FACT SHEET

Traffic management systems (TMSs) are complex 

operational systems. TMSs enable human operators to 

facilitate improvements in the efficiency, safety, and 

predictability of travel on the surface transportation system. 

As shown in figure 1, TMSs combine field equipment, 

information technology and communications, software, 

and other tools to collect and synthesize traffic data, 

integrate external systems, and enable command and 

control of intelligent transportation system field devices.

Traffic management relies on timely decisionmaking in 

support of monitoring traffic conditions, detecting and 

managing unplanned events, scheduling and managing 

planned events, improving traffic signal timing, assessing 

and planning for adverse weather, and managing critical infrastructure. The management and operation of TMSs rely on an 

agency expert’s decisionmaking, which involves unique knowledge, skills, and experience. Improving the timeliness and 

effectiveness of decisionmaking is a focus for agencies exploring opportunities to improve how they manage and operate 

TMSs, and improve safety and travel.

Decision-Support Tools

Computer- and noncomputer-based 

decision-support tools (DSTs) support a 

range of decisions. Noncomputer-based 

DSTs are paper-based (e.g., incident 

response plans and decision trees) 

that can be printed and collated into 

references or tools. Computer-based 

DSTs are based online and provide 

real-time information (e.g., traffic 

analyses and lookup tables). 

Computer-based DSTs can potentially 

play a key role in improving the 

real-time decisionmaking of traffic 

operations personnel by complementing 

and enhancing the operational 

capabilities of TMSs.

DSTs can aid operations personnel 

in monitoring and assessing 

conditions (e.g., environmental 

factors, transportation facilities, or 

transportation networks), detecting 

and verifying adverse conditions, and 

identifying and evaluating appropriate 

response strategies to planned and 

unplanned events. DSTs can also help 

agencies achieve more consistent and 

understandable decisionmaking across 

their transportation management staff. 

Within a TMS, computer-based DSTs 

may process vast amounts of data, 

capture the operational processes 

of an agency, and potentially mimic 

the real-time decisionmaking of 

TMS operators.

Types of DSTs

There are three common types of DSTs 

that are useful for traffic management:

Knowledge-Driven Tools

Seek to generate a decision or 

recommendation by emulating 

reasoning and decisionmaking 

behavior. These DSTs process facts 

and business rules instead of data and 

are known as “rules-based systems” or 

“expert systems.” 

Data-Driven Tools

Enable the process, manipulation, and 

visualization of large amounts of data 

to derive business insights that support 

decisionmaking. Business intelligence 

tools and artificial intelligence (or 

machine-learning technologies) 

are the most common types of 

data-driven DSTs.

Model-Driven Tools

Incorporate complex mathematical 

models to derive insights and 

provide forecasting and predictions 

using simulations, optimizations, 

and statistics.

Why Use DSTs?

Decisionmaking is an inherently 

complex process (figure 2), and 

the challenges it presents may be 

compounded by additional factors, 

Decision Support for Traffic Management Systems

Research and Development

© 2018 Tom Saunders, Virginia Department of Transportation.1

Figure 1. Photo. A traffic operations center in Chester, VA. Source: FHWA.2

Figure 2. Illustration. Traffic 

management decisionmaking.
FACT SHEET

including the experience level of 

decisionmakers as well as societal 

pressures, cultural norms, fatigue, 

inherent biases, and other limitations 

decisionmakers may face. These 

factors also impact the quality of the 

policies, procedures, programs, actions, 

services, or operational performance 

that result from the decisionmakers’ 

conclusions. These limitations and 

biases become more pronounced when 

systems grow in complexity, decisions 

need to be made quickly, or there is 

either insufficient data or time to assess 

an incomprehensible amount of data.

DSTs can be valuable assets to an 

operator in a TMS. As the management 

and operation of the surface 

transportation system grows more 

complex and congested, DSTs are 

designed to optimize the safety and 

efficiency of the movement of people 

and goods.

Improving the responsiveness, 

timeliness, and effectiveness of 

real-time decisionmaking is a central 

focus for agencies that manage and 

operate TMSs. Decisionmaking 

relies on having a strong knowledge 

of the surface transportation system, 

understanding agency policies and 

operational procedures, and processing 

and assimilating a wide range of data 

and information.

Choosing DSTs

DSTs used within a TMS can be 

characterized by the type of interaction 

and the approach used in the tool. The 

type of interaction refers to the level of 

human interaction with the DST—how 

decisions are made (manually or 

automatically), and the degree to which 

the DST involves the decisionmaker 

(interactive or automated).

Online and offline DSTs can both 

support the management and operation 

of TMSs. Offline DSTs are typically 

used to support policy-related, 

short-term or long-term planning, 

programming, or scenario-based 

testing activities. Online DSTs 

include computer-based software 

tools incorporated into the real-time 

decisionmaking involved with actively 

managing and operating TMSs.

Incorporating DSTs into TMSs

One of the objectives of integrating 

a DST into a TMS is to automate or 

support the decisionmaking required 

by operators or a TMS. A DST 

integrated into a TMS requires an 

interface, procedures, and/or resources 

to assist an operator or analyst using 

the tool. The DST (and/or its software) 

may need to be integrated with the 

software subsystem and software 

programs, data subsystem, and 

computing hardware. This integration 

allows DSTs to use data (e.g., 

translated into appropriate formats), 

make the necessary decisions, and 

include these decisions in the interface 

used by operators to manage and 

operate a TMS.

DSTs 

DSTs benefit TMSs by providing 

agencies with the ability to make 

fast decisions, automate parts of the 

decisionmaking process, and provide 

a higher level of safety. The following 

show the major benefits of using DSTs:

• Ensure consideration of alternatives.

• Minimize the influence of biases 

(e.g., confirmation bias) on decisions.

• Encourage quantitative assessment 

of options.

Resources

1. Virginia Department of 

Transportation, 2018. “RTS _3767 

Traffic Operations Center, Chester, 

VA. CRO” (web page). https://

www.flickr.com/photos/

vadot/44536652080, last accessed 

February 1, 2023. This website shows 

a photo of the Virginia Department of 

Transportation’s Traffic Operations 

Center in Chester.

2. Miller, K., J. Horner, J. Adler, 

and R. Sanchez. 2021. Decision 

Support for Traffic Management 

System–Current Practices. 

Report No. FHWA-HRT-21-108. 

Washington, DC: Federal Highway 

Administration. https://www.fhwa.

dot.gov/publications/research/

operations/21108/21108.pdf, last 

accessed January 20, 2023. This 

report provides an overview of DST 

practices and technologies. This 

report highlights the key issues and 

current practices agencies have been 

using when selecting and integrating 

DSTs into a TMS.

3. Miller, K., J. Horner, J. Adler, and 

R. Sanchez. Forthcoming. Decision 

Support for Traffic Management 

Systems Report. Washington, DC: 

Federal Highway Administration. 

This report provides an overview 

of technologies and processes when 

selecting and integrating a DST into 

a TMS. The report also highlights 

the stages of decisionmaking that 

agencies can use to evaluate, plan, 

design, develop, and deploy a DST.

http://safety.fhwa.dot.gov

Recommended citation: Federal 

Highway Administration, Decision 

Support for Traffic Management 

Systems (Washington, DC: 2023) 

https://doi.org/10.21949/1521988.

FHWA-HRT-23-044

HRSO-50/04-23(WEB)E