Guidebook on Methods to Estimate NonMotorized Travel:

Overview of Methods

PUBLICATION NO. FHWA-RD-98-165

JULY 1999

U.S. Department of Transportation

Federal Highway Administration

Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296
U.S. Dept. of Transportation's Federal Highway Administration - Research, Development, & Technology

Guidebook on Methods to Estimate Non-Motorized Travel: Overview of Methods (July 1999)

Table of Contents

Title Page

Foreword

Technical Documentation Page

1.0 Introduction

1.1 Purpose of Guidebook

1.2 The Importance of Forecasting Demand

1.3 How to Use This Guidebook

2.0 Introduction to Non-Motorized Travel Forecasting

2.1 Dimensions of Travel Behavior

2.2 Perspectives on Modeling Travel Behavior

2.3 The Four-Step Urban Transportation Planning Process

2.4 Factors Specifically Influencing Bicycling and Walking

2.5 Differences in Forecasting Bicycle vs. Pedestrian Travel

3.0 Guide to Available Methods

3.1 Overview of Methods

Demand Estimation Comparison Studies

Aggregate Behavior Studies

Sketch Plan Methods

Discrete Choice Models

Regional Travel Models

Relative Demand Potential Market Analysis

Facility Demand Potential

Supply Quality Analysis Bicycle and Pedestrian Compatibility Measures

Environment Factors

Supporting Tools and Techniques Geographic Information Systems

Preference Surveys

3.2 Key Characteristics and Uses of Each Method

4.0 Conclusions and Future Needs

4.1 Conclusions
4.2 Future Needs Development of a Manual for Bicycle and Pedestrian Sketch-Planning

Research on Factors Influencing Non-Motorized Travel Behavior

Integration of Bicycle and Pedestrian Considerations into Mainstream

Transportation Models and Planning
U.S. Dept. of Transportation's Federal Highway Administration - Research, Development, & Technology

Guidebook on Methods to Estimate Non-Motorized Travel: Overview of Methods (July 1999)

FOREWORD

This two-volume guidebook describes and compares the various methods and tools that can be

used to forecast non-motorized travel demand or that otherwise support the prioritization and

analyses of bicycle and pedestrian facilities. The guidebook is intended to be used by bicycle and

pedestrian planners, technical staff, researchers, advocates, and others who may wish to estimate

bicycle and pedestrian travel demand or to prioritize bicycle and pedestrian projects.

This first volume, Overview of Methods, provides a concise overview for each available method

and tool, including some typical applications, pros and cons, and a quick reference guide on ease

of use, data requirements, sensitivity to design factors and whether widely used. It discusses

general issues for consideration in forecasting non-motorized travel demand, such as the

dimensions of travel behavior and factors influencing bicycling and walking, and identifies

future needs in this arena. The other volume, Supporting Documentation, provides the details on

the methods as well as real world examples.

NOTICE

This document is disseminated under the sponsorship of the Department of Transportation in the

interest of information exchange. The United States Government assumes no liability for its

contents or use thereof. This report does not constitute a standard, specification, or regulation.

The United States Government does not endorse products or manufacturers. Trade and

manufacturers' names appear in this report only because they are considered essential to the

object of the document.
U.S. Dept. of Transportation's Federal Highway Administration - Research, Development, & Technology

Guidebook on Methods to Estimate Non-Motorized Travel: Overview of Methods (July 1999)

1.0 Introduction

1.1 Purpose of Guidebook

The need for improved conditions for bicyclists and pedestrians has received increasing attention

in recent years in transportation planning circles. Planners are recognizing a growing popular

interest in bicycling and walking for health and recreation, the desire to promote alternatives to

automobile travel for environmental reasons, and the need to provide safe and convenient travel

options for the entire population. At the same time, the question of how many people will

actually use new or improved bicycle and pedestrian facilities is gaining attention and

importance. Planners and policy makers need to be convinced that the benefits of improvements

are worth the costs. Furthermore, they want to know where to spend limited resources to get the

most "bang for the buck" as measured by benefits to users.

This guidebook was developed in response to the need to predict bicycle and pedestrian or "nonmotorized" travel. The guidebook is intended to provide a means of addressing the following

related questions:

• If we build a new bicycle or pedestrian facility, how many people will use it?

• If we improve an existing facility or network, how many additional people will choose to

walk or bicycle?

• What types and combinations of improvements will have the greatest impact on

increasing non-motorized travel?

• How will improvements to non-motorized travel conditions affect motor vehicle use?

The guidebook describes and compares the various methods that have been developed to predict

future levels of bicycle and pedestrian travel, i.e., "travel demand." The guidebook also discusses

other quantitative methods that support demand forecasting but do not actually predict future

demand. These include (1) analyses of the potential market for bicycling and walking; (2) "level

of service" measures and "environment factors" which describe the quality of the supply of

bicycle and pedestrian facilities; and (3) supporting tools and techniques such as Geographic

Information Systems (GIS) and preference
Figure 1.1 If We Build a New Bicycle or Pedestrian Facility,

How Many People Will Use It?

surveys. The guidebook is intended to be used by bicycle and pedestrian planners, technical staff,

researchers, advocates, and others who may wish to apply these methods to estimate bicycle and

pedestrian travel demand and/or to prioritize bicycle and pedestrian projects.

While all of these methods focus on non-motorized travel, some important distinctions in scope

can be identified. Some methods are directed specifically at either bicycle or pedestrian travel,

while others are generally relevant to both. Some methods focus on demand for a specific

facility, such as a bicycle lane or shared-use trail, while others focus on travel over an entire area,

such as a city or census tract. Finally, the methods differ in the extent to which they consider

trips made for recreational, as opposed to utilitarian, purposes(2).

The guidebook is based on an extensive international review of both published and unpublished

sources. Most of the methods were developed in the United States and Europe, but examples are

also included from Japan, Australia, and South America. While it is doubtless that some relevant

sources and methodologies have been overlooked, the guidebook should serve as a reasonably

complete review of methods currently available to the bicycle and pedestrian planner.

1.2 The Importance of Forecasting Demand

There are many compelling reasons both to apply existing methods of forecasting bicycle and

pedestrian travel and to advance the state-of-the-practice in this area. If properly done, demand

forecasting has a variety of uses including:

• Estimating the benefits of a proposed project, such as number of users served, reductions

in automobile emissions and energy consumption, or time and cost savings to travelers;

• Prioritizing projects based on the greatest benefit to existing users or on the greatest

payoff in attracting new bicyclists or walkers;

• Planning bicycle or pedestrian networks and identifying and correcting deficiencies in

existing networks, based on desired travel patterns and facility characteristics; and
• Planning for bicycle and pedestrian safety by developing exposure information for

crash/safety models. In the United States in particular, two recent developments

underscore the importance of quantifying demand:

• The 1994 U.S. Supreme Court Dolan vs. Tigard decision. This decision mandates that

local jurisdictions quantify proposed bicycle project benefits when the project involves

private land dedications under master plans.

• The 1998 passage of the Transportation Equity Act for the 21 st Century (TEA-21). TEA21 continues and expands provisions of its predecessor, the Intermodal Surface

Transportation Efficiency Act of 1991 (ISTEA), to improve facilities and safety for

bicycles and pedestrians. TEA-21 places an emphasis on quantifying the air quality and

congestion alleviation benefits of projects, including bicycle and pedestrian projects to

receive funding under the Congestion Mitigation and Air Quality (CMAQ) program(3).

TEA-21 also adds "bicycle transportation and pedestrian walkways" to the list of eligible

projects for National Highway System Funds and expands eligibility for funding under

other programs(4). Estimates of the benefits of bicycle and pedestrian projects will be

useful in competing for funding under these programs.

All of these reasons underscore the need to apply available demand forecasting methods and to

continually advance these methods. Forecasts of demand provide a much needed complement to

other considerations, such as improvements to safety and convenience for existing users, in

planning bicycle and pedestrian facilities.

Nevertheless, many people in the bicycle and pedestrian planning and advocacy communities are

skeptical of demand forecasting, and raise valid points about its limitations. Skeptics argue that

the factors influencing non-motorized travel are largely attitudinal and cannot be easily described

or quantified in models. They further believe that comprehensive efforts to improve facilities,

policies, and social attitudes toward bicycling and walking are required, and that such measures

would result in significant mode shifts that would not be predicted by existing models. Others

take the philosophical viewpoint that conditions for bicyclists and pedestrians should be

improved simply as a matter of fairness to existing users, regardless of whether new users would

be attracted. Still others are concerned that a focus on predicting demand will divert much

needed energy away from the actual implementation of bicycle and pedestrian improvements.

These arguments, although valid, should not detract from the usefulness of forecasting bicycle

and pedestrian travel demand. A simple "if you build it, they will come" attitude is not sufficient

given that resources for implementing projects are limited. Existing forecasting methods, even

given their limitations, can help allocate resources toward the most beneficial projects and can

help determine which improvements will attract the most new users. Furthermore, future

developments have the potential to greatly increase the accuracy and usefulness of these

methods. While qualitative assessment based on experience and judgment will continue to play a

key role in identifying projects with the greatest benefits, quantitative methods can become

increasingly useful in providing information for planning and decision making.

1.3 How to Use This Guidebook

This guidebook consists of two parts: Overview of Methods and Supporting Documentation.
Overview of Methods provides a concise overview of the available methods and of general issues

for consideration in forecasting demand for non-motorized travel. Supporting Documentation

provides substantially more detail on the methods described in the guidebook and identifies

sources and real-world applications for the methods.

