Measuring TDM Activities {{ currentPage ? currentPage.title : "" }}

Many measures for TDM activities exist, and no two measures are equal. Some measures are more meaningful to a specific audience than others. While Federal funders may be primarily concerned with VMTR (Vehicle Miles Traveled Reduced), local funders may be more moved by participant testimonials. VMTR is an example of a quantitative measure. Testimonials are an example of a qualitative measure. Measurements, by themselves, are neutral.

Quantitative measures can be expressed completely by numerical data. In TDM (Transportation Demand Management), the best quantitative measures are generally VMTR, TR (trip reduction), AVR (Average Vehicle Ridership), and, more recently, ROI (Return on Investment). These types of measures are typically required to be reported for grant funding. VMTR in the most common measure associated with TDM[1], and VMT (Vehicle Miles Traveled) is a Federal Highway Administration (FHWA) standard measurement, representing demand in the transportation system[2]. Measuring VMTR is beneficial in comparing the cost of a TDM program to a more expensive intervention, as VMT provides a standard for comparison. VMTR is also useful in cost-benefit analysis for this reason.

Types of High-quality Quantitative Measures:

  • VMTR – this is generally the most impactful measure, as it can be extrapolated to determine program air-emissions reductions, return on investment, and more. VMTR can measure TDM impacts within a program as a whole, on a project-level, or within a timeframe, depending upon how it is defined.

  • AVR – a site-specific measure of the ratio of the number of people to vehicles arriving at a site, for instance, a work site. A higher ratio is better (for instance a “2” means that there are two people for every vehicle meaning half the population is not using a vehicle to get to work). As an example of AVR, a two-person carpool would count as one vehicle for two people, or half a vehicle for each person.

  • SOV Rate – a site-specific measure of the ratio of the number of people arriving by SOV mode to all people traveling to a site, for instance, a work site. SOV Rate is normally expressed as a percentage. As an example, a two-person carpool would not count as an SOV arrival and would not contribute to the SOV rate.

  • TR – an important measure in situations where mileage is more difficult to ascertain, or as a precursor to VMTR. Applying regional average mileage to TR can produce a reasonably acceptable VMTR value.

  • ROI – a value especially important to funders and policy makers, ROI has traditionally been difficult to generate due to the scale of TDM projects and the number of external factors influencing the success of a TDM project. However, recently, Mobility Lab has created the TDM ROI Calculator, and expanded it through FHWA funding. The TDM ROI Calculator[1] receives quasi-quantitative inputs and programmatic inputs, as well as location inputs, in order to extrapolate acceptable VMTR and ROI figures. The ROI Calculator “[s]erves primarily to evaluate existing TDM programs by converting program activities and costs into calculations of mode shifts of various types, vehicle trips reduced, VMT reductions, emissions reductions, fuel savings, congestion reduction, road construction deferred, vehicle safety (accidents avoided), noise pollution reduction, and calculation of ROI or Benefit-Cost ratio.”[1]

  • LOS - Level of Service (LOS) traditionally measures “the speed, convenience, comfort and security of transportation facilities and services as experienced by users,”[2] and is measured on a six-degree scale between “A” and “F.” LOS is a means of proxy to evaluate qualitative measures such as driver discomfort, frustration, and lost travel time.[3] Level of Service has traditionally focused on delay to automobile traffic. In 2010 the Highway Capacity Manual included multimodal LOS which provided Level of Service definitions for Bicycling, Transit, and Walking. There are proponents, as well as critics, of focusing on delay as a measure.[4] Level of Service is one measure of vehicle delay and is similar to “Traffic Delay.”[5]

Additionally, “mode shift” is a kind of TDM measure and is based on travel change from SOV to non-SOV modes (and is often attributed to the specific mode that is switched to, for instance, the number of bike trips replacing former SOV trips). When attributed to specific modes, mode shift is often the basis of more accurate VMTR calculation, as the calculation of VMTR can be more precise if derived from mode shift, rather than an average for all non-SOV modes.

Qualitative measures are primarily descriptive and observational. In TDM, observations about program success, anecdotes, and testimonials are all examples of qualitative measures. Additionally, quasi-quantitative measures can fall into the qualitative category; for instance, the number of people stopping at a table, the amount of engagement on social media, the participants coming to a trip-origin event, etc. While these “counts” are technically quantitative, without an extrapolation metric derived from either a statistically valid study or survey, or an industry-accepted standard, they are not easily compared with VMTR, TR, or ROI data.[1] However, a quasi-quantitative measure can be tied directly to a performance measure and can indicate success or failure if framed in that way (for instance, number of employers contacted through a program).

Note: Measuring is different than forecasting. Forecasting relies on predicting a program’s effectiveness; measuring focuses on collecting data for a program that is already in progress or has been concluded. While simple programs can use very basic equations to forecast impacts (basically assuming future variables in a measurement equation, for instance, a VMTR equation), more complex programs will rely on modeling to forecast program effects. The four TDM forecasting models in use in the United States are the EPA COMMUTER Model, the TDM Effectiveness Evaluation Model (TEEM), the Worksite Trip Reduction Model (WTRM), and the Trip Reduction Impacts of Mobility Management Strategies (TRIMMS).[2] Modeling takes training and time to conduct and should be used only by practitioners who understand the modeling process, its inputs, and its outputs.

[1] Some programs take the time to study participants and connect quasi-quantitative data to an extrapolation metric, resulting in VMTR or another quantitative measure. For instance, an organization may periodically conduct studies of the people who stop by their table at events, collecting “before” data and contact information at their table, and following up with participants to collect “after” data later, to determine how the stop at their table changed the person’s behavior. With this information, they can apply an extrapolation metric to all their tabling outreach events that follow the same parameters.

[2] These models are described in greater detail in the FHWA’s “Integrating Demand Management into the Transportation Planning Process: A Desk Reference,” Pages 148-150

[1] “USING THE TRANSPORTATION COST-SAVINGS CALCULATORS: A Guide to The TDM ROI Calculator and TRIMMS 4.0” Page 1. (See Additional Resources Section).

[2] Victoria Transport Policy Institute (VTPI) “Multi-Modal Level-of-Service Indicators” (See Additional Resources)

[3] Highway Capacity Manual (See Additional Resources)

[4] Level of Service focuses on “delay,” a single facet of a complex system. Proponents of “delay” or “congestion” as a quantitative measure articulate that delay is highly measurable with a consistent baseline, and relatable to monetary figures. However, critics raise concerns that “delay” is measured against a free-flowing baseline situation that “could never exist in reality” (VTPI’s “Congestion Costing Critique”), and suggest a focus on “capacity-maximizing or efficiency-optimizing” (VTPI’s “Congestion Costing Critique”) efforts. (See Additional Resources).

[5] The TRB defines Traffic Delay as “The additional travel time experienced by a driver, passenger or pedestrian due to circumstances that impede the desirable movement of traffic. It is measured as the time difference between actual travel time and free-flow travel time.”

[1] Mobility Lab’s TDM ROI Calculator (See Additional Resources)

[1] FHWA’s “Integrating Demand Management into the Transportation Planning Process: A Desk Reference,” Page 34 (See Additional Resources Section).

[2] The “R” in VMTR refers to reduced VMT, and therefore reduced demand, or “managed” demand – a quintessential element of TDM.

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