WHY IS DATA NEEDED FOR PLANNING, OPERATING, AND 

EVALUATING ATDM?

Data needs increase as an agency evolves toward more active management. A 

successful ATDM initiative requires a variety of quality data in each stage:

 Planning for ATDM needs data to identify the areas in the region that would most

benefit from ATDM approaches.

 Operating ATDM systems generally requires more reliable and sophisticated realtime data than typical ITS deployments.

 Evaluating ATDM requires more data than simple traffic counts to show benefits in 

improved mobility and safety and the effectiveness of deployed strategies.

This brief will inform practitioners about data considerations for ATDM initiatives at 

each stage of deployment: Planning Data Needs, Data for Day-to-Day Operations, 

and Data Needs for Monitoring and Evaluation.

PLANNING DATA NEEDS

Data needs for planning are driven by monitoring needs to define the extent and 

duration of congestion and system performance, and modeling needs to estimate the 

performance of potential ATDM strategies on the transportation network. Model 

outputs generally include an estimated benefit/cost ratio and can help establish 

performance targets.

Goals and Objectives. Transportation agencies must consider ATDM solutions 

within the context of regional goals, which may relate to safety, mobility, reliability, the 

environment and climate change, and/or livability and accessibility. These goals can 

be translated to objectives with performance measures and targets for an ATDM 

deployment that will drive data needs. Consider the “SMART” objectives framework—

specific, measurable, attainable, realistic, and time-bound—to determine and 

periodically assess performance measure targets.

During the planning phase, agency coordination to facilitate data sharing may aid in 

determining and prioritizing corridors and areas where specific ATDM approaches will 

be most effective by providing a more comprehensive picture of overall system 

performance. Inter-agency coordination may also facilitate a common understanding 

and agreement of problem areas, needed improvements, and the identification and 

prioritization of performance measures. 

Based on regional needs and goals, a preliminary list of initiatives should be identified that would receive the greatest 

benefit. During a regional assessment, agencies should keep the following key questions in mind, which will drive ATDM 

data needs:

 What specific approach or combination of approaches would work best?

 What multimodal networks and facilities would be best suited for ATDM? 

 What would be the range of expected benefits? 

 What would be the expected costs (capital and ongoing)? 

What is Active 

Transportation and Demand 

Management (ATDM)?

ATDM is the dynamic 

management, control, and 

influence of travel demand, traffic 

demand, and traffic flow of 

transportation facilities. Through 

the use of available tools and 

assets, traffic flow is managed and 

traveler behavior is influenced in 

real-time to achieve operational 

objectives, such as preventing or 

delaying breakdown conditions, 

improving safety, promoting 

sustainable travel modes, reducing 

emissions, or maximizing system 

efficiency. Under an ATDM 

approach the transportation 

system is continuously monitored. 

Using archived data and 

or/predictive methods, actions are 

performed in real-time to achieve 

or maintain system performance.

Active 

Management
Performance Measures for Planning. Agencies should leverage all 

available, relevant archived and forecasted data during the planning phase to 

identify potential deployment areas, and also for day-to-day operations.

Similarly, agencies should cast a wide net to use comprehensive data 

sources for system evaluation to measure the effectiveness of ATDM 

approaches. Agencies should consider data from sources that may 

traditionally be overlooked, including data from other agencies. Relevant data

are available from a variety of sources, such as:

 Travel Demand Models. These models, developed by regional metropolitan planning organizations (MPOs) provide 

outputs identifying and monitoring the severity of current and future bottlenecks, areas of congestion, and air quality 

problem areas.

 Transportation Management Center (TMC) Data. Local and state agency TMCs typically collect data from traffic 

detectors, road weather information systems (RWIS), and maintain incident data.

 Transit Ridership Records. Transit agencies often maintain records of ridership that can identify available capacity 

and transit travel times; on-time performance records may provide a measure of transit reliability, as well as for the 

larger arterial network.

 Crash Records. Law enforcement agencies maintain detailed crash records that may be used to identify unsafe 

intersections and segments, and examine the specific causal factors of those crashes to confirm that an ATDM 

initiative would be effective.

