Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

PUBLICATION NO. FHWA-HRT-23-020 AUGUST 2023 

Development of Crash Modification 

Factors for Bicycle Treatments 

at Intersections
FOREWORD

The research documented in this report was conducted as part of the Federal Highway 

Administration’s (FHWA) Evaluation of Low-Cost Safety Improvements Pooled Fund Study 

(ELCSI-PFS). FHWA established this PFS in 2005 to conduct research on the effectiveness of 

safety improvements identified by the National Cooperative Highway Research Program 

Report 500 guides as part of the implementation of the American Association of State Highway 

and Transportation Officials’ Strategic Highway Safety Plan. ELCSI-PFS studies provide a crash 

modification factor and benefit–cost economic analysis for each targeted safety strategy 

identified as a priority by member States of the PFS.

This report documents the safety effectiveness of bicycle treatments at urban intersection 

locations in Virginia and Texas. This study included bicycle lanes, extension lines, through 

bicycle lanes, buffered bicycle lanes, and chevron pavement markings along the intersections. 

The evaluation considered the total number of vehicular crashes, including those resulting in 

fatality and injury. The analysis found statistically significant crash reduction for separated 

bicycle lanes in certain, but not all, contexts. These study results may be of interest to roadway 

safety professionals, State and local engineers, and planners responsible for the design and 

operation of facilities that may benefit from bicycle lane installations. 

Brian P. Cronin, P.E. 

Director, Office of Safety and Operations 

Research and Development

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. 
TECHNICAL DOCUMENTATION PAGE

1. Report No.

FHWA-HRT-23-020

2. Government 

Accession No.

3. Recipient's Catalog No.

4. Title and Subtitle

Development of Crash Modification Factors for Bicycle

Treatments at Intersections

5. Report Date

August 2023

6. Performing Organization Code

7. Author(s)

Raul Avelar (ORCID: 0000-0002-3962-1758), Boniphace 

Kutela (ORCID: 0000-0002-5450-1623), Xiao Li (ORCID:

0000-0002-6762-2475)

8. Performing Organization Report No.

9. Performing Organization Name and Address

Texas A&M Transportation Institute

The Texas A&M University System

College Station, TX 77843-3135

10. Work Unit No. (TRAIS)

11. Contract or Grant No.

DTFH6116D00039L

12. Sponsoring Agency Name and Address

Federal Highway Administration

Office of Safety Research and Development

6300 Georgetown Pike

McLean, VA 22101

13. Type of Report and Period Covered

Final Report; May 2019—August 2022

14. Sponsoring Agency Code

HRSO-20

15. Supplementary Notes

The Federal Highway Administration (FHWA) Office of Safety Research and Development managed this 

project under the Development of Crash Modification Factors program. The FHWA Office of Safety and 

Operation Research and Development Program Manager and the Technical Manager was Roya Amjadi 

(HRDS-20; ORCID: 0000-0001-7672-8485).

16. Abstract

This research developed crash modification factors (CMFs) for bicycle treatments at urban intersections in the 

United States. The treatments evaluated were the presence of bicycle lanes, extension lines, through bicycle 

lanes, buffered bicycle lanes, and chevrons along the intersections. The research team compiled and analyzed 

safety data from Virginia and Texas. The evaluation considered the total number of vehicular crashes, including 

those crashes resulting in fatality and injury. Safety data collection focused on locations with available bicycle 

traffic data, or where direct demand models were applicable, and at intersections where bicycle intersection 

countermeasures were sufficient to conduct a statistical analysis. The research team included bicycle traffic 

volume counts as a key factor in the analysis, similar to how average annual daily traffic accounts for motor 

vehicle exposure. For the Virginia study sites, the research team developed estimates of average daily bicycle 

traffic using actual bicycle counts. For the Texas sites, the bicycle volume estimates were based on direct 

demand models developed by team members for this evaluation. The analysis found that having separated 

bicycle lanes and providing a mixing zone between bicyclists and motor vehicles at intersection approaches was 

associated with reductions in total, fatal and injury, and non-weather-related crashes in Texas. Other evaluations 

that considered bicycle and vehicular traffic volume, traffic control devices, and other geometric design features 

yielded CMFs that were not statistically significant. For the economic evaluations, benefit–cost ratios for the two 

identified treatments were estimated as low as 5.9 when additional right of way was assumed, and up to 113.3 

otherwise. Results indicate that these treatments are economically feasible. 

17. Key Words

Safety, crash modification factor, CMF, bicycle lanes

18. Distribution Statement

No restrictions. This document is available to the public 

through the National Technical Information Service, 

Springfield, VA 22161.

http://www.ntis.gov

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages

64

22. Price

N/A

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized
ii

 

 

 

 

 

 

 

 
iii

TABLE OF CONTENTS

EXECUTIVE SUMMARY .......................................................................................................... 1

CHAPTER 1. INTRODUCTION................................................................................................ 3

Literature Review ................................................................................................................... 3

Intersection Geometric Design and User Expectations ...................................................... 3

BL Evaluations.................................................................................................................... 7

Bicycle Intersection Crash Characteristics ......................................................................... 8

Summary of Literature on BL Treatments at Intersections .............................................. 10

Traffic Control Devices for Bicycles at Intersections....................................................... 11

CHAPTER 2. STUDY DESIGN AND STATISTICAL METHODOLOGY ........................ 17

Statistical Methods in the Data Management Process....................................................... 17

Data Extraction and Integration ........................................................................................ 18

Data Balancing.................................................................................................................. 18

PS Methods ....................................................................................................................... 18

Study Design.......................................................................................................................... 19

Data Analysis Methods ......................................................................................................... 20

Generalized Linear Regression Analysis with PSM or PSW ........................................... 20

Mixed-Effects Models ...................................................................................................... 20

CMF Estimation................................................................................................................ 22

Chapter Summary ................................................................................................................ 23

CHAPTER 3. DATA COLLECTION AND INTEGRATION............................................... 25

Bikeway Facility Type and Roadway Data ......................................................................... 25

Supplemental Data on Intersection Approaches................................................................ 29

Virginia .................................................................................................................................. 29

Bicycle Count Data ........................................................................................................... 29

Crash Data......................................................................................................................... 31

Texas....................................................................................................................................... 31

Bicycle Exposure Data...................................................................................................... 32

Crash Data......................................................................................................................... 33

Chapter Summary ................................................................................................................ 34

CHAPTER 4. SAFETY EFFECTIVENESS EVALUATION ................................................ 35

Modeling Process .................................................................................................................. 35

Virginia ............................................................................................................................. 35

Texas................................................................................................................................. 41

Chapter Summary ................................................................................................................ 46

CHAPTER 5. ECONOMIC ANALYSIS.................................................................................. 47

Cost of Crashes and B/C Ratio ............................................................................................ 48

Chapter Summary ................................................................................................................ 48

CHAPTER 6. SUMMARY AND CONCLUSIONS................................................................. 49

ACKNOWLEDGMENTS.......................................................................................................... 51

REFERENCES............................................................................................................................ 53 
iv

LIST OF FIGURES

Figure 1. Illustration. Intersection with BL, left-turn lane, and RTL (FHWA 2012). .................... 5

Figure 2. Map. Sample buffered BL design at intersections. .......................................................... 6

Figure 3. Map. Sample SBL design between intersections. ........................................................... 6

Figure 4. Map. Sample intersection with extension lines and chevrons. ...................................... 12

Figure 5. Map. Sample intersection with extension lines and colored crossing markings. .......... 12

Figure 6. Map. Sample intersection with two-phase-turn queue box for bicycles. ...................... 13

Figure 7. Map. Sample intersection with bike box on the bottom approach. ............................... 14

Figure 8. Map. Sample intersection with keyway bike through lane (to the left of RTL)............ 14

Figure 9. Map. Sample intersection with mixing zone for a BL, MV through lane, and 

right-turn movements. ......................................................................................................... 15

Figure 10. Equation. PS definition as logistic function of covariates. .......................................... 18

Figure 11. Equation. Site-level Poisson distribution of yearly crashes. ....................................... 21

Figure 12. Equation. Parameterized yearly expectation of crashes. ............................................. 21

Figure 13. Equation. Parameterization of explanatory variables in regression model. ................ 22

Figure 14. Equation. Standard error for contrast in log scale. ...................................................... 23

Figure 15. Equation. ADBT estimation equation developed from crowdsource data. ................. 33

Figure 16. Graph. Bicycle volume distributions by presence of intersection treatments in 

Virginia. ............................................................................................................................... 36

Figure 17. Graph. Weighted bicycle volume distributions by presence of treatments in 

Virginia................................................................................................................................ 37

Figure 18. Graph. Major AADT width distributions by bicycle intersection treatments in 

Texas. .................................................................................................................................. 42

Figure 19. Graph. Major AADT weighted distributions in Texas. ............................................... 42
v 

LIST OF TABLES

Table 1. List of variables collected for each intersection. ............................................................ 26

Table 2. Virginia intersections descriptive statistics (n=59 sites). ............................................... 30

Table 3. Virginia traffic control at intersections descriptive statistics (n=59 sites)...................... 31

Table 4. 2015–2019 Virginia crashes descriptive statistics (n=59 sites). ..................................... 31

Table 5. Texas descriptive statistics of intersections (n=126 sites). ............................................. 32

Table 6. Texas descriptive statistics of traffic control at intersections (n=126 sites). .................. 32

Table 7. Texas 2016-2019 crash descriptive statistics (n=138 sites). ........................................... 34

Table 8. Response variable and model specifications. ................................................................. 35

Table 9. Coefficient estimates for bicycle crash prediction model in Virginia 

(n=236 intersection periods). ............................................................................................... 38

Table 10. Bicycle crash CMF estimates for bicycle treatments at intersections in Virginia........ 38

Table 11. Coefficient estimates for total crashes prediction model in Virginia 

(n=236 intersection periods). ............................................................................................... 39

Table 12. Total crash CMF estimates for bicycle treatments at intersections in Virginia. ........... 39

Table 13. Coefficient estimates for FI crashes prediction model in Virginia 

(n=236 intersection periods). ............................................................................................... 40

Table 14. FI crash CMF estimates for bicycle treatments at intersections in Virginia. ................ 41

Table 15. Coefficient estimates for total crashes prediction model in Texas 

(n=126 intersections). .......................................................................................................... 43

Table 16. Total crash CMF estimates for bicycle treatments at intersections in Texas. ............... 44

Table 17. Coefficient estimates for FI crash prediction model in Texas 

(n=126 intersections). .......................................................................................................... 44

Table 18. FI crash CMF estimates for bicycle treatments at intersections in Texas. ................... 45

Table 19. Coefficient estimates for non-weather-related crash prediction model in Texas 

(n=126 intersections). .......................................................................................................... 45

Table 20. Non-weather-related crash CMF estimates for bicycle treatments at intersections 

in Texas. .............................................................................................................................. 46
vi 

LIST OF ABBREVIATIONS

AADT annual average daily traffic

AASHTO America Association of State Highway and Transportation Officials

ADBT average daily bicycle traffic

B/C benefit–cost

BL bicycle lane

CMF crash modification factor

DCMF Development of Crash Modification Factors 

DOT department of transportation 

ELCSI-PFS Evaluation of Low-Cost Safety Improvements Pooled Fund Study 

FHWA Federal Highway Administration

FI fatal and injury 

GIS geographic information systems

GLM generalized linear model

GLMM generalized linear mixed model

LTL left-turn lane 

MUTCD Manual on Uniform Traffic Control Devices

MV motor vehicle 

NACTO National Association of City Transportation Officials

ROW right of way 

RTL right-turn lane 

SBL separated bicycle lane

Std Dev standard deviation 

PS propensity score 

PSM propensity score matching 

PSW propensity score weighting 

SPF safety performance function

VDOT Virginia Department of Transportation 

Vpd vehicles per day

VSL value of a statistical life
1 

EXECUTIVE SUMMARY

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

(DCMF) Program was established in 2012 to address highway safety research needs and

evaluating new and innovative safety strategies (improvements) by developing reliable 

quantitative estimates of their effectiveness in reducing crashes (FHWA 2022a). 

The ultimate goal of the FHWA DCMF Program is to save lives by identifying new safety 

strategies that effectively reduce crashes and promote these strategies for nationwide installation 

by providing measures of their safety effectiveness through the development of crash 

modification factors (CMFs) and benefit–cost (B/C) ratios through research. State departments of 

transportation (DOTs) and other transportation agencies need to have objective measures for 

safety effectiveness and B/C ratios before investing in new strategies for statewide safety 

improvements. 

The Evaluation of Low-Cost Safety Improvements Pooled Fund Study (ELCSI-PFS) functions 

under the DCMF Program (FHWA 2022b). Forty-one State DOTs are members of the 

ELCSI-PFS and provided technical feedback on high-priority research needs for safety 

improvements to the FHWA DCMF Program. These States implement new and unproven safety 

improvements to facilitate ELCSI-PFS evaluations. 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. 

