using bicycle level of service to assess community-wide bikeability

Lowry, Callister, Gresham, and Moore 1

Using Bicycle Level of Service to Assess Community-wide Bikeability

Michael B. Lowry, PhD*

Assistant Professor

Department of Civil Engineering

University of Idaho

Box 441022

Moscow, ID 83844

Phone: (208) 885-0139, Fax: (208) 885-6608

[email protected]

Daniel Callister

Research Assistant

Bioregional Planning and Community Design

University of Idaho

Box 441022

Moscow, ID 83844

Phone: (208) 885-0139, Fax: (208) 885-6608

[email protected]

Maureen Gresham, AICP

State Bicycle and Pedestrian Coordinator

Idaho Transportation Department

PO Box 7129

Boise ID 83707

Phone: (208) 334-8272, Fax: (208) 334-4432

[email protected]

Brandon Moore

PhD Candidate

Department of Geography

University of Idaho

Box 441022

Moscow, ID 83844

Phone: (208) 885-0139, Fax: (208) 885-6608

[email protected]

*corresponding author

Revised text length: 5,837 words + 3 figures and 3 tables equal to 1,500 words = 7,337 words.

Revised for publication in Transportation Research Record, Journal of the Transportation

Research Board and presentation at the 91st Annual Meeting.

TRB 2012 Annual Meeting Paper revised from original submittal.


Abstract

This paper introduces a novel method to assess the quality of bicycle travel throughout a

community. At the outset, the paper distinguishes between “bicycle suitability” as an assessment

of the perceived comfort and safety of a linear section of bikeway and “bikeability” as an

assessment of an entire bikeway-network in terms of access to important destinations. The focus

of this paper is the latter. A review of the literature reveals that most of the previous work

concerning the quality of bicycle travel deals with bicycle suitability, not bikeability. There is,

however, ample research concerning the related concept of “accessibility.” The proposed

calculation for bikeability builds upon a common accessibility equation and is demonstrated

through a case study involving three different capital investment scenarios. Engineers and

planners can follow a similar procedure to help prioritize improvement projects or to

communicate the benefits of new projects. The analysis uses a geographic information system.

Keywords: bicycle level of service, accessibility, bikeability, scenario analysis



Using Bicycle Level of Service to Assess Community-wide Bikeability

INTRODUCTION

There is intense interest across the country to assess and improve the quality of bicycle travel.

State departments of transportation and local agencies have turned to bicycle travel, along with

public transit and walking, to address concerns about congestion, health, and the environment.

Consequently, there is tremendous need for performance measures that can assess the comfort

and convenience of bicycling. Decision-makers need to know where money should be allocated

for capital improvements, they need to know how completed projects have benefited the

community, and they need to be able to communicate the improvements expected from proposed

projects.

Research to assess the comfort and convenience of bicycle travel is actively moving

forward on many fronts. Despite the progress, there seems to be inconsistent terminology in the

literature. Terms, such as “suitability” and “bikeability” are used differently by some authors and

interchangeably by others. We propose the following definitions:

bicycle suitability – an assessment of the perceived comfort and safety of a linear section of

bikeway (the term bikeway includes shared-use paths and any roadway where bicycle travel

is permitted).

bikeabilty – an assessment of an entire bikeway-network in terms of the ability and

perceived comfort and convenience to access important destinations.

bicycle friendliness – an assessment of a community for various aspects of bicycle travel,

including bikeability, laws and policies to promote safety, education efforts to encourage

bicycling, and the general acceptance of bicycling throughout the community.

The distinction between the three assessments is more significant than it might seem at

first glance. For example, a street might be suitable for bicycle travel, but may not lead to useful

destinations. If there are very few destinations that can be reasonably reached, then the network

is not bikeable, even if there are links in the network with good bicycle suitability. Likewise, a

community might be bikeable, but not bicycle friendly; for example, there may be animosity

toward bicyclists or a lack of laws to protect and encourage bicycling (The same three-level

hierarchy can be applied to assessing the comfort and convenience of pedestrian travel: (i) a

street can be suitable for walking, (ii) a community can be walkable, and (iii) a community can

be pedestrian friendly.).

