TECHBRIEF

Developing Crash
Modification Factors forSeparated Bicycle Lanes
FHWA Publication No.: FHWA-HRT-23-025
FHWA Contact: Ann Do, HRSO-30, ORCID: 0000-0001-9932-7422,(202) 493-3319, ann.do@dot.gov
This document is a technical summary of the Federal Highway
Administration report Developing Crash Modification Factors (CMFs)
for Separated Bicycle Lanes (forthcoming).

INTRODUCTION
In 2020, the United States had 938 bicycle fatalities due to roadway-related
crashes.(1) As bicycle use increases, transportation agencies are tasked withdetermining the optimal location and configuration for constructing on-roadbicycle facilities. Several bicycle lane configurations are available, including
the traditional bicycle lane, a buffered bicycle lane, and a separated bicycle
lane (SBL). Recently, many U.S. transportation agencies have started
implementing SBLs—also known as protected bicycle lanes—as a safetyenhancement. Figure 1 provides an example of an installation. SBLs provide
a bicycle lane that is separated from the adjacent motor vehicle lanes byincluding both a buffer and a vertical element between the motor vehiclelanes and the bicycle lane.
Figure 1. Photo. Example of an SBL on a two-way street.

Research, Development,and Technology
Turner-Fairbank Highway
Research Center6300 Georgetown Pike
McLean, VA 22101-2296
https://highways.dot.gov/research

Source: FHWA.

STUDY OBJECTIVE
The large number of bicycle-involved collisions
demonstrates the need to prioritize analyzing andenhancing the safety of bicyclists. In recent years,transportation agencies have constructed a varietyof bicycle lane configurations, including SBLs, yetthe associated influence that an SBL may have on
reducing crashes has yet to be determined. This Federal
Highway Administration (FHWA) project evaluatedthe safety effect for various on-street bicycle facilities.
The research focused on the feasibility of developing acrash modification factor (CMF) for the placement ofSBLs at roadway segment locations.

SITE IDENTIFICATIONThe criteria established for a site to be included in thisFHWA study are as follows:•The study region, at a minimum, must include
traditional bicycle lanes and SBLs.

•Data that can be used to estimate bicycle exposuremust be available.

•

Reported crash data are available for all
bicycle-involved crashes, and the data must be
multiyear data prior to 2020.

•The SBL configuration should be consistentthroughout the study region.

•Site features, such as roadway cross-sectional
characteristics and direction of travel, can beacquired from an online source.

First, the research team identified several candidate
locations based on published literature and a bicycle
database known as the Green Lane Project.(2) For thatproject, bicyclists were encouraged to document any

locations they encountered with SBLs. Even thougha database primarily developed by volunteers canbe error-prone, this resource provided information
about locations that were equipped with some sortof bicycle facility.
Based on the location information included in theGreen Lanes database and other published literature,
the research team was then able to identify several
potential locations to study. Following additionalsite screening via aerial photographs and jurisdiction
databases, the research team identified the following
promising study locations:•Cambridge, MA.

•San Francisco, CA.

•Seattle, WA.

In addition to these three cities, the team identified
two locations with smaller sample sizes that could be
used for CMF validation. These locations included
the following:•Austin, TX.

•Denver, CO.

DATA COLLECTION
The team compiled a list of data-related issues toconsider when assessing the safety of an SBL facility.
Table 1 identifies and defines these data requirements.
To develop a CMF, there must be a base condition(e.g., no bicycle lane or traditional bicycle lane) and atarget CMF (e.g., buffered bicycle lanes, SBLs). For
this reason, site selection included all bicycle facilities
within the study region. The research team populated thedata needed for each study site segment, as summarizedin table 1.

Table 1. Site information in database.
DATA NEEDTYPE OF DATACOLLECTED SITEDATA ELEMENTS

Location description

Location

Street name, city, and State (including
beginning and ending cross streets).

Starting latitude/longitudeLocation

Measured to extended curb
if at intersection.

2

Table 1. Site information in database. (Continued)
DATA NEEDTYPE OF DATACOLLECTED SITEDATA ELEMENTS

Ending latitude/longitudeLocation

Measured to extendedcurb if at intersection.

LengthLocation

Distance in miles.

Cross streets

Location

Between beginningand ending points.

Number of motor
vehicle lanes

Site characteristics

Total for both directions of travel.

Roadway facility type

Site characteristics

Urban arterial (two-way and one-way).
Urban collector (two-way and one-way).
Urban local street (two-way and one-way).

Intersection channelizationand traffic control

Site characteristics

Islands (traditional, bend-in,bend-out, turn lanes, etc.).
Motor vehicle and bicycle signalization.
Signage and pavement markings.

Lighting

Site characteristics

Street light presence,placement, and type.

Corridor information

Site characteristics

Speed limit; driveway density; number,type, and width of lanes; number
and type of intersections; street
network configuration; vehicle
parking restriction hours; presence
and location of sidewalks.

Direction of bicycle
traffic flow

Site characteristics

Approaching or departing
field of view.

