Development of Crash 

Modification Factors for Bicycle 

Treatments at Intersections

FHWA Publication No.: FHWA-HRT-23-031

FHWA Contact: Carol Tan (ORCID: 0000-0002-0549-9782), 

Safety Data and Analysis Team, (202) 493-3315, 

Carol.Tan@dot.gov

This document is a technical summary of the Federal 

Highway Administration report Development of Crash 

Modification Factors for Bicycle Treatments at Intersections

(FHWA-HRT-23-020).

INTRODUCTION

The Federal Highway Administration’s (FHWA) Development of Crash 

Modification Factors (DCMF) Program was established in 2012 to 

address highway safety research needs for evaluating new and innovative 

safety strategies by developing reliable quantitative estimates of their 

effectiveness in reducing crashes (FHWA 2022). Forty-one State 

departments of transportation (DOTs) provided technical feedback on 

safety improvements to the DCMF Program and implemented new 

safety improvements to facilitate evaluations. These States are members 

of the Evaluation of Low-Cost Safety Improvements Pooled Fund 

Study (ELCSI-PFS), which functions under the DCMF Program.

This project evaluated bicycle treatments at urban intersections. The 

ELCSI-PFS Technical Advisory Committee selected this evaluation as 

one of the priorities within its purview.

Study Objective

This evaluation assessed the potential safety improvements of various 

intersection geometric and traffic control device treatments to reduce fatal 

and injury vehicular crashes and bicycle-specific crashes. The intent was to 

develop crash modification factors (CMFs) and benefit–cost (B/C) ratios for 

each safety improvement. Practitioners can use the CMFs and B/C ratios for 

decisionmaking in the project development and safety planning processes. 

Background

Intersections are particularly dangerous areas for bicyclists. According 

to the National Association of City Transportation Officials (NACTO), 

in 2017, 43 percent of bicyclist fatalities in urban areas occurred at 

intersections, based on their analysis of the National Highway Traffic Safety 

Administration’s Fatality Analysis Reporting System data (NACTO 2019). 

Changes to traffic control devices, channelization, parking regulations, or 

roadway design could potentially reduce intersection crashes.

TECHBRIEF

Research, Development, 

and Technology

Turner-Fairbank Highway 

Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

https://highways.dot.gov/research
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METHODOLOGY

Safety studies are often limited to evaluations of 

observational data since randomization is not possible 

and randomized comparison group experiments are not 

feasible. Good observational studies rely on data from 

both treated and nontreated sites in a manner consistent 

with control group experiments. After reviewing potential 

data sources, the research team deemed obtaining 

before–after data from multiple jurisdictions on the 

installation of bicycle lanes infeasible. Therefore, the 

research team developed a database for cross-sectional 

analysis. To incorporate comparison sites, team members 

adopted the use of propensity score (PS) weighting 

methods to minimize imbalances between covariates to 

make the sites more comparable. Under this framework, 

the PS of the treatment sites and their corresponding 

control sites are estimated and compared. The PS is a 

metric of similarity between covariates in the two study 

groups (i.e., sites treated and comparison sites). 

Balance in the comparison is achieved by defining 

appropriate weights so that the groups reflect their 

representation in an underlying target population of sites. 

The target population was set to be the overlap between 

the treated and comparison populations, as proposed by 

Li, Morgan, and Zaslavsky (2018). Under this scheme, 

the target population is the set of all sites that have 

comparable chances to be in either the treatment group 

or the comparison group. This approach effectively curbs 

the undue influence of control sites that are unlikely to 

be candidates for the treatment and of treated sites with 

unusual characteristics so that no comparable control sites 

are represented in the data.

The empirical analyses were conducted using the 

statistical methods appropriate to the characteristics 

of the assembled datasets. The research team used 

appropriate Poisson-lognormal and negative binomial 

generalized linear models for the different datasets and 

response variables. Best fit models were estimated, 

including the key variables (the various intersection 

treatments under evaluation) and other influential 

covariates. CMFs and their standard errors were 

estimated from linear combinations of model coefficients 

to reflect changes in cross-sectional elements at 

intersections of interest. 

DATA 

Obtaining enough bicycle volume data is essential in 

these evaluations. Since bicyclists do not necessarily 

follow the same travel patterns as passenger cars, actual 

large counts were preferred, when available, over the 

alternative of estimating the volumes from limited data.