Most research on the comfort and convenience of bicycle travel has focused on some

aspect of bicycle suitability, including the work recently released in the latest edition of the

Highway Capacity Manual (HCM). The HCM is a standard reference for engineers and planners

to calculate the “level of service” for streets, highways, freeways, and intersections. Historically,

level of service analysis has focused on automobile travel, but the new edition of the HCM has

substantial guidance for calculating transit, pedestrian, and bicycle level of service (1).

This paper demonstrates how the HCM’s bicycle level of service (BLOS) can be

calculated across an entire community with a geographic information system (GIS) and the

results can then be used to calculate bikeability. The new measure for bikeability introduced in

this paper is a novel modification of an equation commonly used to measure accessibility.

Accessibility is a well-studied concept typically defined as the ease of reaching important

destinations (2). Bikeability is more specific in that it is an assessment of the comfort and



convenience of traveling by bicycle and takes into account the suitability of the bikeways

traversed.

The next section of this paper provides a literature review for bicycle suitability,

bikeability, and bicycle friendliness. This is followed by an explanation of the new bikeability

method. The explanation includes a discussion about calculating BLOS across a community. The

usefulness of calculating bikeability is demonstrated through a case study involving three

scenarios. The scenarios include 1) adding new bike lanes to existing streets, 2) constructing new

shared use pathways, and 3) adding both new bike lanes and shared use pathways. The final

section summarizes with conclusions and offers suggestions for future research.

LITERATURE REVIEW

Bicycle Suitability

There are numerous methods for assessing bicycle suitability. Table 1 lists several methods

frequently cited in the literature. Each method attempts to provide a score (i.e. rating) of the

perceived comfort and safety of a linear section of bikeway. All methods essentially follow the

same general format: various attributes of the bikeway are given a certain number of points and

the points are combined to calculate a score that categorizes a section of bikeway on a spectrum

from desirable to undesirable. The choice of attributes, the point system, and how the points are

combined distinguish the different methods. The authors of each method usually provide theory

and empirical findings to support the inclusion or exclusion of certain attributes and the point

system.

TABLE 1 Common Bicycle Suitability Methods

Name of Method Acronym Reference Reference Date

Bicycle Safety Index Rating BSIR Davis (3) 1987

Bicycle Stress Level BSL Sorton and Walsh (4) 1994

Road Condition Index RCI Epperson (5) 1994

Interaction Hazard Score IHS Landis (6) 1994

Bicycle Suitability Rating BSR Davis (7) 1995

Bicycle Level of Service (Botma) BLOS Botma (8) 1995

Bicycle Level of Service (Dixon) BLOS Dixon (9) 1996

Bicycle Suitability Score BSS Turner et al (10) 1997

Bicycle Compatibility Index BCI Harkey et al (11) 1998

Bicycle Suitability Assessment BSA Emery and Crump (12) 2003

Bicycle Level of Service (Jensen) BLOS Jensen (13) 2007

Bicycle Level of Service (Petritsch et al) BLOS Petritsch et al (14) 2007

Bicycle Level of Service (HCM) BLOS HCM (1) 2011

Table 1 shows research concerning bicycle suitability has remained fairly constant since

the seminal work by Davis (3). Many authors have used the name “bicycle level of service.”

Some authors have worked to improve earlier methods and others started from scratch to develop

distinctly different methods.



This paper focuses on assessing an entire network of bikeways in terms of the suitability

of the individual links that make up the network. Any bicycle suitability method could be used in

our proposed assessment. For this paper, we chose to use the HCM’s bicycle level of service

(BLOS) (1) because it is the most recent method and is expected to become the most common in

practice. The BLOS method is considered state-of-the-art. It builds on dozens of earlier studies

and was developed through a massive effort to improve the HCM for non-motorized travel. The

details of the development can be found in NCHRP Report 616 (15). Presumably, engineers and

planners across the country will become increasingly familiar with the BLOS method as they

utilize the ubiquitous HCM. Consequently, although any of the methods shown in Table 1 could

be used in our bikeability assessment, we take time here to briefly explain the HCM method.

The HCM provides a procedure for calculating BLOS for a link (i.e. a linear section of

roadway between intersections) and for an intersection. The link BLOS and intersection BLOS

can be combined to obtain the BLOS for what is referred to as a segment (i.e. a combination of

one link and one intersection). A group of segments can be combined to obtain the BLOS for

what is referred to as a facility (i.e. a set of contiguous segments). The HCM states that there are

known weaknesses and limitations with the equations for intersection and segment LOS; and

therefore, recommends focusing large-area analysis on “link-based evaluation” (1, page 17-56).