Traffic operations formotor vehicle lanes

Site characteristics

One-way or two-way.

MedianSite characteristics

Physical separation of opposing
motor vehicle maneuvers.

3Table 1. Site information in database. (Continued)
DATA NEEDTYPE OF DATACOLLECTED SITEDATA ELEMENTS

Parking lane location

Site characteristics

Presence and location ofparking lane; located left and/or
right side of road if applicable.

Type of bicycle facility

Bicycle facility
characteristics

No dedicated lane, traditional
bicycle lane, buffered bicycle
lane, or SBL.

Year of construction

Bicycle facility
characteristics

SBLs constructed earlier than 2010
may not be suitable if exposure
information and crash data
are not available.

Width of buffer

Bicycle facility
characteristics

Locations with on-street parking
should be further assessed to determine
actual and effective buffer widths.

Vertical element

Bicycle facility
characteristics

Flexible posts, bollards, or light poles;
curb or raised median; landscaping
and planters; concrete (zebra/armadillo)bumps, buttons, and parking stops;
parked cars; grade; concrete
barrier, guardrail, or fence.

SBL facility configuration

Bicycle facility
characteristics

Right side one-way, or two-way.
Left side one-way, or two-way.
Middle two-way.

Bicycle facility
characteristics

Truck aprons, mixing zones,
or elevated SBLs atdriveways/crossings; green
markings; bicycle facility continuity
and operational consistency.

Supplemental data from agency
where sites are locatedTotal number of crashes
and number of fatal and injury
crashes (specific consideration
to who is injured: motor vehicle
drivers or passengers,
bicyclists, and pedestrians).

Unique SBL features

Crash data4

Table 1. Site information in database. (Continued)
TYPE OF DATACOLLECTED SITEDATA ELEMENTS

Supplemental data from agency
where sites are locatedAverage daily traffic or peak-hourvolumes for motor vehicles(passenger cars and trucks),
bicycles, and pedestrians; land
use and driveway types (as potential
volume surrogates). The increasein bicyclists due to the placementof SBL facilities is also an important
exposure consideration.

DATA NEEDExposure

The project team included variables that potentiallycould influence the safety performance of SBL facilities
and the bicycle exposure estimate. This supplemental
information provided valuable insights into theoperational characteristics of the facilities.

ANALYSIS APPROACHES

The research team used the initial site characteristic
information to develop exposure models for each site.
Because the available count data ranged from long-termhourly to short-term daily, the exposure model was
unique to each jurisdiction. The team also acquired
crash data for all of the study site locations and merged
them into a data file that ultimately included estimatedbicycle counts, site data information, and crash history.
The regression analysis focused on bicycle-involved
crashes. Note that data for 2020 and 2021 were excluded
from the CMF development efforts because those data
were atypical. The team could not locate a datasetthat included the date of implementation for each site,
so they elected to use a cross-sectional analysis withpropensity score weights.
The team developed regression equations to create
CMFs for the following combinations:•SBLs versus traditional bicycle lanes.

•SBLs versus buffered bicycle lanes.

•

SBLs versus either traditional bicycle lanes
or buffered bicycle lanes.

Ultimately, this combination of CMFs will enable a user
to assess the benefits of deploying an SBL when the basecondition is either a traditional bicycle lane or a bufferedbicycle lane. This CMF development activity focused on
Cambridge, San Francisco, and Seattle. Following CMF

development, the team then assessed if the CMFs
could be applied to Austin and Denver.

FINDINGS

The research team developed several regression
models for Cambridge, San Francisco, and Seattle(both independently and with a merged dataset).
Following that effort, the team used the data from
Austin and Denver to validate the models. The Austin
model performed quite well, but the Denver model
appeared to underpredict bicycle crashes. The researchteam conducted an equivalency test and could not ruleout that the CMFs for the different jurisdictions werenot equivalent, so the models passed the validation
test; however, the CMF for Denver conditions shouldbe applied cautiously. The team was not completely
sure about why these data differed, but Colorado has
different weather patterns (particularly in the winter)and has a greater elevation than the other study
sites. Table 2 summarizes the research finding.
The CMFs for SBLs show a clear trend that, with
their implementation, a transportation agency canexpect to see a reduction in bicycle crashes. The
individual city models suffer from smaller sample
sizes; however, they continue to result in estimatedcrash reductions consistent with those of larger sample
sizes. The combination of data from different cities
also results in similar trends and, for the most part,
greater statistical significance. For baseline conditions,the use of traditional bicycle lanes, bicycle lanes withbuffers but no vertical elements, or bicycle lanes witha combination of traditional and buffers resulted ingenerally similar trends. The SBL treatments that werethe most effective included flexible delineator posttreatments and treatments that were blended (most often
flexible delineator posts and other vertical elements).