 Third-party mobility data. Historical third-party data (i.e., probe or private-sector vendor data) on traffic congestion 

may be available to identify trends, monitor progress toward performance targets, and help evaluate effectiveness of 

the ATDM solution.

 Citation data. Citations issued by law enforcement for parking violations, including double parking, may highlight areas 

where active parking management (APM) would be beneficial. Similarly, citations issued for speeding or other minor 

offenses, as well as qualitative information from law enforcement officers may be useful in identifying areas that would 

benefit from ATDM. Citation data could also be reviewed to determine if there is an underlying need for better public 

awareness of new operations management strategies such as managed lanes/pricing, variable speed limits, part time 

shoulder lanes, or overhead lane control signals. This could influence a need for a public outreach campaign.

The key is for an agency to obtain the right data to measure performance. An agency can leverage these available data 

sources and resources to quantify the potential and actual benefits of ATDM deployments. If available data are not 

sufficient to measure performance, the 

agency should not simply compromise, but 

rather should collect or purchase data to 

provide the required measures. Utilizing 

these resources, ATDM solutions can be 

prioritized by evaluating regional conditions 

that support ATDM deployments and 

producing a benefit-cost ratio by ATDM 

initiative and area. Additional considerations 

should be made for the facility characteristics, 

existing ITS infrastructure, and institutional 

issues to ascertain the appropriateness of 

various ATDM solutions.

Source: MnDOT

An agency must obtain the right data 

to measure performance. If available 

data are not sufficient, the agency 

should not simply compromise, but 

should collect or purchase data to 

provide the required measures.
DATA NEEDS FOR DAY-TO-DAY OPERATIONS

Data identified during the planning phases may be useful 

for day-to-day operations. However, operations may require 

a different, higher quality dataset than was needed for 

planning. Operations data needs to be current, and usually

real-time, in order to allow for more active management. By 

comparison, planning data can have a higher latency or

can be collected over a shorter time period. Archived data 

can also be used to inform the development of algorithms 

that determine an appropriate response for a given current 

condition to achieve the desired result. 

ATDM initiatives may require the integration of and 

additional data from arterials, freeways, and/or transit 

operations systems in an entire corridor or region for 

effective, reliable, and proactive management. To be more 

proactive, additional real-time data feeds with reduced 

latencies, increased accuracy, and better geographic 

coverage are needed. Regions should leverage interagency data coordination and warehousing, when 

available, in order to receive and provide seamless, realtime traveler information as well as utilize that 

comprehensive data set to better manage the dynamic 

transportation network and conduct evaluations. This 

increased data may also require better data fusion, data 

processing and communications systems in the field and 

TMC that link data sources to calculate system 

performance measures in real time. Proactive management 

requires increased real-time situational awareness and 

therefore increased data processing capabilities, such as 

data mining tools, data fusion systems, prediction systems, and decision support systems to analyze and proactively 

respond to real-time conditions, due to the increased complexity associated with real-time data capture and response 

beyond traditional ITS. Consider the following ATDM operations data examples:

Active Traffic Management (ATM)

Dynamic signage with variable speed limits and lane control guidance requires detailed volume, density, occupancy,

travel time, and/or speed data for very short-distance segments for incident detection and as inputs about incident location 

and lane closures for an algorithm to generate automated responses. This data may also drive the system requirements 

during the planning stages. Although manual input may be required, sufficiently robust data and processing systems may 

be able to automatically detect incidents and/or changes in vehicle speeds.

Active Demand Management (ADM)

Automated variable pricing algorithms require real-time data on volume, vehicle occupancy, density, and speed, possibly 

in conjunction with historical data, to calculate appropriate prices at any given time for the current demand, and potentially 

activate incentive programs during peak periods of congestion. For example, the number of free high-occupancy vehicles 

(HOVs) that will not be impacted by pricing changes needs to be known to calculate the appropriate price for tolled 

vehicles in order to sufficiently manage volumes to maintain speeds above the designated threshold.