The calculation for link BLOS is based on ten attributes: 1) width of outside lane, 2)

width of bike lane, 3) width of shoulder, 4) proportion of occupied on-street parking, 5) vehicle

traffic volume, 6) vehicle speeds, 7) percent heavy vehicles, 8) pavement condition, 9) presence

of curb, and 10) number of through lanes. The ten attributes are combined in an elaborate non-

linear equation to produce a numeric score to represent bicyclist “perceptions” of comfort and

safety (It is important to note that perceptions of safety do not necessarily correlate to actual

safety). The equation is described in detail in the HCM and the interested reader is encouraged to

go directly to the source for more information. For this paper, it is instructive to see the essence

of the equation. Essentially, the ten attributes are weighted as “adjustment factors” and combined

as follows:

(1)

where Fw is the width adjustment factor, Fv is the vehicle volume adjustment factor, FS is the

vehicle speed adjustment factor, Fp is the pavement condition adjustment factor. Equation (1)

produces a numeric score, which is then used to determine a letter grade as follows:

< 2.00 is BLOS “A”,

2.00 – 2.75 is BLOS “B”,

2.75 – 3.50 is BLOS “C”,

3.50 – 4.25 is BLOS “D”,

4.25 – 5.00 is BLOS “E”, and

>5.00 is BLOS “F”.

Bikeability

As defined in this paper, bikeability refers to the comfort and convenience of an entire bikeway-

network for accessing important destinations. Unlike suitability, there are only a few examples of

bikeability assessment in the literature. On the other hand, the more general concept of



accessibility has a long history and substantial literature. The most widely used measures of

accessibility are based on Hansen’s (16) early model which has the form:

( ) (2)

where Ai is the accessibility of location i, Ej represents the intensity of activity (number of

employees, shopping square footage, etc) at destination j, and f(rij) is an impedance function for

, which represents the travel time, distance, or generalized cost from i to j. Various impedance

functions (also called distance decay functions) have been proposed. The most common is

( ) (3)

which results in exponentially lower values of accessibility for greater distances between i and j.

The parameter β determines how strongly distance impedes travel and is often obtained through

travel surveys. Typical values range between 0.5 and 2.0 depending on trip purpose, mode, and

other travel characteristics. Other common functional forms of ( ) are the power function,

, and the cumulative opportunities function where ( ) , if is less than a particular

threshold, e.g. 15 minutes, otherwise ( ) .

Literature reviews on different formulations and other issues concerning accessibility are

provided by Song (17), Handy and Niemeier (18), Handy and Clifton (19), and Iacono et al (2).

Most of the literature concerns automobile travel.

One exception is the research by Iacono et al (2). Their research team used household

surveys to estimate the parameter β in equation (3) in what they suggest is the first empirical

study of its kind in the literature. They showed that the attractiveness of a destination greatly

diminishes when the destination is more than a mile away. They calculated accessibility for the

parcels of a community in Minnesota. However, their study did not incorporate bicycle

suitability (i.e. the perceived comfort and safety of a linear section of bikeway), so it falls short

in terms of measuring bikeability.

Likewise, McNeil (20) calculates accessibility for bicyclists, but does not explicitly

incorporate suitability in his equation (he does consider that bike lanes and pathways allow

bicyclists to travel longer distances). His method assigns points to various destination types (e.g.

grocery store, movie theaters, etc.) and calculates a score by summing the points within a 20

minute bike ride. McNeil’s method is similar to the popular Walk Score®, which calculates a

score out of 100 for a given address based on the number of amenities within walking distance.

Although not stated by McNeil, his method and the Walk Score®

method use a cumulative

opportunities impedance function with equation (2).

Klobucar and Fricker (21) use suitability to assess a bikeway network. They multiply the

length of a link by its bicycle suitability score and then route a fabricated volume of bicyclists

across the network assuming bicyclists choose their route so as to minimize link length times

suitability score. They calculate statistics about the frequency of link usage under different

improvement scenarios. Their analysis does not attempt to make a statement about bikeability.