This evaluation assessed the potential safety improvements of various intersection geometric and 

traffic control device treatments to reduce crashes with regard to total vehicular crashes, fatal and

injury (FI) vehicular crashes, and bicycle-specific crash frequencies. The intent was to develop 

CMFs and 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. 

In this study, the assessment focused on various strategies to accommodate bicycle lanes on the 

approaches, as well as other pavement markings on the intersection box, such as extension lines, 

colored crossing markings, and chevrons. The research team obtained geometric, traffic, and 

crash data at treated locations in Virginia and Texas. The limited availability of bicycle volume 

data to estimate average daily bicycle traffic (ADBT) was a controlling factor in the feasibility of 

developing a before–after study with a sufficiently large sample size. Ultimately, this research 

evaluated the bicycle treatments using a cross-sectional design. The modeling approach used

generalized linear models and generalized linear mixed models. The evaluation included 

untreated sites (i.e., without bicycle treatments) with similar characteristics to sites with the 

intervention, so a contrast between treated and comparison sites could be made. This approach 

yields an unbiased estimate of a shift in safety performance associated with the treatments. The 

research team developed and applied propensity score (PS) weights to support causal inference 

while correcting for imbalances in the covariates (Banihashemi 2016; Li, Morgan, and Zaslavsky 

2018). These methods are supported by the statistical literature regarding applicability in causal 

effects estimation problems, as is the case in this study (Imai and Ratkovic 2015; Vermeulen and

Vansteelandt 2015). 
2 

The study included locations for which bicycle traffic volume estimates were available or could 

be made available, as volume was expected to be an influential variable affecting bicycle 

crashes, similar to how annual average daily traffic accounts for motor vehicle exposure. In the 

case of Virginia, the research team developed ADBT estimates using actual bicycle counts. In 

the case of Texas, the bicycle volume estimates were based on direct demand models developed 

for this purpose. CMF estimates for total and non-weather-related crashes in Texas were

statistically significant for the application of separated bicycle lanes at intersections (0.552 and 

0.456 CMFs, respectively). A CMF of 0.571 for FI crashes was estimated when a mixing zone 

between bicycle and two motor vehicle movements at intersection approaches was present. For 

other evaluations, results were statistically insignificant. The economic evaluation used the 

statistically significant CMF as the best available estimate of a hypothesized benefit of the two 

treatments of interest. The evaluation found that when these treatments are added at 

intersections, the construction and maintenance costs are notably smaller than the expected 

benefit. The B/C ratio estimates varied from 5.9 up to 113.3, depending on the assumptions 

implied in the cost estimates. Overall, results indicated the economic feasibility of the assessed 

treatments. 
3 

CHAPTER 1. INTRODUCTION

A large proportion of crashes between bicyclists and motor vehicles (MV) occur at intersections. 

Bicycle lanes (BLs) are travel lanes dedicated to bicyclists along a street. The presence of bicycle 

treatments at intersections can help raise awareness among motorists that these vulnerable users 

are present and that they should be alert. To provide additional accommodation for bicycles, a 

BL, when present, is commonly placed on the left of MV right-turn lanes (RTLs). 

Developing crash modification factors (CMFs) for a particular treatment relies on a sufficient 

number of sites with the treatment and a sufficient number of bicycle crashes to assess any 

changes due to the treatment. This study overcame these challenges by considering several 

intersection approach configurations when BLs are present on the major approaches. 

LITERATURE REVIEW 

This section presents a summary of the relevant literature on safety effectiveness of bicycle 

configurations at intersections. 

It has been well documented that a significant number of crashes between bicycles and MVs

occur at intersections. In 2017, the National Association of City Transportation Officials 

(NACTO) estimated that 43 percent of urban bicyclist fatalities occurred at intersections 

(NACTO 2019). BLs are the most common bicycle facility in use in the United States, and most 

jurisdictions are familiar with their design and application, as described in the Manual on 

Uniform Traffic Control Devices (MUTCD) and the American Association of State Highway and 

Transportation Officials (AASHTO) Guide for the Development of Bicycle Facilities (FHWA 

2012; AASHTO Task Force on Geometric Design 2012). 

Bicycle crashes at signalized intersections are linked to the average daily bicycle volume

(Reynolds et al. 2009). Because of this connection between crashes and exposure, an increase in 

ridership could lead to an increase in bicycle crashes at newly implemented BL locations, 

especially at locations with separated bicycle lanes (SBLs), which are BLs physically shielded 

from the main MV lanes (Dill and Carr 2003; Bryant, Deutsch, and Goodno 2016). 

Intersection Geometric Design and User Expectations

Past work has recommended that construction of cycling facilities should not create a false sense 

of safety (Høye 2017; Arvidson 2012). Many urban intersections are designed with visibility of 

bicyclists and their expected interaction with MVs. The goal of these design strategies is to 

provide bicyclists and other users a higher level of safety. Documented intersection designs with 

safety and operational benefits include bicycle boxes (figure 7), BLs to the left of RTLs, and 

raised bikeways that are continuous across side road crossings or minor street crossings (Klassen, 

Basyouny, and Islam 2014). 

According to Høye (2017), an important concern with bicycles at intersections is that they might 

not be conspicuous to motorists. Another concern is that MV drivers may potentially assume that 

bicyclists in BLs must give way to MVs, regardless of the actual priority rules. Other safety 

concerns in the vicinity of intersections include poor sight conditions, the proximity to—and 
4 

level of—MV parking, and the effective separation from pedestrian areas (Høye 2017). 

Intersections will experience increased bicycle–MV conflicts when bicyclists and motorists lack 

a good understanding of each other’s intended behavior. Forty percent of cyclists reported being 

in a near collision on the BL (Arvidson 2012). 

According to one study, the main design considerations to improve BL safety for users 

approaching and moving through an intersection are as follows (Bryant, Deutsch, and Goodno 

2016): 

• On-street parking setbacks. 

• Lateral deflection. 

• Mixing zones. 

• Bicycle signs and signals. 

• Conflict zone treatments.

A detailed review of six crashes involving bicyclists showed that five of them occurred when 

drivers crossed the BLs to enter an alley when a bicycle was present; similarly, one crash 

occurred when a vehicle exited an alley and crossed a BL when a bicycle was present (Bryant, 

Deutsch, and Goodno 2016). In their examination of the prevalent conditions during these 

crashes, Bryant, Deutsch and Goodno (2016) pointed to the need for better visibility and 

proposed potential strategies, such as eliminating on-street parking or providing parking setbacks 

at alley entrances and exits. Lateral deflection—an alignment deviation of the travel way to 

better position bicycles to be easily viewed by motorists—has an added benefit of increasing 

bicyclists’ awareness of all cross-traffic modes at intersections. However, providing lateral 

deflection can be problematic if the number of alignment shifts becomes excessive. 

An AASHTO guideline indicates that some cities have exceeded the prescribed minimum 

recommended dimensions for bicycle facilities, resulting in increased comfort and safety for

bicyclists (AASHTO Task Force on Geometric Design 2012). A potential safety concern for 

cyclists at intersections is the conflict with right-turning MVs, and a geometric design decision 

on BL placement must be made at intersections featuring RTLs. To provide additional 

accommodation for bicycles, an alternative is to place a BL on the left of the RTL, as shown in 

figure 1. Alternatives designs are also available, including dissolving the BL into a mixing zone 

in the area immediately upstream of the stop bar. 
5 

Source: FHWA.

Figure 1. Illustration. Intersection with BL, left-turn lane, and RTL (FHWA 2012). 

The Urban Bikeway Design Guide by NACTO (2014) provides design guidelines for treating 

BLs and turn lanes. In some situations, where a through travel lane becomes an RTL, bicyclists 

need to move laterally to weave across the travel lane. The BL along the curb is commonly

dropped, and another small BL segment—often referred to as “keyway” or “pocket”—is 

introduced on the left side of the RTL, just upstream of the stop bar. 

A good design provides bicyclists with the opportunity to reposition themselves to avoid 

conflicts with turning MVs. The NACTO (2014) Urban Bikeway Design Guide offers detailed 

guidance on intersection design treatments that reduce vehicle-bike and vehicle-pedestrian 

conflicts. These treatments include conventional BL designs, buffered BLs, and SBLs.

Lane Designs at Intersections

Buffered BLs are conventional BLs paired with a designated buffer space separating the BL from 

the adjacent MV travel lane and/or parking lane. The buffer provides greater distance between 

MVs and bicycles and creates space for a bicycle to pass another bicycle without encroaching 

into the adjacent MV travel lane. Figure 2 shows an example of a buffered BL design, and

figure 3 shows an SBL, which includes vertical elements, such as plastic pylons, inside the 

buffer. 
6 

© 2022 Google® EarthTM.

Figure 2. Map. Sample buffered BL design at intersections. 

 © 2022 Google® EarthTM.

Figure 3. Map. Sample SBL design between intersections.
7 

BL Evaluations 

This section summarizes past evaluations of the safety performance of BL treatments at 

intersections.

Studies have showed that BLs increase perceived safety by bicyclists who feel less safe cycling 

among mixed traffic because they fear being sideswiped (Rasmussen and Rosenkilde 2007; Ng, 

Debnath, and Heesch 2017). According to a study conducted by researchers at Portland State 

University, bicyclists felt the risk was lower in buffered BLs, and nearly 9 of 10 cyclists 

preferred a buffered BL to a standard lane at intersections (Monsere, McNeil, and Dill 2011). 

Bicyclist perception of safety appears to be more heavily influenced by the volume of turning 

MV traffic rather than the prevalence of wrong maneuvers of turning MVs at these locations

(Monsere et al. 2015). 

Flügel et al. (2015) found a perceived safety gain at urban environments from a stated preference 

survey for separate bicycle facilities (bike paths) where bicycle interactions with motorized 

traffic are restricted. Another study found that the introduction of cycling tracks (i.e., BLs) was 

not perceived by the participants of the survey to bring a significant overall change in the number 

of bicycle-related crashes (Elvik et al. 2009). 

At locations with bicycle traffic, the use of signs prohibiting certain turning movements may be 

warranted (Hunter, Thomas, and Stutts 2006). Approximately 70 percent of bicyclists indicated 

that installing BLs improved their sense of safety, and 45 percent of the motorists also felt that 

driver behavior was safer and calmer when a BL is present. In the study by Hunter, Thomas, and 

Stutts (2006), 41 percent of cyclists stated that they had been involved in a near collision with 

pedestrians at intersections before the installation of a BL. 

Cherry, Hill, and Xiong (2012) simulated three intersections in VISSIM® using real-world data 

(Planung Transport Verkehr 2022). They then developed measures quantifying the conflict 

between bicycles and right-turning automobiles in China. The authors proposed layouts that 

introduced separate lanes for through bikes to eliminate prolonged conflicts that occur when 

bicycles and MVs share a lane. Delays and queue lengths were studied, and mixed results were 

obtained, depending on the configuration. The study suggested that a small barrier be installed 

between the RTL and the through BL to ensure compliance. This configuration moved the 

conflict point upstream from the intersection where bike and car traffic densities are lower 

(Cherry, Hill, and Xiong 2012). 

At buffered BLs without an RTL, motorists’ turning actions became inconsistent, posing a risk to 

both cyclists and other motorists. More than one-third of cyclists reported being involved in a 

near collision with a right-turning vehicle, and four respondents (3 percent) were involved in an 

actual collision with a right-turning vehicle in the buffered BL (Monsere, McNeil, and Dill 

2011). 

Madsen and Lahrmann (2017) compared five intersection layouts based on video data to evaluate 

reaction-based conflicts. This study suggested that ending the BL upstream of the intersection, as 

well as the presence of a narrow BL next to a shared lane for straight and right-turning MVs, is 

less safe for cyclists than a BL marked with solid lines next to a shared lane for straight and 
8 

right-turning MVs. The low number of conflicts observed, however, suggests that the amount of 

data may not have been sufficient to make generalizable conclusions (Madsen and Lahrmann 

2017). 

Monsere et al. (2015) conducted a comprehensive evaluation of intersection designs for SBL in 

Oregon. The study used 78 h of video data that showed 6,082 bicyclists and 7,574 turning MVs. 

Results from this study indicated that the amount of weaving conflict in the mixing zone tended

to be similar across treatments, but a semiprotected through BL (skip pattern of extension lines 

with green pavement markings on the approach) may be associated with fewer conflicts in the 

mixing zone of the design. 