The contents of Overview of Methods include:

• Section 2.0- An introduction to non-motorized travel demand forecasting, including ways

in which travel behavior can change, general approaches to travel demand forecasting,

factors specifically influencing bicycle and pedestrian travel, and differences in

forecasting bicycle vs. pedestrian travel.

• Section 3.0 - An introduction to 11 classes of methods, and a one-page overview of each

which includes a description, typical applications, advantages, and disadvantages.

Section 3.0 also contains a summary of key characteristics and uses of each method as

well as a guide to choosing an appropriate method for a specific purpose.

• Section 4.0 - A summary of this guidebook and a discussion of the limitations of existing

forecasting methods and future research needs for improving non-motorized demand

forecasting.

Supporting Documentation includes:

• Section 1.0 - A description of the research methodology and a categorization of the

methods according to their major purposes.

• Section 2.0 - An in-depth, structured description (e.g., purpose, structure, inputs/data

needs, assumptions) of each method along with evaluative criteria. Multiple variations on

some methods are included, as well as specific examples and real-world applications.

• Section 3.0 - An annotated bibliography of references on demand forecasting methods,

supporting tools and techniques, and factors influencing the choice to walk or bicycle.

• Section 4.0 - A list of individuals and organizations contacted in developing this

guidebook.

1Bicycling and walking are the most common forms of non-motorized travel in most countries and the term "nonmotorized" is used herein to refer collectively to bicycle and pedestrian travel. Nevertheless, the term "nonmotorized" could also refer to many other forms of travel such as in-line skating, skateboarding, or horseback riding.

The methods discussed in this document may be applicable to these other forms of non-motorized travel although

specific applications have not been identified.

2A significant weakness of existing methods is that none differentiate explicitly between utilitarian and recreational

travelers. The two travel markets have very different characteristics and needs, and a greater focus on these

distinctions would help improve the accuracy and usefulness of travel forecasting methods in the future.

3Title I, Sections 1110.

4Title I, Sections 1106 and 1202.
U.S. Dept. of Transportation's Federal Highway Administration - Research, Development, & Technology

Guidebook on Methods to Estimate Non-Motorized Travel: Overview of Methods (July 1999)

2.0 Introduction to Non-Motorized Travel

Forecasting

2.1 Dimensions of Travel Behavior

The objective of travel demand forecasting is to predict changes in travel behavior and

transportation conditions as a result of proposed transportation projects, policies, and future

changes in socioeconomic and land use patterns. For non-motorized forecasting in particular, the

objective is generally to predict the change in the number or characteristics of bicycle,

pedestrian, or vehicle-trips as a result of facility improvements or policy changes which are

designed to make bicycling or walking more attractive. In addition to affecting overall levels of

non-motorized travel, changes in non-motorized travel conditions may affect travel behavior in a

variety of ways:

• Trip making. A high-quality walking and bicycling environment is likely to increase

total person travel, while a poor quality environment may lead some people to choose not

to travel.

• Trip location. A high-quality pedestrian and bicycling environment may cause changes

in the choice of destinations, e.g., diverting travel from more distant automobileaccessible areas to closer-by pedestrian-oriented locations.

• Mode choice. Changes in the quality of the travel environment may spur changes not

only in the number of people who walk and bicycle, but also decrease the propensity to

use public transportation, rideshare, or to drive an automobile.

• Route choice. Changes in the quality of the travel environment may spur changes in the

use of various routes by pedestrians and bicyclists.

• Trip scheduling. The quality of the travel environment may vary by time-of-day (e.g.,

with changes in on-street parking regulations or non-peak-period traffic restrictions) and

may also affect trip scheduling of motorized travel. For example, bicyclists may choose

to make trips when there is less motor vehicle traffic.

• Land use. Changes in the travel environment may spur changes in land use over a period

of several years or more, with some locations becoming more or less desirable for certain

types of uses. For example, pedestrian-friendly urban environments may be more

attractive, thus increasing development in these areas.
• Distribution of effects. Changes in the pedestrian and bicycling environment are likely

to have widely varying effects on different segments of the population. For example,

some types of improvements will primarily benefit recreational users while others will

benefit those for whom bicycling or walking is the primary means of transportation.

Figure 2.1 A High-Quality Walking Environment May

Cause Changes in the Choice of Destinations

2.2 Perspectives on Modeling Travel Behavior

A variety of forecasting methods has been developed to predict changes in travel behavior.

Forecasting methods are generally founded on theoretical models and then verified by empirical

studies, which describe how people change their behavior in response to changes in the major

factors which influence this behavior.

Travel behavior, including non-motorized behavior, may be studied or modeled from two

perspectives:

The aggregate perspective. Aggregate studies look at travel from an areawide perspective. They

attempt to relate characteristics of an area (e.g., population, employment, or average income) to

travel characteristics of that area (e.g., average number of trips per household, or the number or

percent of trips made by foot or bicycle). In the context of non-motorized travel, these studies

may also look at characteristics of specific facilities (e.g., roadway and sidewalk width or type)

in conjunction with characteristics of the surrounding area (e.g., population density, or number of

students) to predict the number of people using the facility.

The disaggregate or individual perspective. Disaggregate studies look at travel decisions from

the perspective of the individual. The individual's personal characteristics (e.g., age, gender,

attitudes, beliefs) interact with the travel options available to them (e.g., time, cost, comfort of

competing modes). To predict overall demand, models of individual behavior are applied across
a population with known characteristics.

Each approach has its advantages and disadvantages. Aggregate-level methods tend to be

relatively easy to apply, with readily available data sources and computational methods, and can

be useful for sketch-planning purposes. Disaggregate-level methods are more complicated to

develop but can be much more effective at predicting behavior changes. This is because they

explain individual choices rather than making generalizations based on overall population

characteristics.

2.3 The Four-Step Urban Transportation Planning Process

Variations on both the aggregate and disaggregate approaches can be developed and applied as

stand-alone travel demand forecasting methods, appropriate for specific purposes. Alternatively,

a set of methods can be applied in conjunction with each other to create a larger modeling

framework. The four-step Urban Transportation Planning Process (UTPP) (Weiner, 1997), first

developed in the 1950s to forecast automobile travel and now applied in urban areas throughout

the world, is an example of such a framework. To predict how travel patterns will change as a

result of future changes in land use patterns and the transportation system, this framework

integrates models of various aspects of travel behavior (e.g., trip-making or mode choice) with

spatial information on land use patterns and the transportation network.

The UTPP is important to understand because it is widely used in transportation planning and

because of its potential for integrating bicycle and pedestrian with automobile and transit travel

forecasting. The basis for UTPP models is the division of the urban area into traffic analysis

zones (TAZs), which may correspond to census tracts, and the definition of a network of

transportation facilities connecting the zones (figure 2.1). The network is described by the time

and cost of travel, for each mode, between each pair of zones. Inputs include proposed future

transportation networks and forecast population and employment characteristics by zone. A fourstep process is then used to forecast travel:

1. Trip generation - Total trips generated by persons that start and end in each zone are

predicted, based on the population, employment, household characteristics, etc., of the zone;

2. Trip distribution - The trips are distributed among pairs of zones, usually based on a gravity

model which distributes trips in inverse proportion to the distance between zones;

3. Mode choice - The trips are allocated among the available travel modes, based on relative

characteristics (usually time and cost) of the modes; and

4. Network assignment - The trips are assigned to specific links (road segments) in the

transportation network, generally based on the shortest time path between two zones.

The different stages of the process may include both aggregate and disaggregate behavior

models. In addition, these models have sometimes been modified to incorporate additional travel

behavior factors, such as feedback from later steps to earlier steps (e.g., congestion influencing

trip generation and mode choice) or variations in travel by time of day.

The UTPP framework has primarily been applied to automobiles and transit but is increasingly

being modified to include bicycles and pedestrians. Non-motorized modes can be incorporated in
the models in various ways. For example, a bicycle or pedestrian network can be defined.

Bicycling and walking can be included as modes in the mode choice model. The advantages and

limitations of this framework for modeling non-motorized travel are discussed more fully under

the specific entries on "Regional Travel Models" in Section 3.0 and in the Supporting

Documentation.

2.4 Factors Specifically Influencing Bicycling and Walking

Standard travel demand modeling procedures generally predict total trip-making and mode

choice based on a limited number of variables, such as household characteristics and the time

and cost of competing modes. These factors, however, only partially explain the decision to

bicycle or walk. Development of non-motorized travel forecasting methods requires

consideration of a range of factors specific to non-motorized modes. From an individual

perspective, personal factors, environmental factors, and trip characteristics interact to determine

whether a trip is made by bicycle, foot, or other mode. The specific factors which are important

vary depending on whether the mode being discussed is bicycling or walking.

If behavior studies are performed from an aggregate-level perspective, factors must be identified

which proxy for the personal and environmental factors seen from the individual's perspective.

For example, median income of an area may represent household income, or average vehicle

travel speeds and parking costs in a city may serve as a proxy for the time and cost of travel by

automobile for a particular trip. Figure 2.2 presents a framework for how a general set of factors,

including facility design factors, interact to affect non-motorized travel levels, both overall and

for specific facilities (links) in a network. These factors are described in table 2.1.