The Pedestrian and Bicycle Information Center (PBIC) include suitability in their method

to assess access to destinations (22). Their method is appropriately called the Bikeability

Checklist. It is a simple two page form for citizens to assess their community. The user is asked

to take a bike trip to a destination and answer a series of questions about the comfort and



convenience of the trip. The Bikeability Checklist is a useful public involvement tool; however,

it is not conducive to systematic assessment of large areas and numerous destinations.

Bicycle Friendliness

Bicycle friendliness is an assessment of various aspects of bicycle travel, including bikeability,

laws and policies to promote safety, education efforts to encourage bicycling, and the general

acceptance of bicycling throughout the community. Often the assessment of bicycle friendliness

is combined with efforts to determine the level of bicycling in the community.

A well-known bicycle friendliness assessment was developed by the League of American

Bicyclists (LAB) (23). Since 2003, LAB has assessed 158 communities across the country for

bicycle friendliness. The LAB assessment is based on achievement in five categories:

engineering, education, encouragement, enforcement, and evaluation. Communities must apply

and pay a fee to be assessed. Participant communities are awarded a designation of platinum,

gold, silver, or bronze. LAB also has a state level assessment based on five categories:

legislation, policies and programs, infrastructure, education, enforcement, and evaluation.

The Alliance for Biking and Walking assesses bicycle friendliness every two years for all

50 states and select communities (24). The results are published in a biennial benchmarking

report.

A number of state and city organizations have devised their own bicycle friendliness

assessment methods (sometimes called “report cards”) (24). For example, Oregon’s Bicycle

Transportation Alliance (BTA) developed the Bike-Friendly Report Card to compare cities

throughout Oregon and "grade" them on their bicycle-friendliness (25).

There are a number of international examples for assessing bicycle friendliness. One

example is the Bicycle Policy Audit (BYPAD) funded by the European Union. BYPAD has

been used to assess more than 100 European cities in 21 countries (26).

Any bicycle friendliness assessment, including those mentioned above, could be

enhanced with the proposed bikeability measure described in the next section.

METHOD

Calculating BLOS Across the Community

The first step is to calculate BLOS for all bikeways across the community (a bikeway is any

roadway where bicycle travel is permitted regardless of the presence of a bike lane). Many

communities already maintain some of the data for the ten attributes needed to calculate BLOS.

As mentioned earlier, the ten attributes are 1) width of outside lane, 2) width of bike lane, 3)

width of shoulder, 4) proportion of occupied on-street parking, 5) vehicle traffic volume, 6)

vehicle speeds, 7) percent heavy vehicles, 8) pavement condition, 9) presence of curb, and 10)

number of through lanes (see the HCM (1) for details).

For the case study community, complete data were not available for all streets. In

particular, data were lacking for vehicle traffic volumes, the proportion of occupied on-street

parking, percent heavy vehicles, and pavement condition. It was determined that some data

would be collected in the field; and some would need to be estimated.

Sensitivity analysis was conducted to determine which values should be estimated more

accurately. First data was collected at select locations throughout the city. Table 2 shows the

range of values collected. Next, BLOS was calculated with hundreds of perturbations from the



“typical” input values to construct sensitivity tables and sensitivity spiderplots. The analysis

helped determine which values should be estimated more accurately for the case study. For

example, for arterials it was discovered that accurate estimates for the proportion of on-street

parking are critical because the resulting BLOS can range from “A” to “F.” Likewise, for

arterials it is critical to get an accurate percent heavy of vehicles. For collectors, special attention

should be made when estimating vehicle volumes. For local streets, nearly all perturbations

produced BLOS “A” or “B”; suggesting that accuracy for the geometries isn’t as critical as long

as the vehicle volumes are truly low. Indeed, the HCM advises caution when applying the

calculation to local streets because the method was developed primarily for higher vehicle

volumes like those expected on arterials and collectors.