After the team reviewed potential datasets from multiple 

States (Texas, Washington, Oregon, Florida, and Virginia), 

the limited availability of locations with actual bicycle 

counts or a potential for estimation (e.g., through other 

variables) drove the decision to narrow the evaluation 

down to the two States with the most promise to develop 

the dataset for analysis. The research team used data from 

Virginia and Texas because of the number of potential 

locations with bicycle exposure estimates or direct 

measurements that could be obtained.

To develop the databases, the research team collected the 

following data elements:

• Bicycle and motor vehicle traffic count.

• Bicycle facility type.

• Multiple intersection design elements (e.g., number 

of approach lanes, presence of turn lane).

• Posted speed limit.

• Crash data (e.g., location, year, type, and severity). 

The research team used geographic information systems 

tools to prepare, filter, and combine multiple datasets 

containing geolocation (typically in shapefile format) 

(EsriTM 2019). GIS tools allow the manipulation, 

combination, and display of data for different types of 

information, including crashes, road infrastructure, traffic 

volume, census tract, and land use, among others.

Bikeway Facility Type and Roadway Data

To better characterize the bicycle treatments applied at 

each intersection and its legs, the research team developed 

and refined a data structure and collection protocol. 

The database consisted of two Microsoft® Excel® 

spreadsheets: one for intersection characteristics, and 

the other for features of each intersection leg. The 

intersection-level spreadsheet contained 12 variables 

to describe the geolocation, control type (signed or 

signalized), and bicycle treatment presented over 

each intersection. The leg-level spreadsheet contained 

24 variables to describe the roadway design characteristics 

and bicycle-lane-related features of road segments 

connected with each intersection.

The collected data can be broadly classified into three 

categories (table 1):

• Intersection characteristics category contains variables 

to describe the geolocation, control type (signed or 

signalized), and bicycle treatment presented for each 

intersection. In this study, the evaluations focused 

on bicycle treatments such as keyways or pockets, 
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mixing zones at the approach, chevrons, extension 

lines, cross-markings, two-stage turn queue boxes, 

and bicycle boxes. 

• Roadway design characteristics category contains 

road-related variables to characterize the road 

segments connected with each intersection. In this 

study, major legs/streets refer to the road segments 

with relatively higher annual average daily traffic 

(AADT) values, and minor legs/streets refer to the 

road segments with relatively lower AADT values.

• Bicycle lane characteristics category contains 

bicycle-lane-related variables to describe each 

intersection’s connecting bicycle lanes, including 

the number of bicycle lanes and the existence of 

four types of treatments—keyways, mixing zones at 

the approach, buffered bicycle lanes, and separated 

bicycle lanes (SBLs). Additionally, the data collected 

include pavement markings, such as chevrons, 

extension lines, and green pavement markings 

through the intersection.

Table 1. Virginia intersections descriptive statistics (n=59 sites).

Variable Mean Std Dev Median Min Max

MajADT 12,921.95 4,684.51 12,921.95 5,200 28,000

MinADT 10,458.54 5,077.33 10,458.54 1,900 28,000

ADBT 89.38 81.89 63 6.98 484.72

NumLegs 3.29 0.74 3 3 5

NLanes.mj (major) 2.32 1.12 2 1 6

NLanes.mn (minor) 1.42 0.7 1 1 4

Lane_Width (ft) 10.39 1.03 10 9 14

Inter_Length (major) (ft) 76.32 31.69 74.02 27.37 212.92

Inter_Length (minor) (ft) 57.58 22.97 53.99 22.63 127.66

Signalized 0.59 0.50 1.0 0 1

Chevrons 0.03 0.18 0 0 1

Cross_Markings 0.39 0.49 0 0 1

NumBikeL 0.53 0.3 0.5 0 1

Bike_L_Wid 2.33 1.85 2 0 10.5

Buffered_BikeL 0.04 0.14 0 0 0.67

Bike_Box 0.02 0.09 0 0 0.5

Two_Way_Cyc 0.06 0.16 0 0 0.5

Through_BikeL 0.31 0.32 0.33 0 1

Std Dev = standard deviation; Min = minimum; Max = maximum; MajADT = major annual daily traffic; MinADT = minor annual daily traffic; 

Num and N = number; mj = major; mn = minor; Inter = intersection; L = lane; Wid = width; Cyc = bicycle.
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Virginia

The researchers obtained the bicycle count data from the 

Virginia DOT bicycle data online repository (VDOT 2021). 