Bicycle Intersection Crash Characteristics

Carter et al. (2006) developed macrolevel pedestrian and bicycle intersection safety indexes that 

would allow engineers, planners, and other practitioners to use known intersection characteristics 

to proactively prioritize crosswalks and other intersection countermeasures for pedestrian and 

bicycle safety. The study sites were in Gainesville, FL (19 sites); Philadelphia, PA (21 sites); 

Portland, OR (13 sites); and Eugene, OR (14 sites). The characteristics evaluated by these 

researchers included traffic speed (high and low); traffic volumes (high and low); number of 

traffic lanes (two lanes and three or more lanes); bike facilities (BLs, wide curb lanes, etc.); RTL

design (shared or exclusive); and left-turn lane (LTL) design (shared or exclusive). Crash data, 

behavioral data (conflicts and avoidance maneuvers), and subjective intersection ratings were 

analyzed. Statistical models for the average left-turn, right-turn, and through ratings were 

developed using regression analyses. Bicycle crashes were found to increase with increasing 

major street traffic volume, higher main street speed limits, presence of turning vehicle traffic, 

cross-street traffic volume, number and presence of RTLs on main street approach, presence of a 

traffic signal at an intersection, on-street parking on main street approach, number of traffic lanes 

for bicyclists to cross to make a right (or left) turn, and presence of a BL. 

Based on a Bayesian meta-analysis of 20 studies that evaluated the effectiveness of different 

bicycle facilities at road junctions in Scandinavia, Garder, Leden, and Thedeen (1994) estimated 

that introducing a BL at an intersection may increase the risk of crashing with a vehicle by about 

40 percent. Their Bayesian methodology study includes the prior opinion of experts (that the 

introduction of the BL would increase the risk by about 20 percent on average), the opinion of 

interviewed cyclists (that the risk would decrease by about 20 percent), and the evidence of past 

studies. Those percentages refer to continuing the BL through the intersection. The authors 

recommend terminating the BL some distance before the intersections or switching the path to 

the left of an RTL (Garder, Leden, and Thedeen 1994). 

The literature shows mixed findings concerning the benefits of BLs arriving at intersections. 

Rasmussen and Rosenkilde (2007) and Loveday (2000) argue that the construction of BLs at 

intersections in Copenhagen increased collisions by 18 percent. However, other studies offer 

evidence that BLs reduce the number of injury collisions (Turner, Binder, and Roozenburg 

2009). Herrstedt et al. (1994) recommended the elimination of BLs 20–30 m before signalized 

intersections and forcing road users to drive or ride closer to each other to improve the visibility 

of cyclists. The study determined that a truncated BL followed by an RTL in which cyclists 

merged with the right-turning MVs was safer. The study showed no difference in the number of 
9 

cyclist crashes, whereas moped riders experienced significantly fewer right-hook and left-hook 

crashes (turning vehicle against straight-going bicycles) compared to a full-length BL. This 

recommendation is aligned with the recommendations by Garder, Leden, and Thedeen (1994). 

Another study in Kentucky used partial proportional odds models to evaluate the injury severity 

among bicyclists at unsignalized intersections (Wang, Lu, and Lu 2015). The authors coded the 

injury severity on a four-point scale: slight injury, non-incapacitating injury, incapacitating 

injury, or fatality. Older (age > 55 yr) drivers and bicyclists and child (age < 16 yr) bicyclists 

were more likely to be severely injured. Uncontrolled intersections, foggy and rainy weather, 

inadequate use of lights in dark conditions, heavier vehicles, and wet road surfaces were also 

linked with increased injury severity. Stop-controlled intersections, one-lane approaches, helmet 

usage, and lower speed limits were associated with decreased injury severity. 

Thomas and DeRobertis (2013) reported that one-way BLs are generally safer at intersections 

than two-way lanes. Additionally, the authors argued that properly signed and demarked, new 

BLs on busy streets should result in fewer collisions and injuries. When controlled for exposure 

and including all collision types, building one-way bicycle lanes was estimated to result in 

reduced injury severity, even when intersection specific treatments are not employed. 

Ma et al. (2010) conducted longitudinal analyses of signalized intersections and road segments to 

understand the influence of a variety of factors on crash occurrences at signalized intersections. 

The study used generalized estimating equations. Barriers that separated bikeways from 

roadways on minor roads were found to be significantly linked to reduced severe crash risk

(Ma et al. 2010). 

Safety of Through BLs 

Through BLs are BLs at signalized intersections to the left of the MV RTL (also called 

through-BLs), as shown next to the RTL in figure 1. The objective of this treatment is to replace 

the dangerous conflicts between the right-turning MVs and bicycles going straight ahead with

less hazardous merging situations in front of the intersection. A study was conducted in Oslo, 

Norway to determine the safety impacts of this type of cycle lanes, but a crash analysis could not 

be conducted due to the small number of occurrences. However, analysis of conflicts involving 

cyclists at six intersections showed that intersections with central approach lanes have more 

conflicts than those without this design. However, the measure seemed to improve the mobility 

and perceived safety at intersections (Sørensen 2010). In contrast, work by Schepers et al. (2011) 

showed that the crash probability (as opposed to conflict frequency) seems to be reduced when 

BL approaches at the intersection are deflected between 2 and 5 m away from the main travel 

lanes. 

Consistent with Sorensen’s (2010) assessment of increased perceived safety, a survey in 

Australia showed preference among bicyclists for keeping exclusive BLs to the right of LTLs 

(analogous to exclusive BL to the left of RTLs in the United States) (Daff and Barton 2010). 

Nearly 100 intersections in Melbourne, Australia, were then configured that way. 
10 

Safety of Shared Bicycle/MV Lanes at Intersections

By combining vehicle turn lanes and BLs at signalized intersections, motorists are encouraged to 

turn at a lower speed by yielding to bicycles. These combined lanes could guide bicycles to 

travel with slower traffic by maintaining comfort and priority when BLs are discontinued 

(NACTO 2014). 

The mixing zone technique is a strategy that merges the left-turn MV lane and the BL into a 

single 16- to 18-ft lane, creating a weaving condition⸻where MVs cross the bike lane

(BL)⸻that slows both modes and could increase safety. However, undue delays may occur. 

Bryant, Deutsch, and Goodno (2016) reported a high incidence of violations (41 percent of all 

bicyclists) at an intersection in Washington when the protected left turns for bicycles were 

eliminated because of this treatment, resulting in traffic queuing and higher intersection delay, 

which then contributed to more signal violations. Because of the issue observed with high 

left-turning volumes, the study then recommended bike movements not be separated from 

left-turning movements at locations where the hourly left-turning volume exceeds 250 vehicles 

per hour (Bryant, Deutsch, and Goodno 2016). 

Summary of Literature on BL Treatments at Intersections

The safety and bicycle operational characteristics at intersections are significantly impacted by 

the intersection design. Sight conditions, on-street parking setbacks, and the separation of BLs 

are some of the most important safety features to be considered at intersections (Høye 2017; 

Bryant, Deutsch, and Goodno 2016; Arvidson 2012). The AASHTO task force on geometric 

design and the NACTO urban bikeway design guide provide design guidelines for combined 

BLs and turn lanes, as well as safety central approach designs (AASHTO 2013; NACTO 2014). 

In general, studies show that BLs tend to increase bicyclists’ perceived safety (Rasmussen and

Rosenkilde 2007; Monsere, McNeil, and Dill 2011; Elvik et al. 2009; Hunter, Thomas, and Stutts

2006). Some studies show conflict reductions for both bicycles and vehicles associated with 

familiarity of the design (Philips et al. 2011). Installing bicycle signals was found to increase 

compliance with intended cross-path areas for right-turning MVs (Rahimi, Kojima, and Kubota 

2013). Signage indicating the presence of bicycles was found to correlate with decreased chances 

of severe collisions involving bicyclists at intersections (Klassen, Basyouny, and Islam 2014). 

Although BLs improve the mobility and perceived safety at intersections, some research also 

points to increased conflicts at intersections (Sørensen 2010). 

Regarding crashes, some studies found no changes in risk at intersections after the installation of 

bicycle facilities (Harris et al. 2013). However, other studies have reached opposite conclusions. 

For example, Garder, Leden, and Thedeen (1994) found that the risk of crashing with a vehicle 

increases by about 40 percent when BLs are part of the intersection design. They recommend 

terminating the BL some distance before the intersections or switching the bicycle path to the left 

of an RTL, effectively applying a central approaching lane BL. A truncated BL followed by an 

RTL in which cyclists merged with the right-turning MVs was found beneficial (Herrstedt et al. 

1994). 
11 

In general, crash-based studies offer mixed results regarding the effectiveness of different 

strategies to manage BLs at intersections. Sundstrom, Quinn, and Weld (2019) found that SBLs 

reduced the rate of crashes per bicyclist by an average of 30 percent. One-way BLs were 

observed to be generally safer at intersections than two-way lanes when effective intersection 

treatments are employed. When controlled for exposure and including all collision types, 

building one-way BLs was found to reduce injury severity compared with not employing these

intersection treatments (Thomas and DeRobertis 2013). Crash probability was reduced when the 

BL approaches were deflected between 2 and 5 m away from the main MV lanes (Schepers et al. 

2011). However, Rasmussen and Rosenkilde (2007) and Loveday (2000) found an 18-percent 

increase in associated with the construction of BLs at intersections in Copenhagen. 

Traffic Control Devices for Bicycles at Intersections

Part 9 of the MUTCD addresses signs, markings, channelizing devices, and signals for bicycle 

facilities (FHWA 2012). Five different marking treatments at intersections are described: 

crossing markings, chevrons on the BLs through the intersection, two-phase left-turn markings, 

bike boxes, and bicycle through lane markings. Each of these treatments has been evaluated 

previously for bicycle and motorist compliance and comprehension, in addition to safety, as 

measured through surrogate measures such as conflicts (Ohlms and Kweon 2018; Casello et al. 

2017; Dill, Monsere, and McNeil 2012; Loskorn et al. 2013). 

Chevrons and Extension Lines 

Agencies have used various strategies to clearly mark the path for bicyclists through 

intersections. These markings also help raise motorist awareness of the possible presence of 

bicyclists in the intersection. Figure 4 shows intersection chevron pavement markings. This 

intersection also has white crossing markings showing the BL path through the intersection. 

Some jurisdictions also use color in these crossing markings, as shown in figure 5, as allowed in 

MUTCD Section 9C.03 (FHWA 2012). Extension lines on the intersection box are also referred 

to as “cross-bike markings” (e.g., NACTO 2019). One study of motorist yielding in Portland 

(Appiah 2021) assessed the effect of green crossing markings on motorist yielding behavior 

before and after their installation. The study found through video analysis that motorists yielding 

to bicyclists increased after the installation of these markings.
12 

© 2022 Google® EarthTM.

Figure 4. Map. Sample intersection with extension lines and chevrons. 

© 2022 Google® EarthTM.

Figure 5. Map. Sample intersection with extension lines and colored crossing markings. 
13 

Two-Phase-Turn Queue Box 

Left turns for bicyclists pose a specific quandary—where is the best place to position them? On

the approach to the intersection, a left-turning bicyclist can choose to cross left through lanes to 

reach a dedicated vehicle LTL or execute the left turn in two phases by first crossing through the 

intersection then turning 90 degrees to cross the through lane (figure 6). 

© 2022 Google® EarthTM.

Figure 6. Map. Sample intersection with two-phase-turn queue box for bicycles.

Two-phase-turn queue boxes indicate the location where a left-turning bicyclist should wait, 

which is on the far side of the intersection, before proceeding with the left turn. Ohlms and 

Kweon (2018) assessed compliance and conflicts at several intersections before and after the 

installation of bike boxes and two-phase-turn queue boxes. Their matched-pair analysis showed 

mixed results, with some bicyclists not using the markings correctly and traffic violations 

increasing for some approaches. 

Another assessment of two-phase-turn boxes was completed in Toronto and showed that 

70 percent of bicyclists used the boxes properly and legally executed their turns (Casello et al. 

2017). 

Bike Box 

Figure 7 shows an example of a bike box using colored pavement marking material. Dill, 

Monsere, and McNeil (2012) analyzed video of bicyclist behavior around intersections with bike 

boxes in Portland. They found that 73 percent of bicyclists used the bike boxes correctly, 

whereas 27 percent of queued motorists encroached on the bike box markings. A study 

conducted in Austin, TX, in 2010 showed that only 15 to 25 percent of bicyclists used the boxes 

as intended (Loskorn et al. 2013). 
14 

© 2022 Google® EarthTM.

Figure 7. Map. Sample intersection with bike box on the bottom approach. 

Through BL

Right-turning MVs can pose a threat to through bicyclists at traditional intersection designs. A 

resulting crash of this type is often called a “right-hook” crash. Bike through lanes demarcate a 

continuation of a BL through the intersection queuing area by extending the BL to the left of a 

dedicated RTL, as shown in figure 8. 

© 2022 Google® EarthTM.

Figure 8. Map. Sample intersection with keyway bike through lane (to the left of RTL). 
15 

Figure 9 shows an example of a different strategy to treat a BL in the approach: a combined lane 

for through or right-turn movements that mixes with the BL, which continues, marked by dashed

lines, into the mixing area on the approach to the intersection. Drivers are expected to pull close 

to the curb when making a right-turn, resulting in a mixing zone for motorists and bicyclists. 