TABLE 2 Range of BLOS Input Values used for Sensitivity Analysis

HCM Primary Arterial Minor Arterial Collector Local Street

Attributea Example

b Low Typical

c High Low Typical High Low Typical High Low Typical High

1) Wol 12 12 12 15 10 12 15 8 12 15 8 12 15

2) Wbl 5 0 0 7 0 0 5 0 5 5 0 0 5

3) Wos 9.5 0 12 15 0 10.5 15 0 10.5 15 0 8.5 15

4) ppk 0.2 0.1 0.4 0.95 0.2 0.6 0.95 0.4 0.6 0.95 0.3 0.4 0.8

5) v 940 300 600 1500 100 400 1000 100 250 500 20 50 200

6) SR 33 25 35 55 25 35 45 25 30 40 25 25 35

7) PHV 0.08 0 0.06 0.1 0 0.01 0.1 0 0 0.01 0 0 0.05

8) Pc 2 2.5 3 4.8 2.5 3 4.8 2.5 3 4.8 2.5 3 4.8

9) c 1 0 1 1 0 1 1 0 1 1 0 1 1

10) Nth 2 1 1 4 1 1 2 1 1 1 1 1 1 a 1) width of outside lane, 2) width of bike lane, 3) width of shoulder, 4) occupied on-street parking, 5) vehicle traffic volume,

6) vehicle speeds, 7) percent heavy vehicles, 8) pavement condition, 9) presence of curb, and 10) number of through lanes. b Example values provided in the 2010 HCM (1). c Typical values were the starting point for the sensitivity analysis.

Figure 1 shows the BLOS results for the case study community. (Local streets are not

shown in order to simplify the figure and because the bikeability calculation described in the

next section does not include local streets. The majority of local streets exhibit a BLOS “A” or

“B”, anyway; exceptions are those with atypical high vehicle volumes.) The streets with BLOS

“F” in Figure 1 are arterials without bike lanes. Interestingly, it was found that collectors exhibit

the most variation in BLOS, depending on vehicle volumes, presence of bike lane, and the width

of the outside shoulder.

We note here again that the HCM method does not need to be used in the proposed

bikeability assessment described in the next section. Any suitability method could be used. The

key is to determine the bicycle suitability of the various links of a bikeway-network. The

advantage of the formal methods listed in Table 1, is that they provide a consistent and

systematic approach. One option a community might choose is to use the BLOS calculation as a

starting point and then customize the suitability ratings. Krykewycz et al. (27) demonstrate how

the internet can be used to solicit public involvement to refine suitability scores.



FIGURE 1 Status quo BLOS for the case study bikeway network (local streets not shown).

Calculating Bikeability

Once suitability has been determined for an entire bikeway network and important destinations

have been identified, then bikeability can be calculated. This section first explains the rationale

behind the calculation and then presents the formal equation.

The calculation is based on Hansen’s model of accessibility, i.e. equation (2), with an

exponential impedance function, i.e. equation (3). The parameter β could be estimated using

travel surveys, but for the case study we use β =1 which is a reasonable assumption based on the

work by Iacono et al (2).

Any set of destinations could be used to assess bikeability; for example, an analyst could

assess the bikeability to public parks. For this paper, bikeability was assessed for all commercial

destinations (which were obtained from the community’s parcel data). Likewise, level of activity

could be represented in a variety of ways. For this paper, we used building square footage to

represent level of activity. We normalize the results by dividing by total activity level as

recommended by Ingram (28).

The calculation finds the shortest routes between zone i and every destination j. To find

the shortest routes, we follow the example of Klobucar and Fricker (21) and minimize link

suitability multiplied by link distance. In reality, bicyclists might choose routes for other reasons

besides link suitability and distance. For example they may choose to avoid hills, minimize turns,

or to maximize beautiful vistas. There is a growing body of literature about bicycle route choice

(29). For our purposes, it suffices to assume that bicycle suitability captures the majority of

bicyclists’ preferences.

In summary, bikeability is calculated for a zone by multiplying the square footage for

each destination by an exponentially discounted distance times suitability and then summing

across all destinations and dividing by total square footage. This can be written formally as

∑

∑

∑ (4)

where Bi is the bikeability for zone i, Ej represents the intensity of activity at destination j, and

∑ equals the sum of suitability times distance for every link from zone i to

destination j. The numerator and denominator are summed across j and produce a decimal

between 0 and 1.

