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Federal Highway Administration Research and Technology
Coordinating, Developing, and Delivering Highway Transportation Innovations

This report is an archived publication and may contain dated technical, contact, and link information
Publication Number: FHWA-RD-98-166
Date: July 1999

Guidebook on Methods to Estimate Non-Motorized Travel: Supporting Documentation

2.3 Bicycle Sketch Plan Methods

Demand Estimation

Descriptive Criteria: What is It?


Box with an x insideBicycle Empty BoxPedestrian Box with an x insideFacility-Level Empty BoxArea-Level

Authors and Development Dates:

Goldsmith (1997)


Sketch plan methods can be defined as a series of "back-of-the-envelope" calculations to estimate the number of bicyclists using a facility or area. These methods generally rely on data that already exist or can be collected with relative ease (such as census and land use data), combined with behavioral assumptions derived from other studies. Sketch plan methods tend to vary widely in their specific approaches and in their level of sophistication.

Goldsmith (1997) developed and applied a sketch-plan method to estimate the impact of a new bicycle facility in the Seattle, WA, area on reducing motor vehicle VMT (vehicle miles of travel) and emissions.



1. Determine the location and boundaries of the travel shed (i.e., the area from which most trips on the facility are expected to originate).

2. Determine the population of census tracts within the travel shed.

3. Use census or survey data to determine the percentage of daily commuters within the travel shed.

4. Use census or survey data to determine the bicycle mode split for each census tract within the travel shed.

5. Estimate the number of potential bicycle commuters using the rate of current bicycle commuting in the travel shed as a comparison. For example, if the travel shed has a higher bicycle mode split than the census, then the potential bicycle commuter rate also should be higher. Also could use the proportion of population under 45 years relative to the city average to estimate the potential riding population. Multiply the rate by the total number of commuters in the travel shed and then subtract the number of current bicycle commuters.

6. Determine the expected number of new bicycle trips by assuming that a certain percentage of the population will divert trips from other modes to bicycling. For example, the Seattle survey showed that 26 percent of the potential bicycle commuting population would become bicycle commuters.

7. Determine the proportion of these trips that came from single-occupancy vehicle (SOV) trips. For example, the Seattle survey showed that one in two would be diverted from SOV trips.

8. Determine trip lengths using the city-wide average or one calculated from central locations within the census tracts to main trip generators.

9. Calculate the estimated number of VMT eliminated and emissions prevented using emissions assumptions as shown below in the "Assumptions" entry.

Calibration/Validation Approach:

Goldsmith: The technique should be tested in other settings to ensure its transferability. Furthermore, before and after bicycle counts could help to better improve the accuracy of this type of estimation technique.

Inputs/Data Needs:

Goldsmith: The VMT/emissions model requires the following data items:

  • Geographic area that is affected by a bicycle facility, which also is known as the "travel shed";
  • Population and journey-to-work census data for the travel shed;
  • Current bicycle patterns within the travel shed, especially origin and destination information as well as key bicycle routes; and
  • Emission factors per trip and VMT for the purpose of calculating emission reductions.

Potential Data Sources:

Goldsmith: Not applicable.

Computational Requirements:

Goldsmith: Uses spreadsheets.

User Skill/Knowledge:

Goldsmith: Users should be familiar with the bicycle-related data that are available in the respective area.


Goldsmith: The following assumptions were made for each information need:

  • Travel shed identification. A 0.8-km buffer is the standard approach used to create a corridor-specific travel shed. Other criteria also should be considered, such as the proximity of alternative bicycle routes and physical barriers like mountains or highways. For example, the proposed Pine Street facility has a larger travel shed to the north because very few bicycle facilities are located in this area whereas the travel shed in the south is small since an alternative facility is in close proximity.

  • The proportion of new bicycle trips. To estimate commuter bicycle trips, first multiply the percentage of residents who commute on a daily basis (60 percent in Seattle) by the population of the travel shed. With the commuting population number, multiply it by the bicycle commute rate. This calculation gives existing estimated bicycle commute trips. An estimate for potential bicycle commuters is determined through survey data that reveals that percentage of residents who at one point bicycle commuted. Subtract this percentage from the current bicycle commute rate to obtain the percent of potential new bicycle commuters (Seattle used 8 percent). This number is then multiplied by the number of commuters in the travel shed and then by the number of commuters who said that they would switch to bicycling if safer facilities were provided (26 percent in Seattle). The equation is as follows:

    # new bicycle commuters = # CBD (central business district) commuters * percent potential bicycle commuters * percent ride on safe facilities

  • Non-work trip estimates: Since data are scarce concerning utilitarian non-work trips, the method relies on national surveys that show these trips as 50 to 100 percent more frequent than work trips. In Seattle, household travel survey data show that there are about 70 percent more utilitarian non-work trips than work trips.