© 2022 Google® EarthTM.

Figure 9. Map. Sample intersection with mixing zone for a BL, MV through lane, and 

right-turn movements. 

Keyways at through BLs allow bicyclists to position themselves in a conspicuous space before 

proceeding through, drawing attention to their intended movement to motorists in adjacent lanes 

(NACTO 2014). 

17 

CHAPTER 2. STUDY DESIGN AND STATISTICAL METHODOLOGY

Before full data collection efforts are undertaken, exploring the proposed statistical methodology 

to determine data requirements and identifying suitable datasets that can be used to conduct this 

analysis are necessary. This chapter reviews the study design, and chapter 3 introduces the data 

sources and variables explored for analysis.

Two basic designs for observational studies are frequently used in safety evaluations: 

• Cross sectional. 

• Before and after. 

A strong study design can significantly boost the quality of the results by carefully accounting 

for other influential variables, in addition to the variable of interest. Closely examining all 

potential data sources, their characteristics, and available data elements is critical. Ideally, this 

step should precede any data acquisition/collection and consider the needs of the analysis phase. 

Initially in this study, the research team identified potential data sources from which to gather 

key data elements available for evaluating each improvement. Specifically, sources for safety 

relevant variables, such as vehicle and bicycle exposure, were identified to be critical for a robust 

evaluation. Observational designs are needed in safety evaluations 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. A cross-sectional data analysis that does not include a matching or 

comparison group, and for which the sample is not the entire population, is considered an 

inferior preexperimental design and is sometimes called a static group comparison (Campbell 

and Stanley 1966). Likewise, if the before–after data are analyzed without any comparison 

group, the design quality (one-group pretest–posttest design) is negatively affected. These types 

of preexperimental designs have a higher potential for biased results. Therefore, this study 

initially targeted a quasi-experimental design to the extent possible, such as the nonequivalent 

comparison group (or comparison group) design or a control series design (e.g., Campbell and

Stanley 1966; Campbell and Russo 1999). However, in the case of evaluating the tradeoff of 

bicycle treatments at intersections versus the absence of these treatments, obtaining before–after 

data from multiple jurisdictions proved to be infeasible after potential data sources were

reviewed. Key variables to include in the study would be bicycle and MV volumes as these two 

variables represent exposure to the crash generation process. The research team developed a 

database for cross-sectional analysis with data collected at locations with available exposure 

data. The research team used PS weighting (PSW) methods to minimize imbalances between 

covariates when evaluating the safety effectiveness of various treatments. More details about 

these adjustment types are provided in the following section. 

STATISTICAL METHODS IN THE DATA MANAGEMENT PROCESS

The research team examined a variety of data sources identified in the preliminary stages of this 

research. One goal of this effort was to select candidate sites for study while balancing the 
18 

features of treated and comparison sites. This database development approach is consistent with 

the selected cross-sectional study design. 

The data management stage used for this study required the research team to examine multiple 

data sources, collect candidate data, identify data supplemental sources, integrate variables from 

multiple sources, and prepare the data for the statistical analyses. 

Data Extraction and Integration 

The research team used geographic information systems (GIS) tools (EsriTM 2019) to prepare, 

filter, and combine multiple datasets containing geolocation (typically in shapefile format). 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. 

Data Balancing 

Data-matching and -balancing methods are used to assist causal inference that quantifies the 

impact of a treatment on a response variable. The main principle behind this effort is to identify 

untreated locations that are similar in their covariates to the treated locations so that the contrast 

by the response variable implicitly controls for differences due to other covariates that could 

have an impact on the response variable. The matching of treated and comparison sites is based 

on the covariates identified to “covary” with the treatment variable. A good, balanced dataset is

one in which the means of the covariates are almost identical, carrying the implication that any 

observed differences between the treatment and comparison groups in the response variable are

due to the effect of treatment. The crashes on the selected untreated locations are then used as a 

proxy to estimate the counterfactual crashes on the treated locations—that is, the crash frequency 

that would have been observed if the treatment had not been applied. The quality of the matched 

dataset can be increased by matching one treated site with several comparison sites. The 

epidemiology literature recommends selecting between two and four control units per one treated 

unit (see Linden and Samuels 2013). 

PS Methods

More analytical approaches to guide the data-matching phase are based on PS. Under this 

framework, the PS of the treatment cases and their corresponding control cases are estimated and 

compared. The PS is a metric of similarity between covariates from the cases and can be 

estimated using parametric or nonparametric tools, such as logistic regression or random forest 

analysis (Sasidharan and Donnell 2013; Jovanis and Gross 2007; Guo and Fraser 2015). For 

example, a treatment of a through BL has a propensity to be applied on roadways with dedicated 

RTLs for vehicles. 

In the case of binary logistic regression as a basis for PS estimation, figure 10 shows the 

definition of the conditional probability of a site receiving treatment T. 

Figure 10. Equation. PS definition as logistic function of covariates. 
19 

Where:

P(Ti|Xi) = the PS denoting the probability of the ith site receiving the treatment T. 

Ti = the treatment status of the site ii, which takes binary values {0,1}. 

Xi = a vector of covariates that covary with the treatment presence. 

αi = the vector of coefficients through the binary logistic regression. 

In a balanced sample, the distribution of PS is expected to be similar for treated sites (P(TA|XA))

and comparison sites (P(TB|XB)). An examination of these differences at various stages of data 

collection can be used to direct collection of data at additional comparison sites to improve the 

balance in the dataset. 

An alternative to PS matching (PSM) is PSW. Under this approach, the PS remains the basis to 

balance two or more partitions of the data by the variable of interest (i.e., treatment or control). 

In contrast with PSM, balance is achieved by defining appropriate weights for each unit of 

analysis so they represent an underlying target population of sites. The data are weighted based 

on the probabilities of being in either the control or treatment group, and the selection of the 

weights defines the target population (Olmos and Govindasamy 2015). If all weights are equal, 

then the dataset is implied to be a simple random sample from a population of sites. That pool of 

sites is then implicitly defined as the target population. However, through the use of appropriate 

weights, more flexible definitions of the target population are possible, as can be found in the 

statistical literature (Olmos and Govindasamy 2015). In some cases, defining a theoretical 

population that is most suitable for inference makes sense. The definition of the weights also 

determines quantities that can be estimated, including the average treatment effect, the average 

treatment effect among the treated cases, the average treatment effect among the control cases, 

and the average treatment effect among the evenly matchable cases. 

STUDY DESIGN

Since the study design is cross sectional, team members collected and assembled a crosssectional database for estimating the CMFs of interest (intersection treatments). During data 

collection, comparison sites were sought and included to strengthen the design. Data collection 

required some metric that represented bicycle exposure so bicycle traffic could be included in the 

estimation of exposure. Ideally, this metric would be a direct bicyclist volume count, which was 

the case for some, but not all, sites. In cases where such a direct metric was not available, direct 

demand estimates of bicyclist volumes from appropriate models were incorporated instead. The 

team also decided to implement strategies to balance covariates accordingly. 

The research team decided to use PS weighting rather than matching methods (e.g., Stuart 2010) 

because matching is most suitable with large pools of potential sites. However, this approach is 

challenging in the case of small pools, as was the case for the databases in this research because

unfeasible locations were filtered out. Therefore, the research team subsequently decided to 

adopt the framework of PSW. The target population was set to be the overlap between the treated 

and control 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 either in the 

treatment group or in the comparison group. This approach effectively curbs the undue influence 

of two subsets of sites when the average treatment effect of the countermeasure is estimated: 
20 

• Comparison sites whose characteristics make the sites unlikely to be candidates for the 

treatment. 

• Treated sites with unusual characteristics for which no feasible comparison sites are 

represented in the data.

An additional advantage of this choice of target population is a desirable small-sample, 

exact-balance property, as demonstrated by Li, Morgan, and Zaslavsky (2018). Additionally, the 

corresponding weights are known to minimize the asymptotic variance of the weighted average 

treatment effect within their class of weights (Li, Morgan, and Zaslavsky 2018). 

DATA ANALYSIS METHODS

Team members conducted the data analyses using the statistical methods appropriate to the 

characteristics of the assembled datasets. The research team used appropriate generalized linear 

model (GLM) specifications (e.g., negative binomial, Poisson-lognormal mixture, logistic-log 

normal mixture) as needed by each dataset. PSW was implemented by using the PS obtained 

from the final datasets.

Generalized Linear Regression Analysis with PSM or PSW 

The predictive methods described in the Highway Safety Manual are based on cross-sectional 

statistical models named safety performance functions (SPFs) (AASHTO 2010). These models 

estimate the long-term expected crash frequency using statistical models derived from multiple 

sites with similar characteristics. In principle, the effect of a countermeasure can be estimated 

simply by comparing the counterfactual crash frequencies between treated sites and sites without 

the treatment, but the risk of that comparison is that the comparison group—implied by the SPF

predictions—may not necessarily be representative of sites that have the treatment under study. 

One way to reduce that risk is to develop the SPF based on a probability sample of the types of 

sites of interest. Another possibility is to develop the SPF from a complete population of sites 

(e.g., all sites in a State database inventory), whenever feasible. However, such alternatives are 

not possible or practical in every case. By using PS-based methods (PSM or PSW), the effect of 

a treatment can be studied by employing sites with treatment and matching untreated sites in a 

resulting dataset whose characteristics mimic those expected from a randomized sample

(Rosenbaum and Rubin 1984). Essentially, the PS methods ensure that the treated and untreated 

subsets of data are roughly orthogonal in their covariates, which should result in a nearly 

unbiased, not confounded estimate of the effect of interest. The effects of selection bias that can 

be otherwise present in developing the cross-sectional dataset are, thus, mitigated. 

Mixed-Effects Models

Within the frame of GLM methods, a distinction can be made between models with: 

• Fixed effects. 

• Random effects. 

• Mixed effects.
21 

Commonly, the coefficients obtained from GLMs can be thought of as fixed effects. The 

variables corresponding to fixed effects are implied to have time-invariant effects (e.g., roadway 

design elements). The model coefficients are estimated and interpreted as metrics of underlying 

parameters from a latent data-generating process.

In contrast, random-effects models estimate the effects of factors that are deemed the observed 

realizations of a random variable. Therefore, quantifying how the response variable shifts with 

the observed realizations in the dataset is typically not of interest, but rather accounting for the 

impact of such variation in the model is. The simplest analogy of random effects in a GLM is the 

use of blocking in analysis of variance designs. Typically, the effect of each block is not the 

focus of the analysis. However, accounting for the variability explained by the blocking to 

quantify the variability explained by the independent variable of interest is important. 

Mixed-effects models include both fixed and random effects (Pinheiro and Bates 2000). 

Generalized linear mixed models (GLMMs) approach the analysis of repeated-measures 

cross-sectional data by including a random effect per every unit of data aggregation (i.e., the 

blocking units in the data, such as individual study locations with more than one datum in the 

analysis). Orthogonal to the random effects, the model estimates fixed effects for the treatment 

and any additional fixed-effects covariates. As in GLM methods, an appropriate link function 

can be specified to permit the modeling of count data distributions that are applicable to crash 

data, such as Poisson and negative binomial. 

As described in the preceding section, Generalized Linear Regression Analysis with PSM or 

PSW, the use of PSM in the data collection stage can produce a more robust comparison, and the 

resulting PS can also be incorporated through PSW in the analysis stage, including the use of 

mixed-effects models.

The general functional form of the models is such that for a site i and year j, the model estimates 

the expected frequency for target crashes as in figure 11. 

Figure 11. Equation. Site-level Poisson distribution of yearly crashes. 

Where:

Nij = the number of target crashes at site i in year j. 

μij = the average yearly number of crashes at site i in year j. 

The yearly expectation of crashes is further parameterized as in figure 12. 

Figure 12. Equation. Parameterized yearly expectation of crashes. 

μμiiii = RREEii ∙ AAAAAATTmmaaiiiiii

aa1 ∙ AAAAAATTmmiimmiiiiaa2 ∙ AAAAAATTmmiimmiiii

aa3 ∙ eeeeee(XXii

′ ∙ ββ)
22 

Where: 

REi = the baseline crash expectation at site i (estimated as a random effect).

AADT_majij = the major average annual daily traffic (AADT) at site i and year j. 

AADT_minij = the minor AADT at site i and year j. 

ADBTij = the average daily bicycle traffic (ADBT) at site i and year j, respectively. 

Xi = the set of p independent variables (including BL) at site i.

α1, α2, α3, β = the set of model coefficients (estimated as fixed effects across the complete 

dataset).

Other variables are as previously defined. 

The set of REi is modeled to follow the lognormal distribution, with population-level parameters 

μ0 and σ0. Both these parameters are also subject to estimation by the model. The σ0 parameter is 

estimated as the standard deviation from the estimated REi values in the model link scale. This 

parameter is a measure of unaccounted variability between sites in excess of the variability 

attributable to the fixed effects and can be used to estimate the amount of Poisson overdispersion 

present in the data.