Regardless of whether models are developed at the disaggregate or aggregate level, it is

important to remember that decision making ultimately occurs at the individual level and that a

forecasting procedure should approximate the individual decision-making process as closely as

possible.
Figure 2.2 Structure of Regional Travel Model

Figure 2.3 Relationship of Factors Influencing Non-Motorized Travel

Table 2.1 Description of Factors Influencing Non-Motorized Travel.

B Variable Description
ox

A.Link

Characteristics

Measurable characteristics of a link in a road or path network (e.g., traffic volume, lane width, or

pavement quality).

B. Link

"Friendliness"

The overall acceptability of a link as a bicycle or pedestrian route - a function of link

characteristics. Also varies by user characteristics (e.g., experienced vs. novice bicyclist).

C. Network

Characteristics

Characteristics of a network of links (e.g., connectivity) which determine its overall acceptability

or "friendliness" to the user.

D.Network

"Friendliness" A general measure of how acceptable the local road/path network is for bicycling or walking.

E. Supporting

Policies

Other programs, policies, facilities, etc., which affect the acceptability of bicycling or walking

(e.g., bicycle parking, showers/lockers, and educational programs).

F. Population

Characteristics

Characteristics of the local population which relate to likelihood of bicycling or walking (e.g.,

socioeconomic characteristics, or attitudes).

G.Climate/Weath

er

General propensity to walk or bicycle, as a function of climate/weather. This might be considered

a constant for a given area/region.

H.

Characteristics

of Other

Modes

Relative travel times and costs of bicycling or walking vs. other modes, as well as safety,

comfort, or other factors which influence choice of mode. Policy variables might include parking

pricing, transit service improvements, etc.

I. Land Use Density and distribution characteristics of population, employment, shopping, and other activities

which affect where people travel, how many trips are generated, trip length, etc.

J.

Total NonMotorized

Trip Making

Overall level of non-motorized trip making in an area as a result of the above factors.

K.Link-Level

Trips

Non-motorized trips on a specific facility or link as a function of local trip generation/distribution

characteristics and route choice based on link "friendliness."

Finally, it should be kept in mind that the factors shown in table 2.1 may influence an

individual's travel behavior decisions at a variety of stages, not just on a trip-by-trip basis. For

example, the individual must first decide to even consider bicycling or walking as a viable travel

option. Only when this is done does the question of whether to bicycle or walk for a particular

trip become relevant.

2.5 Differences in Forecasting Bicycle vs. Pedestrian Travel

Bicycle and pedestrian travel are collectively referred to throughout this guidebook as nonmotorized travel, and each class of forecasting methods discussed is generically applicable to

both. Nevertheless, significant differences exist between the two modes, both in terms of travel

characteristics and factors influencing the decision process. These differences are apparent in the

specific examples of the methods, most of which were developed for either bicycles or

pedestrians, as discussed in the supporting documentation of this guidebook. Some of the most

significant differences include:

Pedestrian trips are generally shorter than bicycle trips. This is important because
appropriate analysis methods may depend on the spatial scale of analysis. For example, an

analysis of pedestrian conditions may consider every block in a small area, while an analysis of

bicycle conditions may focus on through bicycle routes.

A large percentage of pedestrian trips are actually trips to access other modes, including the

automobile or transit. Bicycle trips, in contrast, are primarily stand-alone trips (although bicycle

access to transit is an important type of non-motorized travel). Therefore, local pedestrian travel

will largely result from automobile and transit trips rather than replacing these trips, and

modeling transit vs. auto mode choice will be relevant to predicting pedestrian travel.

Conversely, pedestrian access factors will be important in predicting transit vs. auto mode

choice, since the quality of the environment for walking may influence the decision to use

transit.

Perhaps most significantly, the decision to ride a bicycle involves a greater conceptual leap

than the decision to walk. Everyone is a pedestrian, but not everyone is a bicyclist. Insights

from the public health and social marketing fields suggest that the decision to even consider

riding a bicycle is a multi-staged process involving a variety of interacting personal, social, and

environmental factors. The choice to bicycle for a particular trip depends not only on the specific

characteristics of that trip but on the individual's attitude toward and willingness to bicycle.

While attitudinal research gives important insights into pedestrian and transit travel choices as

well, its implications are perhaps most significant for bicycle travel.
U.S. Dept. of Transportation's Federal Highway Administration - Research, Development, & Technology

Guidebook on Methods to Estimate Non-Motorized Travel: Overview of Methods (July 1999)

3.0 Guide to Available Methods

3.1 Overview of Methods

This section describes eleven types of quantitative methods that can be used to forecast nonmotorized travel demand or that otherwise support the prioritization and analysis of nonmotorized projects. These methods are categorized according to four major purposes, as shown

and described in table 3.1. Figure 3.1 illustrates how these four purposes relate to each other to

support demand estimation. Following the overview, section 3.2 summarizes key characteristics

of the methods. Section 3.2 also suggests appropriate methods according to specific purpose such

as forecasting the number of new users of a bicycle/pedestrian trail.

Table 3.1 Categorization of Available Methods.

Purpose Method Description

Demand Estimation.

Methods that can be used to derive

quantitative estimates of demand.

Comparison Studies

Methods that predict non-motorized travel on

a facility by comparing it to usage and to

surrounding population and land use

characteristics of other similar facilities.

Aggregate Behavior Studies

Methods that relate non-motorized travel in an

area to its local population, land use, and other

characteristics, usually through regression

analysis.

Sketch Plan Methods

Methods that predict non-motorized travel on

a facility or in an area based on simple

calculations and rules of thumb about trip

lengths, mode shares, and other aspects of

travel behavior.

Discrete Choice Models

Models that predict an individual's travel

decisions based on characteristics of the

alternatives available to them.
Regional Travel Models

Models that predict total trips by trip purpose,

mode, and origin/destination and distribute

these trips across a network of transportation

facilities, based on land use characteristics

such as population and employment and on

characteristics of the transportation network.

Table 3.1 Categorization of Available Methods (continued)

Purpose Method Description

Relative Demand

Potential

Methods that do not predict actual demand levels, but which can

be used to assess potential demand for or relative levels of nonmotorized travel.

Market

Analysis

Methods that identify a likely or maximum number of bicycle or

pedestrian trips that may be expected given an ideal network of

facilities.

Facility

Demand

Potential

Methods that use local population and land use characteristics to

prioritize projects based on their relative potential for use.

Supply Quality Analysis

Methods that describe the quality of non-motorized facilities

(supply) rather than the demand for such facilities. These may be

useful for estimating demand if demand can be related to the

quality of available facilities.

Bicycle and

Pedestrian

Compatibility

Measures

Measures that relate characteristics of a specific facility such as

safety to its overall attractiveness for bicycling or walking.

Environment

Factors

Measures of facility and environment characteristics at the area

level that describe how attractive the area is to bicycling or

walking.

Supporting Tools and

Techniques Analytical methods to support demand forecasting.
Geographic

Information

Systems

Emerging information management tools, with graphic or

pictorial display capabilities, that can be used in many ways to

evaluate both potential demand and supply quality.

Preference

Surveys

Survey techniques that can be used on their own to determine

factors that influence demand, and that also serve as the

foundation for quantitative forecasting methods such as discrete

choice modeling.

Figure 3.1 Relationship of Methods Supporting Demand Estimation

For each of the 11 methods, a one-page summary is provided which includes an overview of the

method, typical applications, advantages and disadvantages, and one or two real-world examples.

Each summary also includes a quick reference guide, which provides a subjective rating of the

method for five factors as described below. The ratings are provided only as a general

assessment of the method's capabilities, and the quality of specific applications of each of these

methods may vary. More detail on the specific ratings for each method is given in table 3.2,

which follows the individual method overviews.

The five factors and criteria used to rate the factors are as follows:

• Ease of Use - "Easy" if the method could be applied by a layperson with basic research
and data analysis capabilities; "difficult" if the method requires extensive specialized

training to understand and apply.

• Data Requirements - "Minimal" if the method primarily uses existing data that can

easily be collected and evaluated; "extensive" if it requires significant new data collection

efforts.

• Accuracy - "Low" if forecasts have not corresponded well to observations; "high" if

forecasts have been found to closely reflect actual demand.

• Sensitivity to Design Factors - "Low" if the method cannot assess the impacts of

specific design factors on demand; "high" if the method can assess the impacts of

multiple factors and the interactive effects of these factors.

• Widely Used - "No" if only a few applications have been identified; "yes" if the method

has been widely used in practice.

Finally, the overview page indicates whether the method can be used to predict demand at the

facility level, area/regional level, or both. Facility-level methods predict the number of users of a

specific facility such as a non-motorized trail, bicycle lane, or pedestrian bridge. Area-level

methods predict total bicycle or pedestrian trips for an entire area such as a city, census tract, or

other geographic area.

Section 2.0 of Supporting Documentation presents a more indepth, structured description of each

method as well as specific variations and applications of the method. Section 3.0 contains

bibliographic references for the real-world examples highlighted in this section. Section 4.0

identifies useful contacts, including individuals and organizations, in the area of non-motorized

travel estimation.

Overview

The simplest form of demand forecasting, comparison studies compare usage levels

before and after a change (such as a facility improvement), or compare travel levels

across facilities with similar characteristics. The results of a comparison study can

be used to predict the impacts on non-motorized travel of a similar improvement in

another situation, assuming that all other influencing factors are roughly the same

between the two situations.