Active Parking Management (APM)

Dynamic parking pricing requires real-time parking occupancy data, in conjunction with historic parking demand data, for 

an automated algorithm to calculate appropriate parking prices at any given time for the current demand to maintain a 

specific threshold of available parking while reducing congestion related to motorists looking for parking.

Even agencies that are “data rich” will likely need to 

deploy new infrastructure to collect additional data to 

fill in gaps. The Washington State DOT deployed ATM 

signage every half-mile on three corridors and found 

that variable speed limits required additional detectors 

for geographic coverage from one-mile spacing to onequarter mile spacing, and improved resolution from 

five-minute to 20-second aggregated data.

Source: WSDOT
Higher quality data can enable a higher quality, 

more dynamic response. It reduces the risk and 

uncertainty of both the current and future 

conditions on the transportation network. Higher 

quality data enables agencies to more closely 

monitor the result of the public’s response to an 

ATDM solution (or group of solutions) and then 

modify the deployment if the initial response did 

not have the desired impact.

Deploying ATDM concepts often requires a highlevel output that may involve different amounts 

and types of data. ATDM may mean that an 

agency must upgrade displayed outputs from a 

color-coded traffic speed map to precise speeds 

on short segments for operator verification, as 

well as inputs for an automated algorithm, when

variable speed limits or managed lane pricing

systems are deployed. Even agencies that are 

already data rich may need to deploy new 

infrastructure to collect additional data to fill in 

gaps. The scale of this additional investment to 

collect necessary data will depend on the 

complexity of the ATDM deployment, as well as the availability of current geographic coverage and data resolution. While 

deploying additional sensors and detectors may be expensive, doing so is critical for successful operations and is likely to 

result in long-term cost savings.

Maintenance as it pertains to the resilience of the data is also an important consideration. A certain level of redundancy or 

planning for outages is important for continuing operations. For example, both SFpark and Los Angeles Express ParkTM

experienced hardware issues with the parking sensors within two years of deploying Active Parking Management. Los 

Angeles Express ParkTM had a maintenance contract that included replacement of faulty sensors. Conversely, SFpark 

chose to remove the sensors from the agency’s decisionmaking and instead decided to adapt a variable parking pricing 

algorithm based on payment data. This approach was developed using the rich dataset of payment and occupancy data 

from the time that the sensors were active, and was found to be reasonably accurate.

DATA NEEDS FOR MONITORING AND EVALUATION

As discussed previously, an agency’s identified ATDM needs should be associated with SMART objectives that have a 

corresponding means of measurement. The data associated with these objectives are critical for tracking ATDM 

deployment progress by assessing day-to-day performance targets, as well as driving management decisions for future 

expansion and deployment within a region. This data is also key in providing performance measures to inform the public 

about how the system is performing over time. While the concept of linking ATDM solutions to existing operations 

objectives is simple, limitations in the data needed for monitoring, evaluating, and enhancing performance and difficulty in 

agreeing upon the appropriate target or timeframe for achievement may hinder agency efforts. Developing ATDM 

operations objectives requires data on baseline conditions and often requires forecasts of future conditions. 

ATDM operations objectives are inextricably tied to performance measures. Performance measures should be developed 

based on the individual needs and resources of each agency. Performance measurement is an iterative process, as 

operations objectives may be refined once performance measures are developed and baseline data is collected. Some 

performance measures may be project-specific and determined based on project objectives. Reported performance 

measures may initially be limited by available data. However, ATDM deployment evaluations often have an advantage 

over traditional ITS or operations analyses in that the additional and higher-resolution data required for ATDM operations

makes for a more comprehensive evaluation. That said, it is important that agencies ensure these data are archived for 

evaluation. Agencies should also make the effort to collect data, which is typically identified in the ATDM planning stage, 

The Urban Partnership Agreement (UPA) 

and Congestion Reduction Demonstration 

(CRD) programs implemented congestion 

pricing for parking and managed lanes, as 

well as transportation demand 

management and advanced technology 

solutions in six U.S. cities. A 

comprehensive before-after evaluation 

effort examined numerous performance 

measures, such as average parking 

occupancy and mean parking search time 

for APM; travel time, travel time reliability, 

and throughput measures for mobility, 

comparing managed lanes performance 

with the general purpose lanes; change in 

number and severity of crashes for safety. 