All model variables other than AADT and ADBT were included in the model in the exponential 

form, per the formulation. For clarity, the last term in figure 12 is implicit of multiple variables 

and can be expanded as in figure 13. 

Figure 13. Equation. Parameterization of explanatory variables in regression model. 

Where: 

X = an independent variable in the model, other than major and minor AADT and ADBT.

β = the corresponding estimated coefficient. 

CMF Estimation 

In most cases, the use of regression models to estimate the influence of a dependent variable 

consists of extracting a single parameter estimate and its standard error from the analysis after 

accounting for additional variability in the data due to covariates and an appropriately modeled 

error distribution. However, single-parameter estimation is not possible in every case, 

particularly when the estimation requires the use of more than one coefficient, as was the case 

for some treatments of interest in this report. To estimate the uncertainty of a CMF derived from 

multiple coefficients, the research team implemented the methods outlined in the following 

subsections. The appeal of these methods is that they leverage the asymptotically multivariate 

normal distribution expected from multiple variable model estimates obtained from maximum 

likelihood estimation (Booth and Hobert 1998; Morrell, Pearson, and Brant 1997; Wackerly 

Mendenhall, and Scheaffer 2008). 

CMF Estimates for Interventions with Multiple Effects

In general, the best fit models are not expected to produce a single coefficient estimate that 

answers the research question at hand: What is the change in safety performance in a facility 

XX′ ∙ ββ = XX1 ∙ ββ1 + XX2 ∙ ββ2 + ∙∙∙ +XXee ∙ ββee
23 

when a BL is accommodated while modifying lane width and shoulder width? If a model had a 

parameter for each of the three elements just described (i.e., BL, MV lane, and shoulder widths), 

the answer would depend on all three values. Additionally, the literature suggests that the 

construction of a BL tends to attract more bicycle traffic, so it might be of interest to compare the 

aggregate of safety effectiveness of the intervention for increased bicycle traffic as well

(Litman 2020; Manuel, El-Basyouny, and Islam 2014). 

Alternatives exist to consider these features explicitly. The ideal situation would be having 

volumes and dimensions for the periods before and after the construction of BLs for a sufficient 

number of sites. 

Scenario-Based Estimation 

Another alternative is the use of the coefficient estimates from each of the best models to 

develop crash predictions using appropriate linear combinations for select scenarios. Said linear 

combinations produce predicted crashes for both the before condition (e.g., no BL and baseline 

ADBT) and the after condition (e.g., BL, reduced lanes, and shoulders, and increased ADBT). A 

contrast between the predictions then yields the estimated CMF. In general, producing the CMF 

estimate for the scenario is straightforward. However, producing the corresponding standard 

error is a more complicated, but feasible, task. For a given scenario with variable vectors XA and 

XB representing the after and before conditions, respectively, of the safety influential variables in 

the model, and maximum likelihood model-inversed-information matrix, ∑, figure 14 gives the 

standard error for the contrast (i.e., CMF estimate) (Wackerly, Mendenhall, and Scheaffer 2008; 

Johnson and Wichern 2007). 

Figure 14. Equation. Standard error for contrast in log scale.

However, the potential problem with this approach is that it requires the analyst to exercise some 

judgment when determining the levels for all covariates in each scenario, as a specific 

combination of variable or covariate values possibly may not be present in the dataset, and 

therefore, such estimates carry an increased (but undetermined) uncertainty.

CHAPTER SUMMARY

This chapter describes the statistical methodology, analyses methods, and tools that the research 

team used in performing the work in this project. The rationale for a cross-sectional study design 

is presented, along with the use of PS methods to reduce the risk of biased estimates in 

cross-sectional designs. Finally, this chapter outlines statistical analysis methods to develop 

statistical models of crashes to be used in developing the CMFs of interest. GLMMs and their 

ability to manage Poisson overdispersion are discussed. The chapter ends with a discussion of 

how situations where the safety effectiveness estimate is not captured by a single coefficient in 

the crash models can be managed with additional procedures based on mathematical statistics

that can be applied to develop the required CMF estimates. The next chapter outlines the data 

collection effort for Virginia and Texas in more detail.

25 

CHAPTER 3. DATA COLLECTION AND INTEGRATION

The data elements needed to develop CMFs can be arranged into three major groups: 

1. Crash-related elements.

2. Roadway inventory elements. 

3. Traffic and bicycle volumes elements.

This chapter details the selection of data sources and locations for the study to build a database 

with these types of data for evaluation. To develop the database, the research team collected the 

following types of data elements:

• Intersection characteristics.

• Intersection leg characteristics.

• Bicycle and traffic counts. 

• Bicycle facility type (e.g., BL, buffered BL, SBL). 

• Multiple roadway design elements (e.g., functional class, number of lanes, and lane and 

shoulder widths). 

• Posted speed limit. 

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

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 collection protocol. The database consisted of two Excel® 

spreadsheets: one for intersection characteristics, and the other for features of each intersection 

leg. The intersection-level Excel table contains 14 variables to describe the geolocation, control 

type (signed or signalized), and bike treatment presented over each intersection, whereas the 

leg-level Excel table contains 35 variables to describe the roadway design characteristics and 

BL-related features of road segments connected with each intersection.

The collected data can be broadly classified into three categories (table 1), including: 

• Intersection characteristics category contains variables to describe the geolocation, 

control type (signed or signalized), and bike treatment presented for each intersection. 

Given the amount of sites with enough representation in this study’s datasets, the research 

team focused the analysis on three types of bicycle treatments at the intersection box: 

presence of chevron pavement markings in the BL across the intersection (figure 4), 

crossing markings across the intersection (figure 5), and two-stage left-turn pavement 

markings (figure 6). 

• 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 AADT values, and minor legs/streets

refer to the road segments with relatively lower AADT values. 
26 

• BL characteristics category contains BL-related variables to describe each intersection’s 

connecting BLs, such as the number of BLs and the existence of six types of BLs that

include through BLs (figure 8), buffered BLs (figure 2), colored BLs (figure 4, figure 6, 

figure 7, and figure 8), and separated BLs (figure 3). The evaluation focused on through 

lanes and bike boxes versus no BLs at the intersection, given the amount of data available 

for that evaluation. 

Table 1 lists the variables collected at each study location and their definitions. Samples of the 

intersection characteristics can be found in figure 4 through figure 9. Figure 4 shows intersection 

chevron pavement markings as well as white crossing markings indicating the BL path through 

the intersection. Figure 5 shows an example of the same crossing markings with added green 

colored markings for visibility. The third treatment evaluated was pavement markings for 

two-phase left turns, as shown in figure 6. These markings indicate the location for a left-turning 

bicyclist to wait on the far side of the intersection before proceeding with the left turn. The fourth

treatment evaluated was bike boxes, which are placed near the stop bar at signalized intersections 

to mark the location where bicyclists may safely congregate to get a head start when the signal 

turns green. Figure 7 shows an example of a bike box using colored pavement. The fifth 

treatment selected was through lanes, indicated by extension of pavement marking delineating 

the BL through the intersection to the left of a dedicated RTL, as shown in figure 8. 

Table 1. List of variables collected for each intersection. 

Variable 

Categories Variable Name Variable Description

Intersection 

characteristics 

(14 variables) 

ID Unique intersection number.

Y Latitude.

X Longitude.

Inter_CTL Intersection control types, including signalized, 

one-way stop, two-way stop, and four-way stop.

Treatment Whether any treatment at the intersection against 

the BL is present (binary: Y (yes) or N (no)). 

Treatments can be of four types: chevrons, 

crossing markings, two-stage turn queue box, 

and bike box.

Chevrons Whether any chevrons are present over the 

intersection (binary: Y or N). 

Chev_color Whether chevrons present are colored (binary: Y 

or N).

Cros_markings Whether crossing markings are present over the 

intersection (binary: Y or N).

2_Stage_TQBox Whether a two-stage turn queue box is present 

over the intersection (binary: Y or N).

Bike_box Whether the bike box is present over the 

intersection (binary: Y or N).
27 

Variable 

Categories Variable Name Variable Description

Inter_Length1 (ft) The edge length of the intersection longer 

edge—from one leg to its opposite leg (using the 

ruler tool in Google® EarthTM to measure the 

length with the unit in feet).

Inter_Length2 (ft) The edge length of the intersection longer 

edge—from one leg to its opposite leg (using the 

ruler tool in Google Earth to measure the length 

with the unit in feet).

Heading (degrees) The heading (angle) of driving on the leg based 

on compass directions. Value is in the range of 

0–360 degrees. 

Speed_Limit Driving speed limit (using street view and 

moving along the increasing milepost direction). 

Roadway design 

characteristics 

(21 variables) 

NumLegs Number of legs.

NumLegs_MJ Number of major legs.

NumLanes_MJ Average number of lanes for intersection’s 

major legs.

NumLanes_T_MJ Average number of through lanes for 

intersection’s major legs.

NumeLanes_LT_MJ Average number of LTLs for intersection’s 

major legs.

NumeLanes_RT_MJ Average number of RTLs for intersection’s 

major legs.

NumeLanes_TL_MJ Average number of shared through lanes and 

LTLs for intersection’s major legs.

NumeLanes_TR_MJ Average number of shared through lanes and 

RTLs for intersection’s major legs.

NumeLanes_LR_MJ Average number of shared LTLs and RTLs for 

intersection’s major legs.

NumeLanes_TLR_MJ Average number of shared through lanes, LTLs, 

and RTLs for intersection’s major legs.

NumLegs_MI Number of minor legs.

NumLanes_MI Average number of lanes for intersection’s 

minor legs.

NumLanes_T_MI Average number of through lanes for 

intersection’s minor legs.

NumeLanes_LT_MI Average number of LTLs for intersection’s 

minor legs.

NumeLanes_RT_MI Average number of RTLs for intersection’s 

minor legs.

NumeLanes_TL_MI Average number of shared through lanes and 

LTLs for intersection’s minor legs.
28 

Variable 

Categories Variable Name Variable Description

NumeLanes_TR_MI Average number of shared through lanes and 

RTLs for intersection’s minor legs.

NumeLanes_LR_MI Average number of shared LTLs and RTLs for 

intersection’s minor legs

NumeLanes_TLR_MI Average number of shared through lanes, LTLs, 

and RTLs for intersection’s minor legs.

Lane_width (ft) Average lane width. This width is determined by 

first measuring the surface width (i.e., excluding 

shoulders), and then this width is divided by the 

number of lanes (at each leg).

SideW_Wid (ft) Sidewalk width (at each leg).

BL 

characteristics 

(11 variables) 

Bike_Lanes Whether both major and minor legs have BLs, 

including MJ (major only), MI (minor only), or 

both.

NumBikeL_MJ Average number of BLs for intersection’s major 

legs.

NumBikeL_MI Average number of BLs for intersection’s minor 

legs.

Through_BikeL Number of legs with through BLs.

Parking_Nby Number of legs with any parking space adjacent 

to the BLs.

Buffered_BikeL Number of legs with buffered BLs.

Color_Pave Number of legs with colored BLs.

2_Way_Cyc Number of legs with two-way cycle tracks.

Ctra_Flow_BikeL Number of legs with contraflow BLs.

Protected_BikeL Number of legs with protected BLs.

Bike_L_Wid (ft) Average BL width (at each leg).

Obtaining enough bicycle volume data was a challenging task. Since bicyclist traffic does not 

necessarily follow the same travel patterns as passenger car traffic, estimating the bicycle 

volumes from a few hours of data has an inherent risk of producing biased estimates, and so 

actual large counts were preferred instead of estimates 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. 
29 

SUPPLEMENTAL DATA ON INTERSECTION APPROACHES

The researchers collected additional data on the features of each intersection approach to allow a 

more nuanced analysis on the different ways to handle BLs. The following codes and 

descriptions were defined, and the sites under study were coded accordingly: 

• Treat_1A: Approaches with an auxiliary RT lane where the BL is dropped in advance of 

the intersection. 

• Treat_1B: Same configuration as treatment 1A but includes sharrow (shared lane) 

markings on the RTL, and a plaque saying “EXCEPT BIKES” posted under the 

Right-Lane-Must-Turn-Right sign. 

• Treat_2A: Approaches with an auxiliary RT lane that maintains the BL to the intersection 

(with a keyway or pocket for the BL between the through lanes and the RT lane). 

Extension lines are shown in the weave area.

• Treat_2B: Similar to treatment 2A but does not include extension lines in the weave area.

• Treat_3: Approaches where the right lane can go through or make a right turn, and the 

BL is dashed in the mixing area on the approach to the intersection. Drivers are often 

supposed to pull close to the curb when making a right-turn, according to local laws, 

resulting in a mixing zone for motorists and bicyclists. 

• Treat_4: Approaches where the right lane becomes a drop lane. A pocket or keyway may 

be present. 