A third-party company maintains and displays an online 

portal with bicycle and pedestrian usage data from 

counters across the State (Eco-Counter 2022). The data 

are collected from several cities from both permanent and 

temporary sites with the help of counters and are managed 

by personnel in the corresponding cities. After eliminating 

locations at bicycle dedicated paths, other locations out of 

the scope of this evaluation, and locations with a limited 

amount of data, the research team examined a dataset of 

historical counts from intersections at 59 locations with 

some level of bicycle treatments represented, as well as 

enough data to estimate daily bicycle volumes. The average 

daily bicycle volume at the intersections ranged from about 

7 to 485 bicycles per day, with an average of 89.

The research team obtained crash data from Virginia’s 

online repository to then integrate with the geometry data 

described at the beginning of this section (VDOT 2021). 

Specifically, team members identified and linked all the 

crashes within the vicinity of 200 ft of the intersections 

included in the database. To match the same period of 

geometry data and when bicycle counts were collected, 

the research team filtered crashes to represent only the 

period from 2015 to 2019. Therefore, the research team 

used 4 yr of crash data. 

Texas

For the Texas sites, the research team used estimated 

bicycle count data from the crowdsourced database Strava 

to produce ADBT estimates (Strava 2018). The research 

team calibrated direct demand models similar to those 

developed by Turner et al. (2019) to estimate the bicycle 

counts from the crowdsourced database.

The research team identified crashes that had occurred on 

the selected intersections (table 2). Because the bicycle 

counts were estimated for a period between July 2016 

and June 2017, the research team selected 2016–2019 

crash data for analysis. This range of dates assumes that 

the bicycle intersection treatments were present at the 

selected locations 1 yr before the data collection. The 

research team used a geolocation buffer of 200 ft to 

initially identify the intersection crashes, as recommended 

by Avelar, Dixon, and Escobar (2015). After crashes 

corresponding to the facilities under study were identified, 

filters were applied to remove non-intersection-related 

crashes before analysis (e.g., driveway related).

Table 2. Texas descriptive statistics of intersections (n=126 sites).

Variable Mean Std Dev Median Min Max

AADT.mj 16,132 13,109 12,517.75 2,678 46,640

AADT.mn 4,568 6,299 2,205.5 23.02 32,497

Maj.ADBT 105 139 37 19 826

Min.ADBT 70 96 29.25 17 569

NumLegs 3.63 0.5 4 2 4

Nlanes.mj 2.83 1.12 3 1 6

Nlanes.mn 1.73 0.83 2 1 4

Lane_Width 12.07 2.36 11.38 9.18 20.19

Signalized 0.58 0.5 1 0 1

Inter_Length1 74.97 30.78 67.75 22.1 204.8

Inter_Length2 53.61 22.5 46.95 23.3 143.6

NumBikeL 0.88 0.58 1 0 2

Bike_L_Wid 3.56 1.95 4 0 11.23
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Table 2. Texas descriptive statistics of intersections (n=126 sites). (Continued)

Variable Mean Std Dev Median Min Max

Buffered_BikeL 0.16 0.35 0 0 1.5

Through_BikeL 0.02 0.18 0 0 2

Treat_1B 0.05 0.31 0 0 2

Treat_2A 0.48 0.95 0 0 4

Treat_3 0.02 0.18 0 0 2

Treat_4 0 0 0 0 0

Chevrons 0.21 0.41 0 0 1

Cros_Markings 0.23 0.42 0 0 1

Treat = treatment; Cros = cross.

ANALYSIS

Safety Effectiveness

In general, the research team used entropy metrics 

(Akaike information criterion and Bayesian information 

criterion) to guide model development. In each case, 

the research team found the best fitting model for the 

response variable of interest, namely crash frequency 

by severity and type. The research team performed 

the analysis separately by State, given the differences 

observed in the descriptive statistics, especially on 

bicycle crashes. Additionally, the dataset from Virginia 

offered ADBT estimates directly from actual counts 

for multiple years at each location under study, whereas 

bicycle volumes were estimated for the Texas dataset.

Virginia

The trends in the safety analyses from Virginia suggested 

crash reductions in general. However, all analyses of this 

dataset yielded statistically insignificant results.