Typical

Applicati

ons

Before-and-after studies have been widely used in Europe to assess the mode choice

impacts of programs to improve bicycle and pedestrian facilities. Some studies have

focused on the change in mode split for an urban area as a whole, after a city-wide
pro- gram of improvements. Others have focused on specific facilities, conducting

user counts both before and after an improvement to the facility. Comparison

studies have also been performed in the United States, using counts from existing

trails to forecast the number of users on a new trail.

Advantages This method is simple to understand and relatively easy to apply.

Disadvanta

ges

Comparison studies only provide a rough estimate of demand for proposed

facilities. Unless very carefully designed, comparison studies may not control for

other factors unrelated to the facility improvement which may affect usage levels. It

is often difficult to find truly comparable facilities. Because of possible differences

in situations, trans- transferring results from one situation to another may lead to

incorrect usage forecasts.

Central Massachusetts Rail

Trail Bikeway

To estimate the potential usage of

a proposed rail trail in

Massachusetts, planning staff

conducted bicycle counts on an

existing trail which has

characteristics similar to the

proposed facility. These counts

were then factored based on the

ratio of total population within the

corridors surrounding the two

facilities to predict total trips on

the proposed facility. Total

volumes were distributed

throughout the proposed corridor

based on the population of

communities along the corridor.

An alternative method was also

applied in which usage of the

existing trails was factored by the

ratio of bicycle commuting mode

share in the two corridors, as

determined from census data

(Lewis and Kirk, 1997).

Comparison of Trails in

Australia

Wigan (1997) compared the

characteristics of users and the

surrounding population on two

existing facilities in Australia.

Trail users were surveyed

regarding mode of access to the

trail, access distance, and personal

characteristics. Data on population

in the surrounding area were also

analyzed. The results indicate that

the Lower Yarra trail attracted

more users from a wider range of

distances than the Lower

Maribrynong, despite similar

levels of surrounding population.

The authors concluded that with

better signage, improved linkages,

and promotional efforts for the

Lower Maribrynong facility,

usage could be comparable to the

Lower Yarra trail.
Overview Aggregate behavior studies involve the development of models to predict mode split

and/or other travel behavior characteristics for an aggregate population, such as residents of a

census tract or metropolitan area. Prediction is based on characteristics of the population and of

the area. An example of an aggregate model is an equation to predict the percentage of trips

taken by bicycle in individual census tracts in a metropolitan area, based on the average income

of the tract and on the total length of bike- ways in the tract.

Typical Applications Aggregate behavior studies have been conducted in the United States and

the United Kingdom, primarily utilizing census data and other readily available data sources to

predict work-trip mode split at a tract, city, or metropolitan-area level.

Advantages Aggregate behavior models have isolated some factors that can be related to nonmotorized travel and have developed quantitative relationships between these factors and modal

split. Also, the results of these studies are potentially useful for the trip generation component of

regional travel models which include non-motorized modes.

Disadvantages Aggregate behavior models have generally had low explanatory power and have

not been successful at predicting mode splits when applied to other areas. Predicting behavior at

an aggregate level suffers from a number of significant difficulties, including: (1) aggregate level

data can mask significant variances within a population which affect behavior, e.g., the average

income of a census tract may be much less important than the distribution of income; (2) the

method ignores the impact of factors which are not readily available, such as attitudinal factors;

(3) the primary data source on trips at a zonal/aggregate level is the census, which looks only at

work trips; and (4) the available data generally do not include environmental variables which

describe the overall quality of the area for bicycling or walking, the overall quality of alternative

modes, etc.

Bicycle Journey-to-Work in the

UK

Ashley and Banister (1989) used

UK census and other data to (1)

evaluate factors influencing

cycling to work, (2) develop a

model to predict the proportion of

residents in a ward bicycling to

work, and (3) test the model. The

Bicycle Mode Split in U.S.

Cities

Nelson and Allen (1997)

conducted a cross-sectional

analysis of 18 U.S. cities to

predict work trip bicycle

mode split (from census data)

based on weather, terrain,

number of college students,
authors used regression analysis to

test the effects of various factors

on the proportion of ward residents

cycling to work. Factors tested

included personal characteristics,

trip distance, avail- ability of

cycling facilities, avail- ability of

other modes, modes, traffic levels,

and local climate and topography.

and per capita miles of

bikeway facilities. A positive

association was found

between the presence of

bikeway facilities and bicycle

work trip mode split.

Overview Sketch plan methods can be defined as a series of simple calculations to estimate the

number of bicyclists or pedestrians using a facility. These methods generally rely on data that

already exist or can be collected with relative ease (such as census and land use data), and can be

combined with behavioral assumptions derived from other studies. Sketch plan methods vary

widely in their specific approaches and in their level of sophistication.

Typical Applications A variety of pedestrian sketch-plan methods have been developed to

estimate pedestrian volumes under existing and future conditions in a pedestrian activity area,

such as a central business district or shopping center. These methods generally use pedestrian

counts and regression analysis to predict pedestrian volumes as a function of adjacent land uses

and/or indicators of transportation trip generation (parking capacity, transit volumes, traffic

movements, etc.) Alternatively, data on surrounding population and employment may be

combined with assumed trip generation and pedestrian mode shares to estimate levels of

pedestrian traffic. At least one bicycle sketch plan method has also been applied to predict usage

of a new bicycle lane in Seattle. This method relies on census data and simple travel survey data

to estimate the travel impact of the project.

Advantages Sketch plan methods tend to be relatively simple to understand and to apply. If the

methods and data are selected carefully, they may give reasonable estimates of the number of

users of a proposed facility. These methods are best for developing rough estimates for planning

purposes and for comparing potential usage levels among facilities or areas to prioritize actions.

Disadvantages Sketch plan methods tend to rely on limited local data and on general

assumptions about behavior. Therefore, they can be imprecise and may not account well for

specific local conditions such as characteristics of the facility, network, surrounding population,

destinations, or competing modes of travel. In addition, methods and assumptions developed for

specific applications may not always be relevant to applications in other geographical areas.
Estimating Pedestrian Corridor

Activity

Matlick (1996) describes a method

to determine the level of pedestrian

activity in 0.8 km buffer areas in

specific corridors. A variety of

sources was used to estimate

activity within the corridor:

population, mode split, and trip

characteristics from census and

National Personal Transportation

Survey data; land use data from

local data bases; and estimates of

school and transit trips.

Estimating Peak Pedestrians per

Hour

Ercolano (1997) describes a

method that determines site,

corridor, and subarea pedestrian

per hour volumes using local

vehicle per hour turning

movements and mode share census

data (at a minimum). Other

features of this method include the

ability to estimate sidewalk and

intersection trips and the ability to

adjust trips based on completeness

of pedestrian infrastructure and

climatic conditions.

Overview A discrete choice model predicts a decision (choice of mode, choice of route, etc.)

made by an individual as a function of any number of variables, including factors that describe a

facility improvement or policy change. The model can be applied across a population to estimate

the total number of people who change their behavior in response to an action. The model can

also be used to derive elasticities, i.e., the percent change in bicycle or pedestrian travel in

response to a given change in any particular variable.

Typical Applications Discrete choice models are widely used by regional travel modelers to

predict auto vs. transit mode choice. Mode choice models have also been developed that include

bicycling and walking as options; a model was recently developed in Chicago to predict the

impacts of pedestrian and bicycle improvements on transit access mode (see sidebar). Discrete

route choice models have also been developed for bicyclists which model bicyclists preference

for various facility design features when selecting a route.

Advantages Discrete choice models based on local survey data are the most accurate tool

available for predicting travel behavior impacts. These models can be a powerful tool for

isolating and quantifying the effects of specific factors, both personal and environmental, on

travel behavior. They can also be used to examine the interaction of each factor with other

factors, e.g., whether age has an impact on the type of facility preferred.

Disadvantages Development of a discrete choice model generally requires the collection of
extensive survey data and requires expertise in discrete choice modeling techniques. Also, since

the number of factors (facility design, personal, etc.) which can be considered in any particular

modeling exercise is limited, it is not possible to identify or control for all factors which may

influence behavior. Furthermore, a model developed for a specific situation may not be

applicable to other situations if important factors not considered in the model differ between the

two situations.

Transit Access Mode Choice in Chicago

The Chicago Regional Transit Authority recently developed a set of

discrete choice models to predict the impacts on transit access mode of

bicycle and pedestrian improvements to rail station areas in Chicago.

Surveys to deter- mine existing commuters mode choice, station access

distance, and other characteristics were used in conjunction with visual

simulation surveys to estimate whether people would shift to nonmotorized access modes as a result of various improvements. Bicycle

improvements tested included removal of debris, provision of parking,

slowing of traffic, and development of curb lanes, paths, and bicycle

routes. Pedestrian improvements tested included sidewalks, recreation

paths, slowing of traffic, and various improvements to intersection

crossings (Wilbur Smith Associates, 1997).

Overview Regional travel models, commonly referred to as four-step travel demand models, use

existing and future land use conditions and transportation network characteristics, in conjunction

with models of human behavior, to predict future travel patterns. These models are described in

more detail in section 2.4 of this overview and section 2.8 of the supporting documentation.

Typical Applications Traditionally, regional travel models have been oriented toward predicting

trips by automobile and transit. However, a number of models in the United States, Canada, and

Europe have recently been modified to estimate non-motorized mode splits based on ratings of

the pedestrian friendliness or bicycle friendliness of individual zones. Some models have also

been modified to include bicycle and/or pedestrian facility networks and to predict the route

choice impacts of improving or adding facilities. Models have also been developed specifically

for bicycle or pedestrian travel. For example, in the 1970s pedestrian demand models were

developed for various commercial business districts in the United States. These models related

pedestrian trips to land uses at a block level and assigned trips between blocks based on

characteristics of the pedestrian network.

