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Adapting Transportation Demand Management Evaluations for Tomorrow’s Needs


Background: Traffic congestion has returned to pre-recession levels, wasting 3 billion gallons of fuel, 7 billion hours and $160 billion in cost nationwide, according to the Texas A&M Transportation Institute (TTI) 2015 Urban Mobility Scorecard. TTI recommended a “balanced and diversified approach to reduce congestion” such as promoting and facilitating travel options. As congestion in US metropolitan areas continues to increase so does interest among state and local officials in the use of commute–oriented, and transit-promoting demand management strategies to take some of the burden off the regional road system or “buy time” until planned infrastructure and technology can ease the pain.

Description: Going forward, Transportation Demand Management (TDM) evaluations will need to adapt to accommodate new technologies, new sources and scales of data, alongside changing policies, trends and tools. For example, Mobility on Demand grants have created partnerships between transit agencies and shared use mobility such as bikesharing and transportation network companies (TNCs). New public and private sources of data on travel behavior are available from mobile devices, connected vehicles, TNCs, Regional Integrated Transportation Information System (RITIS), and crowd-sourced data. These sources can lead to insights on propensity to change both travel behavior and mode choice, for changing conditions to best meet the traveler’s needs. How is the TDM industry tapping into these new data sources? Research is needed to improve TDM evaluation efforts, take advantage of new data systems and address the needs of commuters, companies and communities.


Objectives, tasks and deliverables: 1. Objective: Research the most contemporary, implemented TDM strategies by their effectiveness.

Task: Identify and document recent (no earlier than 2008) efforts to evaluate the effectiveness of TDM strategies. Summarize what is common and what is unique about recent approaches to TDM evaluation along with sidebars to highlight unique and effective use of new data sources and methods.

  1. Objective: Research the methods that are replicable and their findings.

Task: Categorize and outline evaluations benefits, challenges and trade-offs of the data sources used to evaluate TDM strategies (e.g., smartphone, bluetooth, third-party data providers). Categorize and describe the methods used to successfully evaluate TDM strategies and summarize the findings of the evaluations. Identify strategies for which no reliable evaluation results are available to identify the limitations of new data sources. Summarize the research for each unique audience of TDM practitioners such as transit agencies, cities, community based organizations and others.

  1. Objective: Categorize references to help practitioners plan to incorporate new sources of data into their existing TDM strategies and/or prioritize which new TDM strategies to consider for implementation.

Task: Identify and describe tools used to test the potential benefits of TDM strategies. Provide a central resource for TDM practitioners that enables practitioners to estimate costs such as staffing, data purchases or subscriptions and app development.


This research will help with incorporating TDM into local, regional, state, and federal programs; and will help with integrating TDM into project development processes.

Related Research:

The focus of this research is on discovering what can be brought from isolated evaluations to broader uses by TDM practitioners. Transit Center’s Measures for Success: New Tools for Shaping Transportation Behavior (July 2017) provides a springboard for this research. It says “New technologies have the potential to change how travel behavior data is collected.” A few pages discuss the development of, and conditions for using passive tracking, that can log travelers’ transit trips, through smartphone apps and fitness devices in service of TDM evaluation (Jeffrey Chernick, RideAmigos, William Henderson, RideReport). On page three you can see a list of contributors that could be re-engaged around this research topic.

Other examples of TDM evaluations come from travel plans; door-to-door individualized marketing; and guidelines for TDM including evaluation (Marcus Enoch, Loughborough, UK, also London examples)

An example from Active Demand Management (ADM) comes from Metropia’s work to evaluate behavior change (Yi-Chang Chiu, Tucson, Houston, more) https://www.sciencedirect.com/science/article/pii/S2046043016300636 http://metropia.com/behavior-management


(see Objective section)

Sponsoring Committee:AEP60, Transportation Demand Management
Research Period:12 - 24 months
Research Priority:High
RNS Developer:William Loudon, Chair ABE50
Source Info:This research need was identified through a survey of TDM research needs conducted by the TRB Transportation Demand Management Committee (ABE50). The survey was distributed in 2012 to the committee members and friends and the topic emerged as one of the highest rated research needs. The topic was also the primary subject of a workshop at the 2014 TRB Annual Meeting and its importance as a research need was reaffirmed by the workshop participants.
Date Posted:05/28/2018
Date Modified:07/31/2018
Index Terms:Travel demand management, Traffic congestion, Data collection, Travel behavior, Mode choice,
Cosponsoring Committees: 
Operations and Traffic Management
Planning and Forecasting
Transportation (General)

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