  • The proportion of these trips that would have been motorized vehicular trips (as opposed to transit diversions). The estimate for the substitution rate is based on the area's rate of single-occupancy vehicle (SOV) travel. Seattle's proportion of SOV commutes is 60 percent, so Seattle conservatively chose a 50 percent substitution rate meaning that one out of every two bicycle commute trips replaces an SOV trip. For utilitarian non-work trips, only one of three trips were assumed to be diverted from SOV travel, since these trips tend to be much shorter and could be accomplished by non-automobile modes.

  • The average length of these SOV diverted trips. Commuting distances are estimated using census journey-to-work data. Minutes were converted into miles using an assumption that the average bicyclist travels at about 16 km/h or 1.6 km every 6 minutes. The average commute length is between 3.93 and 5.22 km based on low and high estimates. For utilitarian non-work trip distances, the commuting distance was divided in half. For Seattle, the average one-way non-work bicycle trip distance was estimated at 1.43 km, or one-half the average of 3.93 and 5.22.

Facility Design Factors:

Goldsmith: This method does not consider the impact of facility design factors on bicycle travel demand.

Output Types:

Goldsmith: The output consists of new bicycle commute and non-work utilitarian trips per day, and their impact on reducing SOV trips, VMT, and emissions. The following table illustrates the estimated reductions in SOV trips and VMT.

  New Commute and Utilitarian Bicycle Trips Due to Pine Street, Seattle Bicycle Lanes: Projected Totals
  Average Round Trip Length Projected New Bicycle Trips SOV Trips Eliminated VMT Avoided
Daily Commute Trips = (a) 3.56 144 72 244
Daily Non-work Trips = (b) 1.78 381 127 217
Total Daily Reductions =
(a) + (b)
  525 199 461
Total Annual Reductions =
250 * (a) + 365 * (b)
  175,065 64,355 140,205


Real-World Examples:

Goldsmith: Proposed bicycle lanes on Pine Street in Seattle, Washington, were used as the case study for the method. Examples taken from this case study are shown above in the input, output and assumptions entries. The author would like to test this method on other proposed facilities to ensure its transferability.


Stuart Goldsmith, City of Seattle, Engineering Department, Seattle, WA.


Goldsmith, Stuart, Draft: Estimating the Effect of Bicycle Facilities on VMT and Emissions, Seattle Engineering Department, 1997.


photo of a roadway with a designated bicycle path
Figure 2.3 A bicycle facility is likely to divert some trips from other modes to bicycling.

Evaluative Criteria: How Does It Work?


Goldsmith: The author believes that the method provides reasonable estimates of the impact that a new facility would have based on limited data such as census and travel survey data. The performance of the model in other situations has not been tested; local conditions vary considerably and bicycle-related data may be scarce in most jurisdictions. Furthermore, a number of assumptions are made in estimating the travel shed, current number of bicycle trips, and percentage of people who would choose to bicycle as a result of the new facility.

Use of Existing Resources:

Goldsmith: The method uses readily available data such as the census and local household travel survey data. The method also can use local preference surveys regarding travel behavior, although such survey data do not always exist.

Travel Demand Model Integration:

Goldsmith: The method is not designed for integration with regional travel models.

Applicability to Diverse Conditions:

Goldsmith: The inputs vary depending on the locality. For example, the travel shed is determined on a case-by-case basis. Once the travel shed is selected, the demographics and bicycle trip information then can be assessed.

Usage in Decision-Making:

Goldsmith: The method could be used to determine the VMT and emission reductions that could occur from specific proposed bicycle-related projects. This information is needed for air quality-related funding sources such as the Congestion Mitigation and Air Quality (CMAQ) program.

Ability to Incorporate Changes:

Goldsmith: Changes to the inputs can be easily incorporated into the estimation technique.


Goldsmith: The technique uses transportation data such as census data, and other existing data from transportation surveys.



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