Advantages Regional travel models have been developed for all major urban areas in the United

States. The regional travel model structure provides an integrated framework for analyzing
travelers choices between modes. Given sufficient data collection and enhancements to the

model structure, regional travel models could serve as a powerful tool for analyzing bicycle and

pedestrian travel. Regional travel models can also serve as a source of data, such as total trips

generated in an area, which are useful for other bicycle or pedestrian modeling or sketchplanning efforts.

Disadvantages The current generation of regional travel models was developed at a spatial scale

appropriate for automobile rather than bicycle or pedestrian travel. Also, incorporation of nonmotorized modes may require significant data collection to create a zone-level "environment

factor" or develop a network of bicycle and pedestrian facilities. Current regional travel models

also do not consider trips made for the sole purpose of recreation. Finally, the development and

modification of travel models require considerable expertise and the use of specialized software

packages.

Edmonton Transport Analysis Model (Canada)

The Edmonton Transport Analysis Model recently developed for the

Edmonton, Canada region includes both walk and bicycle as separate

modes and also includes bicycle network characteristics in determining

mode choice. Links in the network model can be coded in three ways:

bicycle path, bicycle lane, or mixed traffic. Bicycle travel time on each

link is adjusted by a factor representing the relative onerousness of

bicycling by facility type. These factors are derived from a hypothetical

choice survey of bicyclists in which bicyclists are asked to choose

between different routes based on distance, facility type, and other

factors (Hunt, Brownlee, and Doblanko, 1997).

Overview This is a general approach which estimates the maximum potential number of trips by

bicycle or walking in an area, based on (1) current trip length distributions, usually by trip

purpose; (2) rules of thumb on the maximum percentage of bicycling or walking trips by trip

distance and purpose; and/or (3) the percentage of the population likely to switch to bicycling or

walking, based on identifying a target market of bicyclists or walkers according to commute

distance, demographic characteristics, etc. An ideal network of facilities is assumed, i.e., this

method estimates how many trips might take place if the quality of facilities was not an issue.

Typical Applications Market analysis is a relatively common approach that can be applied in

many different ways, with varying levels of detail. Some studies have taken aggregate data on

trip lengths by purpose for an area and applied a rule of thumb about the maximum bicycle or

walk trip length, in conjunction with a best guess as to the likely mode share diversion, to
estimate the potential bicycle or walk mode share. Others have focused on defining the

demographic characteristics of people most likely to walk or bicycle, and subsequently using

demographic information for an area, in conjunction with trip length distributions, to obtain an

overall maximum potential mode split under ideal conditions.

Advantages Market analysis methods generally define an "upper bound" on the number of trips

by cycling or walking and may therefore give municipalities a target to shoot for in developing

plans to improve facilities city-wide. This type of analysis can also be helpful in identifying areas

of greatest potential demand, as an aid to prioritizing projects.

Disadvantages Market analysis methods are intended only to achieve rough estimates of the

maximum number of trips that could be diverted to bicycling or walking. The methods are not

useful for estimating changes in demand in response to an improvement, and they shed little light

on factors affecting the decision to walk or bicycle.

Market for Bicycle Commuting in the San Francisco Bay Area

Deakin (1985) defined a demographic target group for Bay Area

commuter bicycling, based on data from the Bay Area Travel Survey, a

review of the literature, and interviews with local and state officials. Her

market was defined as employed full-time, under 40 years old, travels

less than 11.3 km one-way to work, drives alone during the peak period,

and owns a bike suitable for commuting. She used these criteria to

estimate a reasonable upper bound on the size of the potential bicycle

commuter market.

Overview Measures of potential demand have been developed for both bicycle and pedestrian

facilities for the purpose of prioritizing facility improvements according to areas of highest

potential demand. Demand potential is measured based on characteristics and levels of the

surrounding population, trip generators, as well as other environmental factors such as

topography and the quality of connecting facilities.

Typical Applications Measures for both bicycle and pedestrian facility demand potential have

been developed and applied to prioritize improvements (see sidebar).

Advantages Measures of potential demand can be a useful aid to prioritizing locations for

improvements, particularly when applied in conjunction with measures of supply or facility

quality to identify areas of both high potential demand and significant deficiencies. In addition,

these measures can frequently be constructed from readily available data sources such as the

census and local land use data bases.
Disadvantages Measures of potential demand only indicate relative levels of demand between

areas, rather than predict the actual number of users of a facility. They do not indicate the extent

to which usage is likely to increase as the result of a particular improvement, and they do not

indicate which improvements to a specific facility or area should be given the highest priority.

Also, the factors used in constructing the index may or may not be good indicators of the true

potential demand for the facility.

Latent Demand Score

A Latent Demand Score (LDS)

technique has been developed to

estimate the latent or potential

demand for bicycle travel

assuming the existence of a bicycle

facility. Trips are estimated based

on the size and proximity of

population and activity centers to

the proposed facility, using

Geographic Information System

(GIS) analysis tools. The LDS has

been applied in a number of cities

with the purpose of prioritizing

existing bicycle facility

improvements or new bicycle

facility improvements or new

bicycle facilities. (Landis, 1996).

The LDS may be combined with

bicycle level of service measures.

Pedestrian Potential Index

A Pedestrian Potential Index has

been developed and applied in

Oregon to prioritize locations for

pedestrian improvements. The

index uses three main factors: (1)

proximity factors that refer to

pedestrian generators such as

schools, transit or neighborhood

shopping; (2) environmental

factors such as mixed use and

street connectivity; and (3) policy

factors that identify certain areas

as critical for pedestrians. The

index has been applied in

conjunction with a Deficiency

Index to identify areas with both

high potential demand and

significant deficiencies. (City of

Portland, 1997).

Overview A variety of compatibility measures have been developed to indicate the suitability of

a particular facility for bicycle or pedestrian travel. These measures have been given names such

as "Level of Service," "Stress Level," "Compatibility Index," and "Interaction Hazard Score."

The measures combine factors such as motor vehicle traffic volume and speeds, lane or sidewalk

width, pavement quality, and pedestrian amenities into an index of overall suitability for travel.

The measures can be used alone or in conjunction with measures of potential demand to

prioritize facilities for improvements.

Typical Applications Compatibility measures have been used in a number of cities to rank

facilities for purposes of prioritizing projects. For example, Orange County, NC, has applied the
Bicycle Stress Level index to determine the suitability of their planned bicycle routes. Level-ofservice measures have also been applied in conjunction with the Latent Demand Score to

prioritize projects in various urban areas in Florida. Oregon has developed a Deficiency Index

which it uses in conjunction with potential demand indicators to rank and prioritize pedestrian

facilities.

Advantages Compatibility measures can serve as a useful means of prioritizing facilities for

improvement as well as determining which improvements will be most beneficial. Compatibility

measures may also become a key component of non-motorized travel demand forecasting, if

relationships can be developed between the indices and individuals' likelihood of making a

bicycling or walking trip.

Disadvantages Existing indices primarily rate individual segments rather than describing the

overall compatibility of a route. They cannot account for the effects of intersections and other

discontinuities, and they do not sufficiently describe the overall compatibility of a route made up

of different segments with different ratings. Also, the indices may not include all relevant factors

(or may require significant data collection to do so), and they may not properly reflect

perceptions if not validated through surveys. In addition, they do not predict the actual number of

trips on the segment.

Bicycle Compatibility Index

The Federal Highway Administration has recently developed a bicycle

compatibility index (BCI) to describe the compatibility of a facility for

cycling (FHWA, 1998). The BCI uses a formula based on traffic

volume, speed, lane width, and other indicators of bicyclist stress to rank

a road segment for compatibility on a scale of 1 to 6, which is then

equated to a level-of-service (LOS) rating. Qualitative adjustment

factors were developed to consider instances of high volumes of trucks

or buses, right-turning vehicles, and vehicles turning into and out of

driveways. The index was developed using a video survey methodology

which asked participants to rate their comfort level on various

videotaped facilities.

Overview Pedestrian and bicycle environment factors describe the friendliness of an area (such

as a city block, census tract, or traffic analysis zone) for walking and/or bicycling. The factors

are quantitative and may be a composite of a number of quantitative descriptors and subjective

factors. Examples of factors considered include lane or sidewalk width, street continuity,

topography, and the aesthetic quality of the environment.
Typical Applications Pedestrian and bicycle environment factors have been developed primarily

for use in regional travel models. A pedestrian environment factor has been developed and

applied to the regional travel model in Portland, OR and modified versions have been applied in

Sacramento, CA and Washington, DC. Montgomery County, MD, has developed a different

pedestrian/bicycle environment factor for use in its travel model. A transit friendliness factor

describing the quality of pedestrian access to transit has been developed in Washington State.

Advantages Considerable research has been performed recently on factors that make areas

inviting to pedestrians, and much of this knowledge has been incorporated in the current

generation of environment factors. The factors have been found to enhance the performance of

travel models in Portland, OR and Montgomery County, MD particularly for predicting vehicle

trips from an area. These factors may also be useful in prioritizing areas for improvements, based

on the relative ratings of individual areas.

Disadvantages Environment factors are frequently based on subjective ratings and their

performance at predicting actual variations in travel behavior has not yet been widely validated.