• Treat_5A: Similar to treatment 4 but the right lane drops at the intersection. In this case, 

the BL ends before the intersection. Sharrows are added in the RTL, and a plaque saying 

“EXCEPT BIKES” is installed under the Right-Lane-Must-Turn-Right sign. 

• Treat_5B: Same as treatment 5A, except there are no sharrows/sign for through bikes 

sharing the RTL. 

VIRGINIA 

Bicycle Count Data 

The researchers obtained the bicycle count data from the online repository (Eco-Counter 2022) 

supported by the Virginia Department of Transportation (VDOT), as recommended in 

communications with VDOT officials. A third-party company maintains and displays an online 

portal with bicycle and pedestrian usage data from counters across the State. The data are 

collected from both permanent and temporary sites with the help of counters and managed by 

personnel in the corresponding cities. The data are collected from several cities. The cities 

included in this data collection effort are as follows: 
30 

• Alexandria, VA. 

• Arlington, VA. 

• Blacksburg, VA. 

• Charlottesville, VA. 

• Richmond, VA. 

• Roanoke, VA. 

After eliminating locations at dedicated bicycle paths and other locations out of the scope of this 

evaluation, as well as 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 and enough data to estimate daily bicycle volumes. 

To estimate the overall ADBT, the research team used the total counts throughout the day. The 

research team first collected bike counters’ data using open data sources as identified for 2015 

through 2019. Then counters nearest to the intersections were linked to the study sites. 

Table 2 shows the descriptive statistics of the resulting database after leg-specific variables at the 

intersection level were aggregated. The average daily bicycle volume at the intersections ranged 

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

Table 2. 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

NumLanes (major) 2.32 1.12 2 1 6

Nlanes (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; Inter = intersection; L = lane; Wid = width; Cyc = bicycle.
31 

Table 3 shows the proportions of intersection control types represented in the Virginia dataset. 

Table 3. Virginia traffic control at intersections descriptive statistics (n=59 sites). 

Intersection Type Percentage in Dataset

One-way stop 25

Two-way stop 10

Three-way stop 0

Four-way stop 2

Signalized 59

Others 4

Note: Fifty-six percent of the testing intersections have four legs, 42 percent are 

T-type (three-leg) intersections, and 2 percent have five legs.

Table 3 shows that the majority of intersections are signalized. 

Crash Data 

The research team obtained crash data from Virginia’s online repository (VDOT 2021) to then 

integrate it with the geometry data described in the first section of this chapter, Bikeway Facility 

Type and Roadway Data. 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 bicycle counts collected, the research team filtered crashes to represent only 

the period from 2015 to 2019 (i.e., the same period represented in the bicycle volumes and 

geometry collected). After the intersection- and driveway-related crashes were removed, 

471 crashes remained for merging and analysis. Crash summary statistics are shown in table 4. 

Table 4. 2015–2019 Virginia crashes descriptive statistics (n=59 sites). 

Crash Type Mean Std Dev Median Min Max Total

Total 7.98 10.79 5 0 53 471

FI 2.66 4.14 1 0 23 157

Bike 0.41 1.05 0 0 7 24

AdvWeather 1.36 2.34 0 0 12 80

NonAdvWeather 6.63 8.73 4 0 42 391

FI = fatal and injury; Adv = adverse; NonAdv = nonadverse.

Nearly 40 percent of all crashes shown in table 4 were fatal and injury (FI). This table also shows 

that the proportion of crashes that occurred under adverse weather conditions is relatively small. 

TEXAS

For the final Texas database, the research team measured the intersection size and width of 

vehicle lanes, BLs, and sidewalks for each intersection’s connected legs, similar to the case of 

Virginia. The average lane width in the Texas study sites is 12.05 ft. The average sidewalk width 

is 6.78 ft. The average daily bicycle volume at the intersections ranged from roughly 17 to 660 

with an average of 109 (table 5). 
32 

Table 5. 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

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_marki 0.23 0.42 0 0 1

mj = major; mn = minor; Treat = treatment; Cros = cross; marki = markings.

In the Texas database, 60 percent of the collected intersections were signalized, 36 percent had 

stop signs installed, and 4 percent were neither signalized nor stop signed, as shown in table 6. 

Table 6. Texas descriptive statistics of traffic control at intersections (n=126 sites). 

Intersection Type Percentage in Dataset

One-way stop 25

Two-way stop 10

Three-way stop 1

Four-way stop 2

Signalized 58

Others 4

Note: Sixty percent of the testing intersections contain four legs, and 40 percent are 

T-type (three-leg) intersections. 

Bicycle Exposure Data 

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 initially intended 
33 

to apply direct demand models developed in Texas Department of Transportation Project 0-6927 

to estimate the bicycle counts from the crowdsourced database (Turner et al. 2019). 

This work collected count data from 124 roadway segments in 11 cities. The data can be 

visualized and queried from the Texas Bicycle and Pedestrian Count Locations Database (Texas 

DOT 2022). These data were collected during different periods from 2016 to 2017. Turner et al. 

(2019) integrated the site counts with the Strava sample and developed direct demand models to 

estimate the average annual daily bicycle counts. The ADBT model from that work was 

constructed using the three most important variables determined to be significantly associated 

with bicycle use:

• Strava sample counts.

• Type of roadway functional class (based on OpenStreetMap® definitions). 

• Density of high-income households in the given census block group (collected via the 

American Community Survey (U.S. Census Bureau 2022)). 

The research team decided to develop additional models from this data source that included more 

explanatory variables that better represented the types of facilities under study. The data from 

155 stations across Texas were examined and filtered for this effort. Ultimately, only 69 

locations were found to represent the types of sites under study in this research. The research 

team considered 47 variables in the modeling process, including Strava counts, weather 

conditions, housing, and demographic information obtained from the American Community 

Survey. Through model selection and cross-validation methods, the research team developed 

negative binomial models for ADBT as a response variable. Figure 15 shows the functional form 

of the selected model used in this research. All terms were found statistically significant at the 

5-percent level or higher. 

Figure 15. Equation. ADBT estimation equation developed from crowdsource data. 

As figure 15 shows, the most parsimonious model was found to include two count types from the 

Strava layer (total tips and commuting trips), the length of the Strava segment (Edge_km), and 

the percentages of college students and white population in the corresponding census tract. 

Crash Data 

The research team identified crashes that had occurred on selected intersections. 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 implies the assumption 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, per recommendations by Avelar, Dixon, and Escobar (2015). Team 

members then applied filters to remove crashes from adjacent locations. After identifying crashes 

corresponding to the facilities under study, filters were applied to remove 
34 

non-intersection-related crashes before the analysis (e.g., driveway related and non-intersection 

related). Table 7 shows the crash descriptive statistics in the Texas dataset.

Table 7. Texas 2016-2019 crash descriptive statistics (n=138 sites). 

Crash type Mean Std Dev Median Min Max Total

Total (crashes) 33.79 45.83 17 0 296 4,663

FI (crashes) 16.36 23.11 7.5 0 127 2,258

Bike (crashes) 0.22 0.59 0 0 3 30

Adv_Wth (crashes) 2.72 4.21 1 0 19 375

NonAdv_Wth (crashes) 31.07 42.55 14.5 0 277 4,288

Wth = weather.

Table 7 indicates more crashes in general and more crashes per intersection in Texas, compared 

with the Virginia numbers in table 4. Notably, the number of bicycle crashes are about the same 

in Texas as in Virginia, despite the larger sample size in Texas. Similar to Virginia, the vast 

majority of crashes are under no adverse weather conditions. 

CHAPTER SUMMARY

This chapter documents the process of selecting the States for evaluation, data elements, and data 

collection in general for the safety evaluation of BLs. Summary statistics are presented for the 

two databases developed: one for Virginia and one for Texas sites. Comparing the tables shows 

that more locations were available in Texas, with more crashes per intersection. Additionally, the 

tables show a similar number of bicycle crashes in Virginia and in Texas, despite the larger 

sample size for Texas. The chapter describes the data sources and data merging procedures 

followed to assemble the two State databases. The next chapter describes the statistical 

evaluations of these datasets that yielded CMF estimates for different safety treatments at 

intersections. 
35 

CHAPTER 4. SAFETY EFFECTIVENESS EVALUATION 

This chapter presents the results of the safety effectiveness evaluation and estimated CMFs. 

MODELING PROCESS

In general, the research team used entropy metrics (statistics that quantify the quality of 

information in the data, such as the Akaike information criterion and the 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, as 

shown in table 8. 

Table 8. Response variable and model specifications.

Response Variable Model Specifications Considered

Total crash frequency GLM and GLMM 

FI crash frequency GLM and GLMM

Bicycle crash frequency GLM and GLMM

The research team decided to perform the analysis separately by State, given the differences

observed in the descriptive statistics, especially on the average occurrence of bicycle crashes, 

which suggests potential underreporting for both States. 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. By separately performing 

the analysis, the research team could then identify potentially diverging trends and levels of 

accuracy between the two analyses. 

Virginia

Initially, the research team explored the balance between treated and comparison locations in the 

Virginia dataset. The team developed a PS model for the presence of any treatment under 

evaluation (e.g., BL, bicycle box, chevrons, crossing markings, or buffered BL). This analysis 

showed unbalances indicating overrepresentation on the treated sites for the following 

covariates: larger ADBTs, more lanes, and number of intersections where through and 

right-turning movements are combined into one lane. Conversely, comparison locations tended 

to have overrepresentations in the following variables: number of intersection legs, signal traffic 

control, and intersection length. Accordingly, the research team then developed overlap weights 

from the PS results as proposed by Li, Morgan, and Zaslavsky (2018). These weights are defined 

such that they represent the population of sites in the overlap of the two subsets. The plots in 

figure 16 and figure 17 demonstrate the impact of the weights on a key variable in the evaluation. 

Figure 16 shows the initial unbalance in this variable. 
36 

© 2022 Texas A&M Transportation Institute. 

Figure 16. Graph. Bicycle volume distributions by presence of intersection treatments in 

Virginia. 

In contrast, figure 17 shows the corresponding weighted distributions and the overlap 

distribution for bicycle volumes. 

Probability density (from 0.000 to 0.020 in increments of 0.005) of average daily bicycle traffic 

(ADBT) (from 0 to 300 in increments of 50). There are three lines on the graph for treated, 

comparison, and overlap. The comparison line increases from the y-axis to its maximum 

probability density value of approximately 0.012 over an ADBT value of approximately 70, 

decreases sharply to a point with probability density value of approximately 0.001 over an 

ADBT value of approximately 120, and then remains relatively flat until it reaches an ADBT 

value of 300. The treated line increases sharply from the origin to its maximum probability 

density value of approximately 0.013 above an ADBT value of approximately 70, tracking the 

comparison line closely. The treated line decreases to a probability density value of 

approximately 0.002 over an ADBT value of approximately 120, increases to a slightly higher 

probability density value of approximately 0.0055 above an ADBT value of 150, and then 

decreases to touch the x-axis at an ADBT value of approximately 180. The overlap line closely 

follows the treated line’s trend, being slightly higher at ADBT 70 with probability density value 

of approximately 0.014, and slightly lower than the treatment line at ADBT 150, with probability 

density value of approximately 0.003. 
37 

© 2022 Texas A&M Transportation Institute.

Figure 17. Graph. Weighted bicycle volume distributions by presence of treatments in 

Virginia.

Figure 17 shows that the application of PS weights balances the distributions, resulting in a more 

comparable contrast between the two data subgroups. The dashed line represents the overlap 

population, which is the distribution of sites with characteristics that make them nearly equally 

likely to be in either the treated group or the comparison group. This distribution of bicycle 

volumes is applicable for inference when the PS weights are applied. 

Because the Virginia dataset was small (59 sites), the research team analyzed the data 

disaggregated at the yearly level, allowing for yearly fluctuations to be captured by yearly 

random effects. 

Data Analysis Results—Virginia

Table 9 presents the estimates derived from the best fitting model to bicycle crashes in Virginia. 

As described in chapter 3, statistical models were fit for four crash responses defined in this 

dataset. The models for these analyses were GLMM with Poisson-lognormal mixture. This 

approach models site-to-site variability as a lognormal distribution, whereas the crashes within a 

site are modeled as a Poisson variable. Therefore, any Poisson overdispersion present in the data 

is captured in the variability of the random effects. An estimate of Poisson overdispersion can 

then be constructed and reported, analogous to the dispersion parameter in the negative binomial 

distribution. 
38 

Table 9. Coefficient estimates for bicycle crash prediction model in Virginia 

(n=236 intersection periods). 