Texas

In contrast to the results in Virginia, the evaluations in 

Texas yielded three statistically significant results: two 

for designs that include SBLs, and one for the mixing zone 

configuration in figure 1. These results are shown in table 3.

This result indicates expected reductions of 44.8 percent 

and 54.4 percent for SBL installations in terms of total and 

non-weather-related crashes, respectively. Similarly, results 

indicate that a 42.9-percent reduction in fatal and injury 

crashes is associated with the mixing zone configuration 

shown in figure 1.

Table 3. Crash CMF estimates for bicycle treatments at intersections in Texas.

Treatment Crash Type CMF Estimate Std Error P Value Significance

SBLa Total 0.552 −0.593 0.345 0.086 ~

SBLa Non-weather 

related 0.456 −0.786 0.253 0.002 **

Bicycle lane mixed 

with through and 

right-turn lanea

Fatal 

and injury 0.571 −0.560 0.264 0.034 *

~Statistically significant at the 0.1 level.

*Statistically significant at the 0.05 level.

**Statistically significant at the 0.01 level. a

Base condition is no bicycle lane.
6

Economic Effectiveness

To perform a B/C analysis, the research team followed 

the procedures recommended in FHWA’s technical 

document entitled, Highway Safety Benefit–Cost 

Analysis Guide (Lawrence et al. 2018). The value of 

a statistical life (VSL) was obtained from the most 

recent memorandum on the U.S. DOT website (Putnam 

and Coes 2021). The recommended range for VSL is 

$10.9 million in 2019, the most recent year included in 

the evaluations of this study.

To estimate B/C ratios, the research team included the cost 

of installing and maintaining SBLs for a period of 20 yr. 

The total project cost of the improvement is estimated as 

$292,918, if no additional right-of-way (ROW) purchase 

is necessary. If ROW should be acquired, the total project 

cost over the period of analysis was estimated to escalate 

to $15,537,362. When the safety benefit is considered, 

the B/C ratio of adding SBLs was estimated as 30.9 if 

no additional ROW is acquired, whereas the B/C ratio 

drops to 5.9 in the scenario where ROW is added to 

accommodate the treatment.

The other treatment that was found beneficial according 

to the evidence in the dataset was the configuration that 

creates a mixing zone between the through and right-turn 

movement for motor vehicles and bicycle lanes (figure 1). 

Assuming that the treatment will not require acquiring 

additional ROW, the B/C ratio of this strategy was 

estimated as 113.3.

Figure 1. Map. Sample intersection with mixing zone for a bicycle lane, multivehicle through lane, 

and right-turn movements. 

© 2022 Google® EarthTM.
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CONCLUSIONS

The objective of this study was to perform a rigorous 

safety effectiveness evaluation of adding bicycle 

treatments at urban intersections that are candidates for 

the treatment. To accomplish the goals of this study, the 

research team compiled safety data from Virginia and 

Texas. The evaluation included total, fatal and injury, 

and non-adverse-weather crashes.

Safety data collection was guided by the availability 

and location of bicycle traffic data, which is an 

influential variable identified in past research on the 

safety effectiveness of the treatment of interest. Similar 

to how AADT is used to account for motor vehicle 

exposure, bicycle traffic should reflect exposure for 

those vulnerable users. In the case of Virginia, the 

research team developed estimates of ADBT using 

actual bicycle counts. For Texas, direct demand models 

were developed and used to estimate bicycle volume. 

Statistically significant CMFs were found from 

Texas for SBL construction with regard to total and 

non-weather-related crashes, as well as for providing 

a mixing zone configuration for bicyclists and motor 

vehicles at the approach with regard to fatal and injury 

crashes. The research team surmises that sample size 

for both States with enough treated sites and enough 

crash data might have been a contributing factor 

for all other evaluations that yielded statistically 

insignificant results.

In the case of the statistically significant CMFs for 

SBLs at intersections, this research found a 54-percent 

crash reduction in non-weather-related crashes in Texas 

(0.456 CMF, 0.002 p value) linked to this treatment. 

This CMF was used to calculate the benefits of 

bicycle through-lane treatments at intersections in the 

B/C ratio evaluations. The costs of construction and 

maintenance were found to be notably smaller than the 

benefit. The B/C ratio was estimated as 5.9 when the 

cost of additional ROW is assumed, and 30.9 without 

that assumption.