BLOS

BLOS

BLOS_Order

A

YYYYYYYYYYYYYYYYYYYYYYYYY B

DDDDDDDDDDDDDDDDDDDDDDDDD C

GGGGGGGGGGGGGGGGGGGGGGGGG D

E

F

BLOS

BLOS

BLOS_Order

A

YYYYYYYYYYYYYYYYYYYYYYYYY B

DDDDDDDDDDDDDDDDDDDDDDDDD C

GGGGGGGGGGGGGGGGGGGGGGGGG D

E

F



The result of equation (4) is that zones near destinations have high bikeability and zones

that are able to access destinations via a good BLOS route also have high bikeability (Intrazone

trips have a distance of zero, and therefore contribute to bikeability in an amount equal to their

level of activity). To illustrate, the results for an imaginary community with two activity centers

are shown in Figure 2. Darker grey indicates better bikeability. The underlying bikeway network

for both Figures 2a and 2b is a grid; however, the network in Figure 2a is comprised only with

links exhibiting BLOS “E” or “F”, while Figure 2b has a few links with BLOS “A.” These high

BLOS links might constitute a shared use pathway or a bicycle “boulevard.”

Figure 2a shows that zones near the activity centers exhibit high bikeability. Figure 2b

shows that zones that are able to access the activity centers via the added shared use pathway

also exhibit high bikeability. This added benefit would not be apparent if the equation did not

incorporate bicycle suitability. In other words, the standard formulation of accessibility cannot

demonstrate the benefits associated with capital investments that improve bicycle suitability. In

fact, the calculation even captures the fact that zones farther from the bike path receive less

benefit (in terms of bikeability).

2a) without bike path, all streets BLOS “E” or “F”

2b) with an added BLOS “A” bike path

FIGURE 2 Bikeability for an imaginary community with two activity centers and a rectilinear grid

of streets that have BLOS “E” or “F.”

Grid_twopoints

12

Ai

0.01 - 0.10

0.11 - 0.20

0.21 - 0.30

0.31 - 0.40

Bikeability

Ai

0.00 - 0.05

0.06 - 0.10

0.11 - 0.15

0.16 - 0.20

0.21 - 0.25

0.26 - 0.30

Destination

Destination

scenario2_both

Existss

Existing bike lane or pathway

Proposed bike lane or pathway

Legend

Destinations

Legend

newbike

Added bike path



GIS Tools to Calculate BLOS and Bikeability

A set of GIS tools were created for calculating BLOS and bikeability. The tools are designed for

ArcGIS® 10 using open-source python code and standard licensing. The tool to calculate BLOS

follows the procedure outlined in the HCM (1) for link BLOS. The user provides a shapefile of

bikeway links (streets and paths), with the ten attributes for each link required for the calculation.

The output is two new attributes: the numeric score and BLOS letter grade.

The tool to calculate bikeability requires a bikeway network with a numeric BLOS score

for each link. It is recommended to focus on major bikeways. For the case study, the major

bikeways only arterials, collectors, and shared use paths were considered part of the major

bikeway-network. The second input is a shapefile with important destinations as points or

polygons. The tool can treat all destinations equally, or distinguish destinations by an attribute

that represents activity level, such as square footage, number of employees, annual retail sales,

etc. The third input is the dimensions for the output zones for which bikeability will be

calculated. Alternatively, users can provide predefined zones, such as land use parcels or traffic

analysis zones (TAZ). The output is a bikeability score for every zone.

CASE STUDY RESULTS

A case study was conducted for the community of Moscow, Idaho (population ~25,000).

Moscow is home to the University of Idaho and is located just nine miles from Washington State

University in Pullman, Washington (a shared use path connects the two campuses). Like most

university towns, bicycle ridership is higher than usual. Recently, a community survey revealed

strong citizen desire to improve the bikeway network. The transportation commission and a

special task force appointed by the mayor have sought to identify and prioritize improvements.

Bikeability was calculated for a number of improvement scenarios. This paper will

discuss three scenarios to demonstrate bikeability results. In the first scenario, various streets

would be restriped with vehicle lanes or shoulders that are more narrow in order to accommodate

new on-street bike lanes. In the second scenario, nearly four miles of new off-street shared use

pathways would be constructed. The third scenario combines the new on-street bike lanes and

off-street shared use path ways, plus it includes land use rezoning to allow new commercial

development in the northeast part of town that would effectively create a new activity center.

BLOS was calculated for all arterials and collectors.

Figure 3 compares bikeability for the status quo and three improvement scenarios (the

analysis covered an area slightly wider than is shown). The first scenario improves bikeability

across a large residential area where new east-west bike lanes would be painted. The better

bikeability is depicted by more dark grey zones especially to the immediate right of downtown.

