODESNG

will be available for dissemination to other 

researchers.

Ultimately, it is envisioned, self-adaptive control 

systems that communicate with moving vehicles 

and intelligent infrastructure will perform many 

functions to improve mobility and safety: 

•	 Gather coordinates, class, speed, and service

requests from IntelliDriveSM-enabled vehicles.

•	 Send traffic advisories directly to IntelliDriveSM-equipped vehicles and, eventually, 

provide in-vehicle control, improving safety. 

•	 Communicate planned signal durations, levels

of congestion, and incident information to 

traffic operations centers for use by incident 

management teams, traffic information systems, and maintenance crews.

•	 Provide selective priority to and exchange information with emergency and transit vehicles 

and, eventually, provide appropriate service 

and priority direction to all vehicles.

•	 Collect data that can be used in local and

regional planning models and traffic forecasts. 

Learn More

For more information on this EAR Program project, 

contact Raj Ghaman, FHWA Office of Operations 

Research and Development, at 202-493-3270 

(email: raj.ghaman@dot.gov).

What Is the Exploratory 

Advanced Research Program?

FHWA’s Exploratory Advanced Research 

(EAR) Program focuses on long-term, 

high-risk research with a high payoff 

potential. The program addresses underlying gaps faced by applied highway 

research programs, anticipates emerging issues with national implications, and 

reflects broad transportation industry 

goals and objectives. 

To learn more about the EAR Program, visit the Exploratory Advanced 

Research Web site at www.fhwa.dot.gov/

advancedresearch. The site features information on research solicitations, updates 

on ongoing research, links to published 

materials, summaries of past EAR Program events, and details on upcoming 

events. For additional information, contact 

David Kuehn at FHWA, 202-493-3414 

(email: david.kuehn@fhwa.dot.gov), or 

Terry Halkyard at FHWA, 202-493-3467 

(email: terry.halkyard@fhwa.dot.gov).

Next-Generation Smart Traffic Signals

RHODESNG With IntellidriveSM—the Self-Taught Traffic Control System

Publication No. FHWA-HRT-09-063

HRTM-04/08-09(1M)E