Development of Crash 

Modification Factors for 

Wrong-Way Driving Treatments

FHWA Publication No.: FHWA-HRT-22-112

FHWA Contact: Carol H. Tan, Ph.D. 

(ORCID: 0000-0002-0549-9782), HRSO-20, Team 

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

carol.tan@dot.gov

This document is a technical summary of the FHWA 

report, Developing Crash Modification Factors for 

Wrong-Way-Driving Countermeasures (FHWA-HRT-22-115).

INTRODUCTION

The Federal Highway Administration (FHWA) Development of Crash 

Modification Factors (DCMF) Program was established in 2012 to address 

highway safety research needs for evaluating new and innovative safety 

strategies (improvements) by developing reliable quantitative estimates of their 

effectiveness in reducing crashes (FHWA 2022a). Forty State departments of 

transportation (DOTs) provided technical feedback on safety improvements to 

the DCMF Program and implemented new safety improvements to facilitate 

evaluations. These States are members of the Evaluation of Low-Cost Safety 

Improvements Pooled Fund Study (ELCSI PFS) (FHWA 2022b), which 

functions under the DCMF Program.

This project evaluated the safety effectiveness of geometry and access 

management modifications and the installation of traffic control devices (TCDs) 

implemented as safety treatments against wrong-way driving (WWD) crashes at 

freeway locations. The ELCSI-PFS Technical Advisory Committee selected the 

safety evaluation of WWD treatments as one of the priorities of the PFS.

Study Objective

This evaluation assessed the potential to reduce WWD crashes of geometry 

changes, access management modifications, and deploy TCDs by developing 

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

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

decisionmaking in the project development and safety planning processes.

BACKGROUND

WWD crashes represent a small portion of the total crashes on freeways 

and highways, but because most of these crashes are higher-speed, 

head-on collisions, they result in more fatalities than do other crash types. 

Multiple past efforts have documented implementation and testing of various 

strategies and devices to reduce wrong-way movements. Researchers have 

studied four main types of countermeasures:

• Geometric design elements, such as channelization and access 

management strategies.

• Conventional TCDs, such as signs, pavement markings, and signals.

TECHBRIEF

Research, Development, 

and Technology

Turner-Fairbank Highway 

Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

https://highways.dot.gov/research
2

• Enhanced TCDs, such as oversized signs, additional 

signs, low-placed signs, and retroreflective strips 

on signposts.

• Intelligent transportation system strategies, 

including detection, active warning, and driver 

notification components.

WWD crashes occur as a result of one or more vehicles 

traveling in the opposite direction of the legal traffic 

flow. The act of driving in the opposite direction might be 

intentional or unintentional, but the definition of WWD 

crashes excludes crashes resulting from median crossover 

encroachment (FHWA 2019).

In the United States, WWD crash studies were conducted 

as early as the 1960s in States such as California 

(Tamburri and Theobald 1965). However, in the early 

2000s, WWD crash studies gained attention from 

researchers in the United States and internationally. In 

general, past studies showed that WWD crashes normally 

represent a small percentage of all crashes: The special 

investigation performed by the National Transportation 

Safety Board (NTSB) (2012) found that WWD crashes 

normally make up less than 3 percent of all crashes. 

These crashes, however, tend to be more severe because 

the collisions are often head-on impacts (Vaswani 1973, 

1977; Cooner, Cothron, and Ranft 2004; Finley et al. 2014; 

Tamburri and Theobald 1965).

WWD Crash Characteristics

The locations and characteristics of WWD crashes are 

crucial elements for evaluating the strategies to position 

potential countermeasures. A literature review revealed 

the following categories for crash locations:

• Land use setting (rural versus urban).

• Facility type (freeway versus arterial).

• Location within the facility (ramp, intersection, 

midblock, or main lanes).

• Location with respect to the travel lane (turning lane, 

through lane, left lane, etc.).