Also, separate bicycle environment factors have not been developed; the ability of these or of

combined pedestrian/bicycle factors to predict bicycle trip activity has not yet been tested. In

addition, environment factors require considerable field data collection to develop for a specific

area.

Portland, OR, Pedestrian Environment Factor

Portland's Pedestrian Environment Factor (PEF), developed for use in its

regional travel model, includes four elements: sidewalk availability, ease

of street crossing, connectivity of street/sidewalk system, and terrain.

Each traffic analysis zone is ranked for each element on a scale of zero

to three, with higher numbers representing higher quality pedestrian

environments, so the overall PEF can range from 0 to 12 (1,000 Friends

of Oregon, 1992 - 1997).

Overview Geographic Information Systems (GIS) relate environmental and population data in a

spatial framework, using location points, lines (commonly roadway links and corridors),

corridors), and polygons (surface areas and analysis zones). GIS are employed as a mechanism

for the physical inventory of transportation facilities; as a planning tool to relate available

environmental, personal transportation and household characteristics data; as a spatial analysis

tool for calculating distances and areas; as a network performance monitor; and as a vehicle for

the graphic display of data and analysis in a geographic context.
Typical Applications GIS have been used in non-motorized planning to inventory and evaluate

facilities such as roads and sidewalks; establish spatial relationships between roadway network

links, features such as activity centers, and area population characteristics; compare and display

current conditions with projected travel and conditions; assess total network performance and

identify optimal routes; produce printed maps; and develop network measures (e.g. street density

and connectivity) and land use measures (e.g., mix of residential, office, and retail) which can be

related to the likelihood of walking or bicycling.

Advantages GIS can greatly increase the ease of analyzing data relevant to non-motorized travel

forecasting. For example, a corridor surrounding a facility can be defined and the characteristics

of the population within the corridor easily identified. GIS allows development of spatial

measures and analysis of data relationships which might otherwise be prohibitively timeconsuming or impossible. The display capabilities of GIS are also valuable for conveying

information to policymakers and the public.

Disadvantages GIS require considerable user skill as well as specialized software to develop,

although future developments will make them more accessible to laypersons. Also, since GIS

can only manage and analyze data, the data must still be collected through other means.

Warwick, RI, Bicycle Network Study

A Bicycle Network Study in Warwick, RI, was assisted by GIS

methods. Trip generation estimates were calculated as a function of

employment, school enrollment, and total population for traffic analysis

zones adjacent to the bicycle network. Composite trip generation scores

were then attributed to network segments within the areas of influence

of trip generators. The results of this analysis were compared to the

existing designated bicycle route network. Alternative route

designations were suggested where an undesignated roadway link's

potential scored higher than a parallel or adjacent designated route

(Beltz and Burgess, 1997).

Overview Using survey research techniques, preference surveys (also known as stated

preference surveys) focus on the choices that people would make given discrete alternatives.

Respondents are asked to express an attitude or make a choice as to how they would act under

certain conditions. Two basic types of preference surveys exist. Attitudinal surveys ask

respondents directly how they would respond to various actions (e.g., would they bicycle if bike

lanes were available), or ask them to rate their preferences for various improvements.

Hypothetical choice surveys require respondents to make choices between hypothetical

alternatives with varying attributes, and survey results are then used to develop models of
behavior.

Typical Applications Attitudinal surveys have been widely used to estimate the potential

impacts of bicycle and pedestrian improvements and to determine relative preferences for such

improvements. Hypothetical choice surveys are generally used to develop discrete choice models

and to estimate the relative importance of each attribute (time, cost, presence of bike lanes, etc.)

in common terms.

Advantages Attitudinal surveys are relatively easy to design and implement. They can also be

good tools for evaluating relative preferences and for estimating the maximum possible response

to an action. Hypothetical choice surveys, if carefully designed, can be used to develop relatively

accurate models of behavior and to give quantitative information on the relative importance

which people place on various factors.

Disadvantages Attitudinal surveys often significantly overestimate the response to a bicycle or

pedestrian improvement, since people tend to be more likely to state that they will change their

behavior than to actually do so (Goldsmith, 1992). Therefore, they are not well-suited for

predicting actual shifts in travel demand. While hypothetical choice surveys overcome many of

the limitations of attitudinal surveys, they must be designed carefully and require considerable

time and expertise to implement. Both types of preference surveys suffer from the further

drawback that people may not have any real-world experience with the choices they are asked to

make, and may therefore be unable to indicate their preferences or actions with accuracy.

Transit Access Mode Choice in Chicago

The Chicago Regional Transit Authority (RTA) surveyed transit and

auto users to determine reasons why they did not currently walk or

bicycle to a transit station. (These surveys were also used to develop

models of individual behavior, as described under Discrete Choice

Models.) Respondents were asked to identify specific reasons for not

bicycling or walking, such as lack of secure parking, dangerous traffic

conditions, or inadequate sidewalks or path- ways. Two different survey

methods were employed: an intercept survey in which respondents were

asked directly to rate factors, and an interactive video survey in which

respondents were asked to make tradeoffs between vari3.2 Key Characteristics and Uses of Each Method

This section summarizes key characteristics of the methods and suggests appropriate methods

according to specific purpose such as forecasting the number of new users of a bicycle/pedestrian

trail. More specifically, table 3.2 summarizes key characteristics of each of the 11 methods,

providing more detail on the factors (e.g., ease of use and data requirements) rated in the quick

reference guide for each method.

Tables 3.3 through 3.6 are intended as a guide for practitioners who need to choose the most

appropriate method for a specific situation. Each table lists a specific purpose for which nonmotorized demand forecasting methods may be applied and suggests which methods are most

appropriate for that purpose. Generally the methods are ordered from simpler to more complex in
Tables 3.3 - 3.5. For each of these methods, the table describes the specific way in which the

method would be applied and identifies major advantages and disadvantages of using the method

for the given purpose. These purposes include:

Table 3.3 - estimating the number of users of a new facility;

Table 3.4 - estimating the number of new bicycle or pedestrian trips area-wide, as a result of

facility or network improvements;

Table 3.5 - prioritizing design features for a specific facility; and

Table 3.6 - prioritizing facilities for improvement.

Figure 3.2 If Sidewalks Were Built Here,

How Many People Would Use Them?

How Far Up on the Priority List is This Project?

Table 3.2 Key Characteristics of Available Methods.

Metho

d

Ease of Use Data

Requirements Accuracy Sensitivity toDesign Factors Where Used

Demand Estimation

Compa

rison

Studies

Simple to

understand

and relatively

easy to apply

Requires facility

user counts; data

on surrounding

population and

land uses are

optional

May provide

rough estimates

of demand if

truly comparable

case studies can

be found.

Accuracy has not

been formally

tested.

Relatively low;

requires

identification of

comparable

facilities within

a comparable

environment

Massachusetts;

Netherlands;

Germany; Australia

Aggre

gate

Requires

simple

Varies; can use

existing sources

Models have

generally had low

Low, since

detailed

UK; Berkeley, CA
Behavi

or

Studies

statistical

analysis skills

such as census

and local land

use data bases

explanatory

power and have

not been

transferable

information on

facilities has

generally not

been collected

Sketch

Plan

Metho

ds

Methods are

relatively

simple to

apply

Varies; can use

existing sources

such as census

and local land

use data bases

Varies by

method; some

methods may

give reasonable

estimates others

have not been

formally tested

Low; rely on

general

assumptions

Seattle, WA

(bicycle); New York

City, NY;

Plattsburgh, NY;

Milwaukee, WI;

Toronto and

Montreal, Canada

(pedestrian)

Discre

te

Choice

Model

s

Knowledge

of statistical

analysis and

specialized

survey and

modeling

techniques is

required

Usually requires

survey data

collection

specific to

situation being

analyzed

Can be relatively

accurate in

predicting

impacts of

specific actions

High, although

only limited

number of

factors can be

considered at

once

Wisconsin;

California, Chicago,

IL; Raleigh, NC

Region

al

Travel

Model

s

Requires

established

capabilities

for travel

demand

modeling

May require

additional data

collection on

bicycle and

pedestrian travel

patterns and/or

facility

characteristics

Including

bikes/peds has

improved

performance of

some models at

predicting auto

and transit trips

Potentially

high; limited by

data availability

and tradeoff

information

Portland, OR;

Montgomery County,

MD; Sacramento and

San Francisco, CA;

Edmonton, Canada;

Leicester, UK;

Netherlands

Relative Demand Potential

Meth

od Ease of Use Data Requirements Accuracy

Sensitivit

y to

Design

Factors

Where Used

Mark

et

Anal

ysis

Methods are

relatively

simple to

apply

Data required on trip

length distributions

(from travel survey or

regional travel model);

Untested.