In the case of the mixing zone at the approach, the 

estimated CMF was a reduction of 42.9 percent in fatal 

and injury crashes (0.571 CMF, 0.034 p value). The 

research team estimated the B/C ratio in this case to 

be 113.3, assuming no additional ROW is required to 

construct and maintain this treatment.

REFERENCES

Avelar, R., K. Dixon, and P. Escobar. 2015. “Evaluation of 

Signalized-Intersection Crash Screening Methods Based 

on Distance from Intersection.” Transportation Research 

Record, the Journal of the Transportation Research 

Board 2514: 171–186. https://doi.org/10.3141/2514-19, 

last accessed August 9, 2022.

Eco-Counter. 2022. “Cycling Data Tracker” (web page).

https://www.eco-counter.com/cycling-data-tracker/, 

last accessed September 18, 2022.

EsriTM. 2019. ArcGIS (software). Version 10.8.

FHWA. 2022. “Evaluations of Low Cost Safety 

Improvements Pooled Fund Study (ELCSI-PFS)” 

(web page). https://highways.dot.gov/research/safety/

evaluations-low-cost-safety-improvements-pooledfund-study/evaluations-low-cost-safety-improvementspooled-fund-study-elcsi%E2%80%93pfs, last accessed 

August 30, 2022.

Google®. 2022. Google® EarthTM Pro (software). 

Version 7.3.2.5779, obtained from https://earth.google.com/

web/, last accessed May 9, 2022.

Lawrence, M., A. Hackey, G. Bahar, and F. Gross. 2018. 

Highway Safety Benefit-Cost Analysis Guide. Report No. 

FHWA-SA-18-001. Washington, DC: Federal Highway 

Administration.

Li, F., K. Morgan, and A. Zaslavsky. 2018. 

“Balancing Covariates via Propensity Score Weighting.” 

Journal of the American Statistical Association 113, 

no. 521: 390–400.

NACTO. 2019. Don’t Give Up at the Intersection: 

Designing All Ages and Abilities Bicycle Crossings.

Washington, DC: National Association of City 

Transportation Officials. https://nacto.org/wp-content/

uploads/2019/05/NACTO_Dont-Give-Up-at-theIntersection.pdf, last accessed April 1, 2022.

Putnam, J., and C. Coes. 2021. “Guidance on the 

Treatment of the Economic Value of a Statistical 

Life (VSL) in U.S. Department of Transportation 

Analyses—2021 Update.” Official memorandum. 

Washington DC: U.S. Department of Transportation.
8

Researchers—This study was performed by Principal Investigator Raul Avelar, along with Boniphace Kutela 

and Xiao Li, Texas A&M Transportation Institute.

Distribution—This TechBrief is being distributed according to a standard distribution. Direct distribution is being 

made to the FHWA divisions and Resource Center.

Availability—This TechBrief may be obtained at https://highways.dot.gov/research.

Key Words—Safety, crash modification factors, CMFs, bicycle, intersections

Notice—This document is disseminated under the sponsorship of the U.S. Department of Transportation (USDOT) 

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 report only because they are considered essential to the objective of 

the document.

Quality Assurance Statement—The Federal Highway Administration (FHWA) provides high-quality information 

to serve Government, industry, and the public in a manner that promotes public understanding. Standards 

and policies are used to ensure and maximize the quality, objectivity, utility, and integrity of its information. 

FHWA periodically reviews quality issues and adjusts its programs and processes to ensure continuous 

quality improvement.

JUNE 2023 FHWA-HRT-23-031

HRSO-20/06-23(WEB)E

Recommended citation: Federal Highway Administration, 

Development of Crash Modification Factors for Bicycle Treatments 

at Intersections (Washington, DC: 2023) https://doi.org/10.21949/1521975

Strava. 2018. “Strava Metro” (web page). https://metro.

strava.com/, last accessed September 18, 2022.

Turner, S. M., R. J. Benz, J. G. Hudson, G. P. Griffin, 

P. Lasley, B. Dadashova, and S. Das. 2019. Improving the 

Amount and Availability of Pedestrian and Bicyclist Count 

Data in Texas. Report No. FHWA/TX-19/0-6927-R1. 

College Station, TX: Texas A&M Transportation Institute. 

VDOT. 2021. “Crash Data—Virginia Roads” 

(web page). https://www.virginiaroads.org/

maps/1a96a2f31b4f4d77991471b6cabb38ba/about, 

last accessed September 18, 2022.