Parameter

Variable 

Estimate Std Error z Value Pr (>|z|) Significance

(Intercept) −50.49 22.99 −2.196 0.0281 *

Signalized_Control 2.736 1.078 2.537 0.0112 *

log(MajADT) 4.334 1.691 2.562 0.0104 *

log(MinADT) 0.4791 1.409 0.34 0.7339 —

MinADT −0.0001 0.0002 −0.951 0.3415 —

log(ADBT+0.5) 1.146 0.588 1.949 0.0513 ~

NumLanes −0.5603 0.3551 −1.578 0.1146 —

NumBikeL −4.549 2.096 −2.17 0.03 *

Bike_L_Wid 0.5425 0.2539 2.137 0.0326 *

Cros_markings 1.653 0.7516 2.2 0.0278 *

Through_BikeL −0.5141 1.309 −0.393 0.6945 —

Buffered_BikeL 0.2443 2.687 0.091 0.9276 —

~Statistically significant at the 0.1 level.

*Statistically significant at the 0.05 level.

—Not statistically significant. 

Bicycle Crashes—Virginia

The first analysis the research team developed for Virginia was on bicycle crashes. Table 9

shows the model estimates for this model.

The model results shown in table 9 indicate a consistent safety performance link to major and

minor AADT and major ADBT. Additionally, statistically significant safety associations were 

found for signalized intersections and the width of any BLs in the approaches (within 200 ft from 

the intersection center). However, regarding the effects of the treatments, the implications are not 

straightforward, as multiple coefficients are involved in the estimation. Namely, the treatments 

are contingent on the presence and dimensions of BLs in the main approaches. 

The research team developed CMFs for the treatments by defining appropriate contrasts as 

explained in chapter 3. The results of this exercise are shown in table 10. 

Table 10. Bicycle crash CMF estimates for bicycle treatments at intersections in Virginia. 

Treatment CMF Estimate Std Error p Value Significance

BL Presenta 0.2740 −1.2947 1.3910 0.3520 —

Cross_Markingsa 1.4314 0.3586 1.5722 0.8196 —

Through_BikeLa 0.1639 −1.8088 1.2563 0.1499 —

Buffered BikeLanea 0.3498 −1.0503 2.7098 0.6983 —

—Not statistically significant. a

Base condition is no BL within 200 ft of the intersection on the main approaches. 
39 

All bicycle crash CMFs developed for bicycle crashes yielded statistically insignificant results. 

The implication of this outcome is that not enough evidence exists in the dataset to support a 

difference in safety due to the presence of these treatments at Virginia intersections.

Total Crashes—Virginia

Next, the research team fitted a mixed-effects model for total crashes. The model and uncertainty 

estimates for this model are shown in table 11. 

Table 11. Coefficient estimates for total crashes prediction model in Virginia 

(n=236 intersection periods). 

Parameter

Variable 

Estimate Std Error z Value Pr (>|z|) Significance

(Intercept) 14.93 7.788 1.917 0.05526 ~

log(MajADT+MinADT) −0.0185 0.0091 −2.03 0.04232 *

log(MinADT) −1.453 0.6627 −2.193 0.02831 *

MinADT −0.2864 0.1716 −1.668 0.09523 ~

NumLanes.min 0.2607 0.8469 0.308 0.75821 —

Lane_width 0.4407 0.2384 1.848 0.06458 ~

NumeLanes_TL 0.0001 0.0001 2.136 0.0327 *

NumBikeL −1.453 0.6627 −2.193 0.02831 *

Bike_L_Wid −0.2119 0.1392 −1.522 0.1279 —

Buffered_BikeL 0.2345 0.1538 1.525 0.12723 —

Through_BikeL −0.2119 0.1392 −1.522 0.1279 —

Cros_markiY 0.2345 0.1538 1.525 0.12723 —

Inter_Leng −1.92 1.062 −1.808 0.07059 ~

Inter_Leng:Inter_Le_1 −1.92 1.062 −1.808 0.07059 ~

~Statistically significant at the 0.1 level.

*Statistically significant at the 0.05 level.

—Not statistically significant. 

TL = turn lane; Y = yes; L, Leng, and Le = length.

The CMFs corresponding to the bicycle treatments are shown in table 12. The table shows that 

none of the estimates was found to be statistically significant.

Table 12. Total crash CMF estimates for bicycle treatments at intersections in Virginia. 

Treatment CMF Estimate Std Error p Value Significance

BL Presenta 0.9548 −0.0462 0.6902 0.9466 —

Cross_Markingsa 1.0278 0.0275 0.7831 0.9720 —

Through_BikeLa 1.1828 0.1679 0.4601 0.7151 —

Buffered BikeLanea 0.2654 −1.3266 0.8416 0.1150 —

—Not statistically significant. a

Base condition is no BL on the main approaches. 
40 

The results indicate statistically insignificant changes in crash frequency associated with the 

presence of bicycle treatments at intersections (i.e., the CMF is statistically equivalent to 1.0). 

This result is not surprising, given that the response variable in this analysis includes 

non-bicycle-related crashes, which are the majority of all crashes.

FI Crashes—Virginia

As in the prior analyses, the research team estimated the safety effectiveness for the intersection 

treatments with FI crashes as the response. Results are summarized in table 13. 

Table 13. Coefficient estimates for FI crashes prediction model in Virginia 

(n=236 intersection periods). 

Parameter Estimate Std Error z Value Pr (>|z|) Significance

(Intercept) −1.6903 3.654 −0.463 0.64365 —

Signalized 0.8247 0.3678 2.242 0.02495 *

log(MajADT+MinADT) 0.1195 0.2984 0.401 0.68877 —

log(ADBT) 0.4694 0.2013 2.332 0.01972 *

Inter_Leng −0.0128 0.0076 −1.688 0.09141 ~

Inter_Le_1 0.0173 0.0106 1.627 0.10366 —

NumeLanes_RT 0.2009 0.1754 1.145 0.25211 —

NumBikeL −2.0071 0.7148 −2.808 0.00499 **

Lane_width −0.1618 0.1473 −1.098 0.272 —

Bike_L_Wid 0.1522 0.1339 1.137 0.25548 —

Through_BikeL 0.9786 0.554 1.767 0.0773 ~

Cros_markiY 0.204 0.2685 0.76 0.44743 —

NumLanes 0.1622 0.212 0.765 0.44419 —

Buffered_BikeL 0.5545 1.1116 0.499 0.61789 —

~Statistically significant at the 0.1 level.

*Statistically significant at the 0.05 level.

**Statistically significant at the 0.01 level.

—Not statistically significant.

The CMFs corresponding to the bicycle treatments are shown in table 14. No result in this table 

showed statistical significance. However, it should be noted that the best fitting model in table 13

from which these CMFs are derived did include bicycle volume as exposure, a feature that 

suggests representation of bicycle involvement in the count of FI crashes. 
41 

Table 14. FI crash CMF estimates for bicycle treatments at intersections in Virginia. 

Treatment CMF Estimate Std Error p Value Significance

BL Presenta 0.3350 −1.0937 0.6807 0.1081 —

Cross_Markingsa 0.4108 −0.8897 0.7781 0.2529 —

Through_BikeLa 0.8913 −0.1151 0.4181 0.7832 —

Buffered BikeLanea 0.5833 −0.5391 1.0772 0.6167 —

—Not statistically significant. a

Base condition is no BL on the main approaches. 

Although all CMFs shown in table 14 are statistically insignificant, the CMF for BL presence is 

nearly statistically significant at the 90-percent confidence level (p value 0.1081). The 

magnitudes and directions of all other CMFs in this table are consistent with the hypothesis of a 

safety benefit of the bicycle treatments under evaluation, which in turn suggests that 

underreported bicycle crashes might be counted among the crashes analyzed. 

Discussion of Virginia Results

The research team estimated CMFs for bicycle intersection treatments with enough 

representation in the Virginia dataset. These calculations were assessed for three crash types: 

bicycle, total, and FI. Most CMF estimates were smaller than 1 but with corresponding standard 

errors, such that all CMFs produced using the data from Virginia were not statistically different 

from 1.0. In other words, these analyses could not establish statistical evidence supporting the 

expectation that adding bicycle treatments at intersections influences safety performance of these 

intersections. Besides the real possibility that there is no safety impact, the research team 

proposes two likely causes for these results: small sample size regarding both the number of 

locations and the number of bicycle crashes recorded at those locations. The second potential 

explanation is linked to the first. The number of bicycle crashes could be underreported, and as 

such, some bicycle crashes could be absent from the bicycle crash analysis or from all the 

analyses altogether. Finding bicycle volume as a statistically significant variable in the FI crash 

analysis is consistent with this potential issue.

Texas 

As with the Virginia dataset, the analysis of Texas data began with an examination to understand 

underlying relationships and to ensure proper representation of key variables. The team 

developed PS weights similar to those for Virginia to even out any remaining imbalances in the 

data before the analyses.

The overlap PS developed were based on a model on the presence of bicycle treatments at 

intersections. The overlap weights were developed as proposed by Li, Morgan, and Zaslavsky 

(2018). Results indicated a higher likelihood of treatment presence was linked to the following 

variables: major and minor AADT, number of lanes in the minor approach, crossing distance on 

the major legs, presence of number of RTL and LTLs, and sidewalk width. Figure 18 shows the 

distributions of major AADT by presence of bicycle treatments at the Texas intersections under 

evaluation to demonstrate the balancing effect of the developed PS weights. 
42 

© 2022 Texas A&M Transportation Institute.

Figure 18. Graph. Major AADT width distributions by bicycle intersection treatments in 

Texas. 

Figure 18 shows a mild imbalance. Among the sites with bicycle treatments, the distribution is 

flatter and broader, compared with the comparison sites that have a larger mode at low volumes. 

After PS weights were applied, the comparison by the presence of a bicycle treatment becomes 

more balanced for this covariate, as depicted in figure 19. The distribution of the overlap 

population is also shown in this figure as a dashed line. 

 © 2022 Texas A&M Transportation Institute.

Figure 19. Graph. Major AADT weighted distributions in Texas. 
43 

The main changes in AADT distributions are additional weights for treated sites with major 

AADTs from 10,000 to 25,000 vehicles per day (vpd) and lower weights at comparison sites for 

AADTs larger than 35,000 vpd. 

Data Analysis Results—Texas 

In contrast with Virginia analyses, the models used for Texas were negative binomial GLMs. 

These types of models were preferred in this case due to their increased efficiency and 

availability of straightforward diagnostic tools, coupled with a sufficiently large pool of 

intersections for analysis.

For the CMF development, the research team followed a similar approach to the one used in the 

Virginia analyses. Safety performance models accounted for documented safety performance 

influential variables, such as major and minor AADTs and estimated bicycle volumes, while 

producing marginal accounts for the presence of bicycle treatments at intersections.

Total Crashes—Texas 

Next, the research team fitted a GLM for three crash types at the Texas sites. The coefficients

and uncertainty estimates for this model are shown in table 15. 

Table 15. Coefficient estimates for total crashes prediction model in Texas 

(n=126 intersections). 

Parameter Estimate Std Error z Value Pr (>|z|) Significance

(Intercept) −0.098 1.224 −0.080 0.936 —

Inter_CTL=“Signalized” 1.016 0.251 4.048 <0.001 ***

log(AADT.min) 0.112 0.076 1.480 0.139 —

log(AADT.maj) −0.040 0.135 −0.297 0.766 —

log(Maj.ADBT+

Min.ADBT) 0.141 0.095 1.492 0.136 —

Nlanes 0.512 0.126 4.074 <0.001 ***

TreatmentY −0.564 0.356 −1.586 0.113 —

Treat_3 0.169 0.142 1.185 0.236 —

Treat_2A 0.084 0.250 0.336 0.737 —

Separated_BikeL −0.030 0.107 −0.276 0.782 —

***Statistically significant at the 0.001 level.

—Not statistically significant.

The statistically significant coefficients in table 15 indicate associations of type of control and 

geometry to safety as anticipated. The CMFs corresponding to the bicycle treatments that 

remained in the most parsimonious model are shown in table 16. 
44 

Table 16. Total crash CMF estimates for bicycle treatments at intersections in Texas. 

Treatment CMF Estimate

Std 

Error p Value Significance

Separated BLa 0.552 −0.593 0.345 0.086 ~

BL mixed with through and RT 

lanea 0.674 −0.395 0.251 0.116 —

Through_BikeL_with_keywaya 0.619 −0.480 0.367 0.191 —

~Statistically significant at the 0.1 level.

—Not statistically significant. a

Base condition is no BL on the main approaches. 

Only for SBL did the results indicate an associated statistically significant safety improvement. 

The number of total crashes at intersections with SBL was estimated as 44.8 percent smaller than 

at intersections without BLs, after accounting for exposure, type of traffic control, and number of 

lanes. 

Fatal and Injury Crashes—Texas 

The research team estimated the safety effectiveness of intersection treatments as measured by FI

crashes next. Table 17 shows this result. 

Table 17. Coefficient estimates for FI crash prediction model in Texas 

(n=126 intersections). 