The second scenario improves bikeability for the zones near the new shared use pathways. One

new shared use pathway is shown in the upper left corner and another in the lower right. These

and other new shared use pathways (outside the view shown in the figure) would increase

network connectivity, and therefore effectively reduce travel distances. Part of the better

bikeability can be attributed to the improved connectivity.



3a) status quo

3b) scenario 1: new on-street bike lanes

3c) scenario 2: new off-street shared use pathways

3d) scenario 3: land use change and bikeway improvements

FIGURE 3 Scenario comparisons of bikeability.

0 1Miles.

Bikeability

Ai

0.00 - 0.05

0.06 - 0.10

0.11 - 0.15

0.16 - 0.20

0.21 - 0.25

0.26 - 0.30

Destination

Destination

scenario2_both

Existss



Bikeability

Ai

0.00 - 0.05

0.06 - 0.10

0.11 - 0.15

0.16 - 0.20

0.21 - 0.25

0.26 - 0.30

Destination

Destination

scenario2_both

Existss



Bikeability

Ai

0.00 - 0.05

0.06 - 0.10

0.11 - 0.15

0.16 - 0.20

0.21 - 0.25

0.26 - 0.30

Destination

Destination

scenario2_both

Existss



Legend

Destinations



The third scenario has the greatest improvement in bikeability compared with the status

quo. This is partly due to the new bike lanes and paths, and partly due to the new destinations

expected after a proposed land use rezoning (the new destinations are in the upper right corner).

Presumably, the new destinations would provide some bicyclists a closer alternative for some

activities (e.g. work, recreation, or shopping).

Mapping bikeability helps to visualize deficiencies and the benefits from improvements.

Other measures of bikeability, in addition to equation (3), might be devised to depict other

aspects of a bikeway system. For example, a simple tally of BLOS for the community is

insightful. Table 3 compares the percent of bikeway miles for each BLOS letter grade. All three

improvement scenarios exhibit better overall BLOS. It is apparent that the new on-street bike

lanes upgrade many streets from BLOS “C” and “D” to BLOS “B.” Likewise, the new off-street

pathways would add a few miles of BLOS “A.” Table 3 also shows that the average bikeability is

consistently higher for each scenario.

TABLE 3 Scenario Comparison of BLOS Mileage

BLOS (percent of bikeway milesa) Average

Bikeability Scenario A B C D E F

0) status quo 32 19 17 22 5 5 0.06

1) new on-street bike lanes 37 25 13 15 5 5 0.10

2) new off-street pathways 41 16 15 19 5 4 0.11

3) land use change and bikeway improvements 45 23 12 12 4 4 0.12 a Primary arterials, minor arterials, collectors, and pathways. 43 miles for scenarios 0 and 1; 47 miles for scenarios 2 and 3.

CONCLUSION

This paper demonstrates a new method for measuring bikeability that successfully incorporates

bicycle suitability in the equation. The advantage of this approach is the ability to capture the

benefits from capital investments intended to improve bicycle suitability. At the same time, the

new method distinguishes a bikeway-network with great suitability and a bikeway-network with

great suitability and access to important destinations.

The new method was demonstrated through an example using scenario analysis for

projects recently proposed in the case study community. Engineers and planners could follow a

similar procedure to help prioritize projects or to communicate the benefits of new projects. In

the case study example there were three scenarios. The first scenario would add new bike lanes

to the community; the second scenario would add new shared use path ways; and the third

scenario would add bike lanes and paths and also allow for land use change through rezoning.

Bikeability is improved for all three scenarios. One reason why the paths produce better

bikeability than bike lanes is because the new bike paths would increase connectivity in the

network, thus effectively reducing travel distances. Likewise, the land use change in the third

scenario would provide some bicyclists a closer alternative to some daily activities.

Future research could focus on creating other measures of bikeability that could be

combined with this one in order to capture a more robust assessment of a bikeway network.

Future research could also explore using other bicycle suitability methods or including

intersection suitability or perhaps including some measure of “suitability continuity.” For



example, the Florida DOT Quality/Level of Service Handbook offers a somewhat simpler

method to calculate BLOS and a method to aggregate links so as to capture “gaps” in suitability

(30). Likewise, our approach could be improved by incorporating the latest theory on bicyclist

route choice.

ACKNOWLEDGEMENT

This project was funded in part by a grant from the Idaho Transportation Department.

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using bicycle level of service to assess community-wide bikeability

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