5Table 2. CMFs for Bicycle Crashes (San Francisco, Seattle, and Cambridge).
CONDITION

CMF

ESTIMATE

STANDARDERROR ESTIMATEPROBABILITY VALUESIGNIFICANCE

Flush buffera

1.128

0.121

0.173

0.484

—

Flexibledelineator postsa

0.498-0.698

0.264

0.008

**

Flexibledelineator postsb

0.441-0.819

0.297

0.006

**

Flexibledelineator postsc

0.468-0.758

0.267

0.005

**

Blendeda

0.822-0.1960.252

0.437—Blendedb

0.729-0.316

0.300

0.292

—

Blendedc

0.774-0.256

0.263

0.331

—

Flexibledelineator postsd

0.605-0.5020.3180.114—

Flexible delineator
posts or blendeda

0.640-0.447

0.203

0.028

*

Flexibledelineator postsor blendedb

0.567-0.568

0.253

0.025

*

Flexibledelineator postsor blendedc

0.602-0.507

0.212

0.017

*

Base condition: Traditional bicycle lane.
Base condition: Flush buffered bicycle lane.
cBase condition: Traditional or flush buffered bicycle lane.
dBase condition: Blended vertical element.
*Statistically significant at the 0.05 level.
**Statistically significant at the 0.01 level.
***Statistically significant at the 0.001 level.
—Not statistically significant.
ab6

Table 3. Overview of significant variables in models.
SIGNIFICANCE LEVELBEFORE CONDITION

AFTER CONDITIONCMFSTANDARDERROR ESTIMATE

0.01

Traditional
bicycle lane.

SBL with flexible
delineator posts.

0.498

0.173

0.01

Flush bufferedbicycle lane.

SBL with flexible
delineator posts.

0.441

0.297

0.01

Traditional or flush
buffered bicycle lane.

SBL with flexible
delineator posts.

0.468

0.267

0.05

Traditional
bicycle lane.

SBL with blend of flexible
delineator posts andother vertical elements.

0.640

0.203

0.05

Flush bufferedbicycle lane.

SBL with blend of flexible
delineator posts andother vertical elements.

0.567

0.253

0.05

Traditional or flush
buffered bicycle lane.

SBL with blend of flexible
delineator posts andother vertical elements.

0.602

0.212

CONCLUSIONSIn addition to developing an SBL CMF for bicycle-involved
crashes, this research identified multiple statisticallysignificant factors that could influence bicycle operations
and safety. In general, the analysis indicates the following:•Bicycle crashes are more likely to occur at locations
with mixed land use and less likely to occur atlocations with industrial or public land use.

•Bicycle crashes occur less frequently at locations withmore motor vehicle lanes. This observation could be
an indicator of route choice by the bicyclist.

•Bicycle crashes occur less frequently at locations
where parking is not permitted in at least one direction
compared to locations without parking restrictions.

The report documents exposure models andregion-specific regression estimates. Collectively,
the research yielded the summary of CMFs,
based on a combined model, as shown in table 3.
The use of flexible delineator posts consistently
resulted in a decrease in total crashes. When
flexible delineator posts are blended with anothertreatment, the crash reduction remains significant.

7REFERENCES
1. National Highway Safety Administration. n.d.
“Bicycle Safety” (webpage). https://www.nhtsa.
gov/road-safety/bicycle-safety, last accessed
October 18, 2022.

2. Monsere, C., J. Dill, N. McNeil, K. Clifton, N. Foster,T. Goddard, M. Berkow, J. Gilpin, K. Voros, D. vanHengel, and J. Parks. 2014. Lessons from the Green
Lanes: Evaluating Protected Bike Lanes in the U.S.
Report No. NITC-RR-583. Portland, OR: National
Institute for Transportation and Communities.
https://ppms.trec.pdx.edu/media/project_files/NITCRR-583_Executive_SummaryProtectedLanes.pdf, lastaccessed October 18, 2022.

Researchers—This study was conducted by Principal Investigator Karen Dixon (ORCID: 0000-0002-8431-9304),
Raul Avelar-Moran (ORCID: 0000-0002-3962-1758), and Maryam Mousavi Seyedeh (ORCID: 0000-0001-81884466), of Texas A&M Transportation Institute under Contract No. HRDS30180007PRPR-DTFH6116D00039.
Distribution—This TechBrief is being distributed according to a standard distribution. Direct distribution is being
made to the Divisions and Resource Center.
Availability—This TechBrief may be obtained at https://highways.dot.gov/research.
Key Words—Separated bicycle lane, protected bicycle lane, crash modification factor
Notice—This document is disseminated under the sponsorship of the U.S. Department of Transportation in
the interest of information exchange. The U.S. Government assumes no liability for the use of the information
contained in this document. The U.S. Government does not endorse products or manufacturers. Trademarks
or manufacturers’ names appear in this TechBrief only because they are considered essential to the objectiveof the document.
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FEBRUARY 2023

Recommended citation: Federal Highway Administration,Developing Crash Modification Factors for Separated
Bicycle Lanes (Washington, DC: 2023)
https://doi.org/10.21949/1521970

FHWA-HRT-23-025
HRSO-30/02-23(WEB)E8