Methods are

designed to

predict an upper

Low;

assumes

ideal

network

San Francisco, CA;

Chicago, IL; Bend,

OR; Minneapolis,

MN; Europe
other population data

may be needed

bound under ideal

conditions

of

facilities

Facil

ity

Dem

and

Pote

ntial

Methods are

relatively

simple to

apply

Data required on local

population and land use,

some methods require

trip distributions by

length and purpose

Attempts to apply

Latent Demand

Score in practice

have had mixed

results

Low;

assumes

ideal

network

of

facilities

(Pedestrian

Potential Index);

Florida;

Birmingham, AL;

Philadelphia, PA ;

Portland, OR

(pedestrian)

Supp

ly

Qual

ity

Anal

ysis

Bicyc

le

and

Pede

stria

n

Com

patib

ility

Meas

ures

Methods vary

but are

generally

relatively

simple to

apply

Requires data on facility

characteristics; some

may exist, others may

need to be collected,

depending on method

Has not been

tested with

respect to

forecasting

demand

High;

factors

included

depend

on

specific

index

Orange County,

NC; Gainesville,

FL; Buffalo, NY;

Ames, IA

Envir

onme

nt

Fact

ors

Relatively

simple to

apply; may

require

judgment in

developing

ratings

Generally requires field

data collection on

facility/ environmental

characteristics

Have improved

performance of

some regional

travel models at

predicting auto,

transit trips

High;

factors

included

depend

on

specific

index

Portland, OR;

Montgomery

County, MD;

Sacramento, CA

Supporting Tools and Techniques

Geograp

hic

Informat

Generally

requires

specialized

GIS can manage and

analyze a wide

variety of data based

Has potential to

improve

accuracy of

Potential

to store

informatio

Portland, OR;

Seattle, WA;

Buffalo, NY;
ion

Systems

knowledge of

GIS analysis

techniques

on availability and

needs

forecasting

methods

n on a

variety of

facility

design

factors

Warwick, RI;

Orange County,

CA; Fort Collins,

CO; Buffalo, NY;

Ames, IA

Preferen

ce

Surveys

Requires

knowledge of

survey

research

techniques;

may require

specialized

survey design

and analysis

skills

Requires survey data

collection

Performance

depends on

quality of survey

design and

implementation

Variety of

design

factors can

be

considered

in survey

Widespread

Table 3.3 Methods for Estimating the Number of Users of a New Facility.

Method Specific Application Major Advantages or Drawbacks

Comparison Study Look at usage on comparable

facility

May be difficult to find truly

comparable situation

Sketch-Plan Method

Look at local population, trip

generators, non-motorized work

trip percentages for area around

facility to estimate potential trips

Easy way to get a rough estimate of

potential usage; however, difficult to

consider factors such as non-work

trips, whether facility serves local

travel patterns, existence of supporting

facilities/network, etc.

Preference Survey

(Attitudinal)

Survey local residents and

commuters as to whether they

would use the facility

Will give relative indication of interest,

but will generally overstate actual

likelihood of using facility

Preference Survey

(Hypothetical

Choice)

and Discrete Choice

Model

Conduct survey of whether people

would use facility under various

scenarios; develop behavior model

to predict usage

A carefully-designed hypothetical

choice survey may be the most

accurate method but is also resourceintensive

Regional Travel

Model

Modify existing regional travel

model to include new facility

Requires travel model which already

includes bicycling/walking networks;

will not capture recreational travel

Table 3.4 Methods for Estimating the Number of New Bicycle or Pedestrian Trips Area-
wide as a Result of Facility or Network Improvements.

Method Specific Application Major Advantages or Drawbacks

Preference Survey

(Attitudinal)

Survey residents to ask if they

would choose to walk or bicycle

given improvements

Survey results tend to overstate

willingness to change mode of travel

Aggregate Behavior

Study

Develop relationship between

levels of non-motorized tripmaking and overall

facility/network characteristics,

based on data from other

cities/areas

Requires data on many cities or areas

which includes indicators of nonmotorized trip making as well as

information on existing facilities/

networks comparable to the

improvements being considered locally

Preference Survey

(Hypothetical

Choice)

and Discrete Choice

Model

Conduct survey of whether people

would bicycle or walk under

various city-wide improvement

scenarios; develop behavior model

to predict usage

A carefully-designed hypothetical

choice survey may be relatively

accurate but is also resource-intensive

Regional Travel

Model

Modify pedestrian/bicycle

environment factors or network

links in regional travel model

Requires travel model which already

includes bicycling/walking

environment factors and/or networks,

and that these networks include facility

characteristics that are desired to be

improved; models must also be based

on data relating behavior responses to

design improvements

Table 3.5 Methods for Prioritizing Design Features for a Specific Facility.

Method Specific Application Major Advantages or Drawbacks

Supply Quality

Analysis

Compare improvements in quality

rating as a result of various design

improvements

Good for identifying facility

deficiencies and most effective

improvements, but using this technique

alone does not predict benefits in terms

of new users

Preference Survey

(Attitudinal)

Ask local residents, employees,

bicyclists, pedestrians, etc., which

design improvements are highest

priority

Responses may vary depending on

population surveyed; for example, just

surveying existing users will not

indicate number of new users attracted

to facility as a result of improvements

Preference Survey Conduct survey to determine Determining who to survey can be a
(Hypothetical

Choice)

and Discrete Choice

Model

relative-preference for facility

improvements, and build model to

determine likely number of new

users

problem; however, can actually predict

benefits of each improvement based on

change in usage as well as benefits to

existing users

Regional Travel

Model

Modify facility travel times to

reflect proposed new facilities or

design improvements, to determine

travel-time equivalent benefits to

existing users and number of new

users

Considers most types and

origins/destinations of trips. However,

requires that the travel network is

coded with the bicycle or pedestrian

facility design features to be analyzed,

and that the valuation of travel time by

bicycle or foot has been related to

these design features.

Table 3.6 Methods for Prioritizing Facilities for Improvement.

Method Specific Application Major Advantages or Drawbacks

Supply Quality

Analysis

Rate facilities based on existing

bicycle or pedestrian compatibility,

environment factors, or deficiency

indicators; prioritize according to

ratings

Does not look at existing or potential

demand/usage on facilities

Preference Survey

(Attitudinal)

Ask local residents, employees,

bicyclists/

pedestrians, etc., which are highest

priority facilities to improve

Responses may vary depending on

population surveyed; for example, just

surveying existing users will not

indicate number of new users attracted

to facility; need to survey population

of potential users as well

Facility Demand

Potential

Look at potential demand for

facility based on surrounding

population, land uses, etc., and

prioritize according to highest

potential

Serves as a good basis for

prioritization assuming that measures

of potential demand are proportional to

actual future demand across projects.

Combination of

Facility

Demand Potential

and

Supply Quality

Analysis

Rate facilities both on potential

demand and existing quality;

prioritize facilities with highest

potential and lowest quality

Combines strengths of both methods;

however, still does not indicate actual

number of new users
U.S. Dept. of Transportation's Federal Highway Administration - Research, Development, & Technology

Guidebook on Methods to Estimate Non-Motorized Travel: Overview of Methods (July 1999)

4.0 Conclusions and Future Needs

4.1 Conclusions

A bicycle or pedestrian planner wishing to estimate future levels of non-motorized travel has a

number of options. These include comparisons of proposed projects with usage on similar

existing projects, calculations based on census and other available local data and assumptions,

aggregate and disaggregate behavior models to predict travel choices, and inclusion of bicycle

and pedestrian factors in existing regional travel models. Alternatively, the planner may choose

to look at measures of the potential market for bicycling or walking, rather than explicitly

forecasting demand. The planner may also use these measures in conjunction with measures of

the quality of facilities supplied to prioritize improvements where they are most needed. Finally,

these methods can be enhanced by tools and techniques such as GIS and preference surveys of

travelers.

In addition, planners may develop combinations of existing and new approaches. Bicycle and

pedestrian travel demand forecasting is an evolving field, and creative thought is needed by those

who are confronted with planning needs in the real world. The best approach for any particular

situation will depend on available knowledge, data, financial, and technical resources, as well as

the specific purpose for which the demand forecasts are being developed.

Finally, planners should be aware of the limitations as well as the advantages of existing

methods, and should supplement quantitative forecasts with the judgment of local practitioners

and advocates when planning projects. Despite limitations, however, the methods discussed in

this guidebook can provide valuable information, both for estimating the benefits of proposed

projects and for prioritizing projects and improvements to achieve the greatest benefits to users.

4.2 Future Needs

As a result of developing this guidebook, a number of areas have been identified in which

additional research and methodological development could be particularly useful. These

suggestions are presented so that users of this guidebook can consider the limitations of existing

knowledge when developing their own methods, collecting data, and conducting research.

Recommended future efforts include:

• Development of a manual for bicycle and pedestrian sketch-planning. In the short

term, practitioners with neither the resources nor the expertise to conduct an indepth

forecasting study need a simple yet effective set of tools and data for estimating future

demand.
• Further research on factors influencing non-motorized travel behavior. Ongoing

research into the specific factors that influence decisions to bicycle and walk will

improve the quality of both sketch-planning and more advanced modeling techniques .

Research should focus not just on identifying specific factors but on how these factors

interact and how they can be modeled to assist in forecasting bicycle or pedestrian travel

for specific projects.

• Integration of bicycle and pedestrian considerations into mainstream transportation

models and planning. Future improvements to regional travel models hold great promise

to improve the quality of non-motorized travel modeling, if these modes are included in

travel model development efforts. Inclusion of these modes will also help place bicycles

and pedestrians on a "level playing field" with motorized modes in transportation

planning.