Exogenous factors considered included 

fuel price, unemployment rate, and road 

work. For more information on the 

UPA/CRD programs, see: 

http://ops.fhwa.dot.gov/congestionpricing/

urb_partner_agree.htm.

Source: SFMTA
FHWA-HOP-16-003

May 2016

to effectively assess outcomes against the regional goals and objectives. Generally, performance measures for ATDM will 

employ similar data and techniques that are used for analyzing traditional ITS and operations. As more data becomes 

available, performance trends should be calculated to provide a comprehensive picture of system and ATDM performance 

over time. The following includes a list of potential measures which may be used to evaluate system performance and 

ATDM deployments:

 Average Travel Time  Travel Delay  Bike/Ped Accessibility

 Travel Time Reliability  Non-Recurring Delay  Transit Use

 Travel Time Buffer Index  Average System Speed  Transit vs Auto Travel Time

 Travel Time xth Percentile  Incident Info. Dissemination  Transit On-Time Performance

 Planning Time Index  Incident Severity  Mode Share

 Vehicle Miles Traveled  Incident Clearance Time  Parking Occupancy

 Congestion Level  Road Weather Clearance Time  Number of Citations

 Traffic Density  Queue Length  Customer Satisfaction

 Traffic Volume  Occupancy  Reduced Trips

Supplementary data should also be collected in order to understand and separate the impacts of exogenous factors, 

particularly since the impacts of an ATDM deployment may be much smaller by comparison. For instance, the price of 

fuel, unemployment rates, other highway improvements, and work zones can all affect traveler behavior and cause 

significant mobility and safety impacts that result in anomalies in baseline and post-deployment data, and potentially 

diminish the impact of the ATDM initiative. Carefully selected data and performance measures are key to a successful 

evaluation since not all performance measures are always easily calculated due to exogenous factors, data availability, or 

data quality issues. This is especially important for an evaluation of ATDM deployments, where the measured 

improvement may be more subtle.

For additional information on performance measures, refer to FHWA’s “Guide for Highway Capacity and Operations 

Analysis of ATDM Strategies: Analysis of Operational Strategies under Varying Demand and Capacity Conditions” 

(FHWA-HOP-13-042) on FHWA’s Office of Operations website: 

http://www.ops.fhwa.dot.gov/publications/fhwahop13042/fhwahop13042.pdf.

In summary, the tools and approaches of active management can help agencies take advantage of the big data 

environment to transform transportation operations, resulting in the realization of ATDM goals: increased system 

productivity, efficiency, and safety.

HOW CAN I FIND OUT MORE?

The ATDM Program is intended to support agencies and regions 

considering moving towards an active management approach.

Through workshops, tools, guidance documents, resources, and peer 

exchanges, the program can assist with the technical support needed 

to implement ATDM. Importantly, ATDM is not an exclusive program 

restricted to specific agencies. Every agency that is considering 

moving towards active and dynamic capabilities can benefit from the 

ATDM program’s efforts.

The FHWA’s ATDM Website has additional information on ATDM 

concepts and tactics, current research, future events, and available 

resources, including resources to help agencies prioritize potential ATDM deployments:

http://ops.fhwa.dot.gov/atdm/about/program.htm.

ATDM Informational Briefs

This informational brief is one in a suite of 

outreach materials. Other available briefs 

include:

 ATDM Program Overview 

 ATDM Program Brief: ATM 

 ATDM Program Brief: ADM 

 ATDM Program Brief: APM 

 ATDM Program Brief: ATDM and Work Zones

 International Influence on ATDM in the US 

For More Information:

Jim Hunt James Colyar Greg Jones

Jim.Hunt@dot.gov James.Colyar@dot.gov GregM.Jones@dot.gov

(717) 221-4422 (360) 753-9408 (404) 562-3906