Parameter Estimate

Std 

Error z Value Pr (>|z|) Significance

(Intercept) 0.9206 1.845 0.499 0.61778 —

I(Inter_CTL=“Signalized”)

TRUE 0.9227 0.2635 3.502 0.000462 ***

log(AADT.mj+AADT.mn) 0.0238 0.1514 0.157 0.875062 —

log(Maj.ADBT+Min.ADBT) 0.1216 0.1025 1.186 0.235446 —

AADT.mn 5.00E-05 2.00E-05 2.602 0.009273 **

Nlanes.mx 0.3486 0.1369 2.547 0.010865 *

TreatmentY –0.7921 0.3956 –2.002 0.045271 *

Treat_3 0.2324 0.1677 1.386 0.165888 —

Treat_4 0.3861 0.3528 1.094 0.273772 —

Lane_width –0.0968 0.0687 –1.41 0.158581 —

*Statistically significant at the 0.05 level.

**Statistically significant at the 0.01 level.

***Statistically significant at the 0.001 level.

—Not statistically significant.

The FI crash CMFs corresponding to the relevant bicycle treatments in the model are shown in 

table 18. Out of the two treatments, only BL next to RT lane showed statistical significance at 

the 95-percent confidence level. 
45 

Table 18. FI crash CMF estimates for bicycle treatments at intersections in Texas. 

Treatment CMF Estimate

Std 

Error p Value Significance

BL mixed with Through and RT 

lanea 0.571 –0.560 0.264 0.034 *

Through_BikeL_with_keywaya 0.666 –0.406 0.373 0.277 —

—Not statistically significant.

*Statistically significant at the 0.05 level.

a

Base condition is no BL on the main approaches. 

Non-Weather-Related Crashes—Texas 

The research team next estimated the safety effectiveness of intersection treatments as measured 

by nonadverse crashes since, logically, bicyclists would avoid riding in adverse weather

conditions. The premise is that this subset of crashes should have a larger proportion of crashes 

involving bicyclists, thus showing sensitivity to the treatments intended for bicyclists. Table 19

shows this result. 

Table 19. Coefficient estimates for non-weather-related crash prediction model in Texas 

(n=126 intersections). 

Parameter Estimate

Std 

Error z Value Pr (>|z|) Significance

(Intercept) 0.3192 1.8108 0.176 0.860063 —

Inter_CTL=“Signalized” 1.0259 0.2497 4.108 0.0000399 ***

log(AADT.mj+AADT.mn) 0.0461 0.1372 0.336 0.736533 —

log(Maj.ADBT+Min.ADBT) 0.4285 0.1807 2.371 0.017747 *

Min.ADBT –0.003 0.0017 –1.793 0.072958 ~

Nlanes 0.4264 0.1228 3.474 0.000514 ***

Lane_width –0.115 0.0629 –1.83 0.067302 ~

TreatmentY –0.5614 0.2234 –2.513 0.011977 *

Treat_4 0.0599 0.2986 0.2 0.841094 —

Chev_ColorY 0.7132 0.3102 2.299 0.021505 *

Separated_BikeLRA –0.2241 0.1256 –1.784 0.074363 ~

~Statistically significant at the 0.1 level.

*Statistically significant at the 0.05 level.

***Statistically significant at the 0.001 level.

—Not statistically significant.

Chev = chevron.

The non-weather-related CMFs corresponding to the bicycle treatments are shown in table 20. 

Similar to the total crash analysis, only the treatment of SBL showed statistical significance at 

the 99-percent level. 
46 

Table 20. Non-weather-related crash CMF estimates for bicycle treatments at intersections

in Texas. 

Treatment CMF Estimate

Std 

Error p Value Significance

Separated BLa 0.456 –0.786 0.253 0.002 **

BL mixed with Through and RT 

lanea 0.606 –0.502 0.316 0.112 — 

Through_BikeL_with_keyway 

and colored chevrons with 

extension linesa

1.236 0.212 0.422 0.616 — 

a

Base condition is no BL on the main approaches. 

**Statistically significant at the 0.01 level.

—Not statistically significant.

Table 20 shows one statistically significant CMF for the presence of buffered BLs (figure 3). The 

analysis indicates that this treatment is associated with a reduction of 54.4 percent in 

non-weather-related crashes, after controlling for vehicle exposure, bicycle exposure, and other 

intersection related factors. 

Discussion of Results

The research team estimated CMFs for bicycle intersection treatments with enough 

representation in the Texas dataset, similar to the Virginia dataset. These calculations were 

assessed for three crash types: total, FI, and non-weather-related crashes. The later crash type 

was analyzed under the premise that a larger proportion of these crashes could be bicycle-related 

but not coded as such. The corresponding standard errors suggest that three CMFs produced 

using the Texas datasets were statistically different from 1.0. The analysis found that the use of 

SBLs at intersection is linked to total and non-weather crash reductions of 44.8 and 55.4 percent, 

respectively. The other statistically significant result was found for configuring the approaching 

BL to the right of a combined through and right-turn MV lane, as shown in figure 9. However, 

the small number of crashes explicitly flagged as bicycle related suggests an important number 

of underreported or uncoded bicycle crashes for the State of Texas. Interestingly, ADBT 

variables were found to be important in explaining the variability of the three types of crashes 

analyzed. This feature of the analyses supports the hypothesis of an important part of the crashes 

analyzed being potentially bicycle related. 

CHAPTER SUMMARY

This chapter documents the statistical evaluations and steps taken to develop CMFs using the 

databases from Virginia and Texas, the two States represented in this study. Separate analyses 

were implemented for each dataset to develop statistical models for three crash types: bicycle, 

total, and FI for Virginia, and total, FI, and non-weather-related for Texas. Using the model 

estimates, the research team computed CMFs corresponding to the safety countermeasures under 

evaluation (i.e., bicycle treatments at intersections). Only three statistically significant results in 

Texas were found in these analyses: estimated crash reductions in total and non-weather-related

crashes for SBL at intersections and for mixing zones (figure 9), for accommodating BLs at 

intersections. Chapter 5 outlines the benefit–cost (B/C) evaluation of these safety improvement.
47 

CHAPTER 5. ECONOMIC ANALYSIS

The research team conducted an economic analysis to estimate B/C ratios for implementing the 

bicycle treatments that showed statistically significant results. The Texas CMF results yielded 

three estimates that were statistically significant for total, FI, and non-weather-related crashes in 

Texas. Although other estimates from Virginia and Texas were found to be statistically 

insignificant, many of the treatments generally indicated a trend for reducing crashes. For the 

economic evaluation, the research team focused on the two treatments that yielded statistically 

significant results. 

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

Federal Highway Administration’s (FHWA) technical document entitled Highway Safety 

Benefit–Cost Analysis Guide (Lawrence et al. 2018). Also, the value of a statistical life (VSL) 

was obtained from the most recent memorandum on the U.S. Department of Transportation 

website (Putnam and Coes 2021). The recommended range for VSL was $10.9 million in 2019, 

the most recent year included in the evaluations of this study. 

According to the Pedestrian and Bicycle Information Center (FHWA 2015), the cost of adding 

BLs varies depending on the project details. This source argues that reducing the width of MV

lanes by adding BLs cost at least $5,000 per mile. However, this cost varies widely based on the 

pavement condition, which involves the intersection treatment in this assessment. Past research 

has reported the costs for individual elements included in bicycle treatments at intersections 

(Weigand, McNeil, and Dill 2013). The authors reported $200 for each pedestrian and bicycle 

crossing sign, $5,000 per bike box, up to $6.35 per foot of BL, up to $9.33 per foot of buffered 

BL, up to $340 for a chevron marking, and $0.62 for each foot of pavement line removal. The 

value reported by Weigand, McNeil, and Dill (2013) for adding a BL by reducing MV lane 

widths is given in 2013 dollars and must be converted to 2019 dollars before it can be used in the 

economic analysis. Based on publicly available consumer price index data, the research team 

estimates a cumulative inflation rate of 9.7 percent between 2013 and 2019. Regarding duration, 

FHWA reports that methyl methacrylate acryline markings tend to last up to 8 yr, whereas 

thermoplastic or paint requires more frequent maintenance (FHWA 2015). Wilson (2020) 

estimated the cost of SBLs could vary from $133,170 to $536,680 per mile in 2020, depending 

on construction materials and specific design needs. For the purposes of this analysis, a value of 

$150,000 per mile was assumed for 2019. 

Additionally, the base condition of the CMFs of interest is no BL arriving at the intersection. 

Although acquisition of the right of way (ROW) and construction of additional lanes might not 

be necessary to convert such an intersection, the research team carried calculations considering 

and not considering acquisition of the ROW. A 2022 report from Hillsboro, OR, shows cost 

estimates for various intersection improvement projects approved in that city (David Evans and 

Assocciates Inc. 2022). A similar intersection project that adds turn lanes and reconfigures the 

existing lanes is reported to have a cost of $1.4 million in 2022. Considering the cumulative 

inflation between 2019 and 2022 of 12.5 percent, the estimated cost of such a project in 2019 is 

$1.24 million. These improvement projects typically have a useful life of 20 yr. Therefore, the 

economic analysis will be based on a 20-yr horizon. 
48 

COST OF CRASHES AND B/C RATIO

To estimate the benefit of SBL, the team calculated the average cost of a non-weather-related

crash in Texas as $106,972 in 2019. Considering a period of analysis of 20 yr and a reduction of 

94.76 non-weather-related crashes in that period (per a 0.456 CMF (table 20) applied to a total of 

7.77 crashes per year times 20 yr), the benefit of implementing SBLs on the main road was 

estimated as $9,040,250 in 2019 dollars (84.51 prevented crashes times $106,972 per crash). 

To estimate B/C ratios, the research team calculated the cost of installing SBLs for a period of 

20 yr. With a construction cost of $150,000 in 2019, the cost of restriping for 200 ft of the two 

opposite main approaches is estimated as $7,302.5 from the work of Weigand, McNeil, and Dill 

(2013). Assuming restriping and replacing of vertical element separators is needed every 5 yr 

(thus occurring four times in the life of the project), the cost of maintenance is estimated as 

$42,918=10,729×20/5. Thus, the total project cost of the improvement is estimated as $292,918 

if no additional ROW purchase is necessary. If ROW should be acquired, the total project cost 

escalates to $15,537,362. Finally, the B/C ratio of adding SBLs is 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 RT movement for the MVs 

and the BLs (figure 9). In this case, the estimate in table 18 indicates a reduction of 42.9 percent 

in FI crashes. The research team estimated that an FI crash costs $105,676 on average in the 

corresponding dataset. Given an average of 4.06 FI crashes per year, the benefit is then estimated 

as $3,708,397 in 20 yr. Assuming that the treatment will not require acquiring additional ROW, 

the combined cost of initial construction and maintenance over the period of analysis was 

estimated as $32,707. Therefore, the B/C ratio of this strategy was estimated as 113.3. 

CHAPTER SUMMARY

This chapter describes the economic analysis performed to estimate the economic effectiveness 

of implementing bicycle through lanes at intersections. The benefit calculation was derived from 

the nonadverse weather crash CMF for SBL in Texas, as well as the FI CMF for mixing the BL 

with through and right turns at intersection approaches. These CMFs were found statistically 

significant in the analyses of chapter 4, which outlines the resources and assumptions involved in 

developing the B/C ratio. With a total benefit of $9.05 million and a total cost of $1.6 million, 

the economic evaluation yielded a B/C ratio of 5.9 for this intersection treatment when assuming 

acquisition of additional ROW. If no additional ROW is necessary, the B/C ratio increases to 

30.9. In the case of providing a mixing zone between the BL, the through, and the right-turning 

MV movements, the benefit value in terms of a reduction of nonadverse weather crashes was 

estimated as $3.7 million. The cost of reconfiguring the approaches was estimated as $32,707, 

which yielded a B/C ratio of 113.3. 

All three B/C results indicate larger benefits than costs expected from the assessed types of 

implementations. Chapter 6 presents the summary and conclusions for this safety evaluation. 
49 

CHAPTER 6. SUMMARY AND 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, FI, and nonadverse weather crashes. 

Safety data collection was guided by the availability and location of available 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 MV 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 MVs at the approach with regard to FI 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 FI crashes (0.571 CMF, 0.034 p value). The research team estimated the B/C 

ratio in this case as 113.3 when assuming no additional ROW is required to construct and 

maintain this treatment. 

51 

ACKNOWLEDGMENTS 

The research team would like to thank Stephen Read at the Virginia DOT for providing contact 

information and guidance on the use of bicycle count data from Virginia. The research team also 

thanks Brooke Struve from FHWA for providing guidance on geometric design features of the 

evaluations. Additionally, the research team acknowledges and thanks the Washington DOT for 

providing preliminary data that was initially considered for this evaluation.

53 

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HRSO-20/08-23(WEB)E Recycled

Recyclable

Recommended citation: Federal Highway Administration, Development of Crash 

Modification Factors for Bicycle Treatments at Intersections (Washington, DC: 2023) 

https://doi.org/10.21949/1521963