Development of a Manual for Bicycle and Pedestrian Sketch-Planning

In the absence of better methods, practitioners who need to estimate usage on a non-motorized

facility generally resort to back-of-the-envelope calculations based on readily available data and

rules of thumb on travel behavior. These methods are somewhat crude and generally have not

been tested for accuracy, but nevertheless may be the best that is possible given limitations on

data, resources, and expertise. Development of a sketch-planning manual for bicycle and

pedestrian forecasting would improve the state of practice in this area and could be widely used

by bicycle and pedestrian planners. Such a manual would include methods and supporting data

for developing local estimates of demand. Specific elements of the manual might include:

• A summary of available bicycle and pedestrian travel characteristics, including trip length

distributions by type of trip, personal and household characteristics of travelers, etc.;

• A summary of studies that have evaluated the effects of various bicycle or pedestrian

facility or policy improvements on non-motorized travel;

• Identification and description of existing data sources, such as the census, travel surveys,

and land use data bases, which can support the estimation of non-motorized travel

demand;

• Guidelines for collecting local data, including user counts and surveys of existing and

potential users;

• Applications of new technologies, including GIS methods and Intelligent Transportation

System (ITS) technologies, for data collection and analysis; and

• A set of back-of-the-envelope procedures for using these various data sources to obtain

rough estimates of demand.

The sketch-planning techniques could, at a minimum, draw from techniques that already have

been developed by practitioners and identified in this guidebook. Ideally, such techniques would

be further developed and tested in practice to ensure that they are applicable to a variety of areas

and that they give reasonable results.
Additional research useful for this type of guidebook might include further analysis of data

sources, such as trail user counts and surveys in conjunction with other trail-related data, to look

for patterns in facility usage and to provide information useful for the planning of comparable

facilities.

Research on Factors Influencing Non-Motorized Travel Behavior

Along with the short-term documentation of planning methods and data for practitioners, more

fundamental research is needed into the factors influencing non-motorized travel behavior and

how these factors can be modeled to support demand forecasting. Particular attention should be

given to identifying factors that are both of significance in predicting non-motorized travel

behavior and that can be collected or created with relative ease from existing data sources or

future survey efforts. Factors should be investigated that can be useful in a variety of forecasting

methodologies ranging from sketch-planning techniques to travel demand and network

modeling. Focusing on the individual traveler as the unit of analysis, rather than on aggregatelevel studies, will provide richer information that will be useful not only for improvements to

current efforts but to future modeling efforts such as activity-based analysis and

microsimulation.

Facility design characteristics. Significant research has focused on developing quantitative

measures of the quality or compatibility of facilities for bicyclists and pedestrians. The next step

is to integrate these measures into methods of forecasting travel demand. Research is needed into

how to aggregate facility-level compatibility measures, such as the Bicycle Compatibility Index,

into an overall route or network compatibility measure, including facilities of varying quality as

well as intersections and other discontinuities. Ultimately, the overall route or set of route

options, rather than just individual facility characteristics, determines whether or not the bicyclist

or pedestrian makes the trip.

Environment factors. Area-level environment factors that describe, or act as a proxy for, the

relative attractiveness of bicycling or walking at an area/zonal level are potentially useful and

should be further developed and tested. Pedestrian environment factors should be further refined

and tested to verify their predictive capability. (Efforts in this area should build on recent

research relating neighborhood design factors to levels of walking.) Bicycle environment factors

also should be developed and tested for predictive capability. Other possibilities include the

quality or impedance of alternative modes (traffic speeds, LOS, cost of parking, etc.) and the

potential demand based on trip-end characteristics (population, employment, special generators,

etc.). These factors should be useful both in sketch-planning techniques and in regional travel

models where the scale of resolution is too coarse to model every facility in the network.

Attitudinal and perceptual factors. The relative importance of attitudinal and perceptual

factors in the choice to walk or bicycle, as well as their potential uses in modeling, should be

investigated. While gathering such data requires additional collection efforts, factors of this type

have been found to be highly significant in determining travel behavior. Research in this area

should focus on (1) which factors are most important; (2) how they can best be

described/standardized; (3) what level of resources are required to collect these data on an

ongoing basis; (4) how the factors may change over time; (5) how they can most effectively be

influenced; and (6) how they can be integrated into modeling/forecasting techniques to predict

the impacts of various policies. Research in this area can build on behavioral research from the
public health field, as well as on existing studies of attitudes and perceptions regarding bicycling

and walking.

Factors influencing recreational travel. None of the methods discussed in this guidebook make

an explicit distinction between recreational and utilitarian travel. Many aggregate-level methods

consider both types implicitly by looking at overall travel on a facility, while others such as

regional travel models consider only utilitarian trip-making. Forecasting recreational travel at

the individual or disaggregate level requires a different analysis framework, involving lifestyle

and activity patterns, than is generally used in transportation modeling. Approaches from the

public health arena that model the decision to exercise as a function of various

personal/attitudinal characteristics and social factors should be helpful for incorporating

recreational travel in transportation modeling.

Figure 4.1 Models Need to be Capable of Modeling

Both Utilitarian and Recreational Travel.

Market research. Marketers in competitive industries have long recognized that marketing

success depends on targeting the right customer with the right product. State-of-the-art

techniques from the field of market research can be used to better identify the "market segments"

for non-motorized travel, the travel characteristics of each market segment, and the facility

design factors that are important in attracting increased usage from each segment. The trip and

personal characteristics of recreational travelers, for example, should be differentiated from those

of utilitarian travelers, while utilitarian users may be further distinguished as necessity vs.

discretionary, commute vs. non-commute, etc. While some research has been conducted in

defining non-motorized market segments, planners have not adequately identified the differences

in techniques required for identifying the needs and predicting the behavior of these various

groups.

Integration of facility/environment, policy, and personal/attitudinal variables into an

overall modeling framework. Insights from the public health and social marketing fields

suggest that personal attitudes and beliefs interact strongly with environmental and policy

variables to influence travel behavior and mode choice, particularly for bicycling. Accurate

forecasting of bicycle travel will require integrating these variables into a modeling framework
which can include personal/attitudinal variables, and which can account for the fact that the

effects of facility/environmental improvements will depend on (as well as influence) the levels of

these other variables.

Integration of Bicycle and Pedestrian Considerations into

Mainstream Transportation Models and Planning

As a final recommendation, further development of modeling techniques and data sources is

needed to better integrate bicycle and pedestrian travel into mainstream transportation models

and planning activities. Regional travel models have the unique advantage of representing an

integrated framework for predicting travel decisions, considering all trips and modal options, as

well as personal and household characteristics, within the spatial structure of the surrounding

area. Furthermore, they are widely used and accepted as demand forecasting methods for

automobile and transit planning. Improvements to existing models should significantly increase

their usefulness for analyzing non-motorized policies and facility improvements. Specific nearterm and long-term improvements might include:

Data collection on bicycle and pedestrian travel. A general need for all types of bicycle and

pedestrian planning is better data on trip and personal characteristics of travelers. Household

travel surveys performed for modeling purposes are a potentially effective means of collecting

these data. While data on non-motorized trips are increasingly being collected in these surveys,

surveys must be designed carefully to ensure that all non-motorized trips are reported. Also,

since there are generally few reported bicycle trips, additional means of collecting data on

bicycle trips, such as supplemental stated preference surveys, may be required. The potential for

non-motorized data collection using emerging ITS information technologies should also be

investigated.

Spatial scales of models. The scale at which travel is modeled should be refined to be more

relevant to the short distances involved in bicycle and pedestrian travel. Improvements in

computational power and in data management tools will make it easier to analyze smaller-scale

networks of bicycle and pedestrian facilities rather than just major roadways.

Facility design factors. For travel models in which bicycle and pedestrian networks can be

accurately represented, the most important design variables for predicting mode and route choice

should be identified and included in the network link characteristics in the model. This will

require quantifying tradeoffs between these variables and link travel time or distance. Travel

time penalties also need to be developed for major intersections or other discontinuities in the

network. The validity of aggregating link-level factors across routes and networks to produce an

overall "utility" or "compatibility" should be tested. In addition, the potential for transferring

preferences for facility design from studies conducted in one area to other areas, to avoid the

need for locally-specific surveys, should be investigated.

Environment factors. For regional models in which zones are too large to model local nonmotorized networks, further development and testing of zone-level environment factors are

needed to validate the usefulness of these models for analyzing non-motorized travel. These

efforts can build on the outcomes of basic research into these factors and can also utilize GIS

data bases and analysis techniques to develop better factors. In addition, environment factors

should be developed for bicycles as well as pedestrians.
Other environmental and policy variables critical to non-motorized modeling. Factors such

as the presence of bicycle parking and workplace showers and lockers may be just as important

as facility and network design factors in determining the decision to walk and particularly to

bicycle. Methods should be investigated for collecting data on these factors; describing them in a

way in which they can be included in travel models; and verifying the relationship of the

identified factors with levels of non-motorized travel.

Modeling behavioral change in multiple stages. Methods and data requirements for modeling

bicycle use in multiple stages should be investigated. Multi-stage behavior models may improve

forecasting efforts because the individual must first decide to even consider bicycling or walking

as a viable travel option. Only when bicycling or walking is regarded as a viable option does the

question of whether to bicycle or walk for a particular trip become relevant. These methods

should be tested for improving the sensitivity and predictive power of travel models. The results

of research into attitudinal and perceptual factors, as well as modeling approaches from the

public health and market research areas, can inform this process.

Inclusion of recreational travel. To be useful for modeling non-motorized travel particularly on

separate facilities, travel models will need to be capable of modeling recreational as well as

utilitarian travel. Advances in activity-based modeling, which looks at personal and household

activity patterns rather than simply trip-making patterns, may be useful in this effort. Research

and methods from the public health arena are also relevant to modeling recreational travel.