Studies show that WWD crashes are more likely to 

occur in urban areas. According to one nationwide study, 

approximately 57 percent of WWD crashes occurred in 

urban areas (Baratian Ghorghi, Zhou, and Shaw 2014). In 

one statewide study for Arizona, the results were slightly 

lower than at the national level, with approximately 

53 percent of WWD crashes occurring in urban areas 

(Simpson and Bruggeman 2015). With respect to facility 

types, some studies tend to clearly indicate that WWD 

crashes tend to occur mostly on freeways compared 

to arterials and other primary highways, whereas 

other studies indicate a shift toward arterial roadways 

(Tamburri and Theobald 1965; Copelan 1989; Vaswani 

1977; Ponnaluri 2016).

Studies that identified the origin of WWD crashes on 

freeways revealed the following sites are the primary 

locations where wrong-way traveling vehicles originate 

(Lathrop, Dick, and Nolte 2010; Morena and Leix 2012; 

Zhou et al. 2012; Kayes et al. 2019):

• Entrance and exit ramps.

• U-turns.

• Partial cloverleafs.

• Diamond interchanges.

• Incomplete or partial interchanges.

• Compressed diamond interchanges.

Exit ramps accounted for 5 of 10 WWD crashes in 

New Mexico and 31 of 110 in Michigan (Lathrop, Dick, 

and Nolte 2010; Morena and Leix 2012). Two recent 

studies added several characteristics of exit ramps that are 

associated with WWD vehicle entry (Kayes et al. 2019; 

Atiquzzaman and Zhou 2018):

• Exit ramps with obtuse or right angles.

• Exit ramps with channelized islands between lanes.

• Exit ramps that have multiple lanes.

Partial cloverleaf interchanges represented approximately 

61 percent of 31 crashes in Michigan and approximately 

11 percent of 47 crashes in Illinois (Morena and Leix 2012; 

Zhou et al. 2012). In addition, the presence of a U-turn 

accounted for approximately 33 percent (4 of 12) and 

approximately 30 percent of crashes in New Mexico and 

Michigan, respectively (Lathrop, Dick, and Nolte, 2010; 

Morena and Leix, 2012).

TCDs

A recent study on divided highways found the following 

four strategies appear to deter WWD movements 

(Finley et al. 2018):

• DO NOT ENTER (DNE) and WRONG WAY (WW) 

signs on the outside of a wrong-way turn.

• Wrong-way arrow markings for the through lanes on 

the divided highway.

• The presence of a centerline in the median opening.

• Stop or yield lines when interior right-of-way 

treatments are provided.

These strategies could also be effective at preventing 

WWD events at high-speed divided highways 

and freeways.

Other countermeasures that have been of interest to 

researchers are flashing-based countermeasures, which 

include light-emitting diode (LED) signs and red rectangular 

flashing beacon (RFB) signs. Ozkul and Lin (2017) 

analyzed public opinion surveys and video data collected 
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from six I–275 off-ramps in Tampa, FL, and concluded 

that the red rectangular rapid flashing beacons (RRFBs) are 

effective countermeasures to WWD incidents. According to 

their 6-mo video data, between 60 percent and 85 percent 

of the WWD vehicles performed corrective maneuvers 

when the red RRFBs were activated. Kayes, Al-Deek, and 

Sandt (2020) reported the percentage of self-corrected 

drivers was higher for RFB signs (69.4 percent) than for 

LED signs (48.1 percent) in comparison to ramps with 

nonflashing WWD countermeasures. Another study that 

compared the performance of two countermeasures for 

WWD—LED signs and RFB signs on I–70 in Florida—

found that greater than 77 percent of the WWD incidents at 

the sites with RFB self-corrected, whereas approximately 

only 14 percent of the incidents self-corrected at sites with 

LED (Kayes et al. 2018).

STUDY DESIGN

The research team collected and assembled data for 

a cross-sectional estimation of the CMFs of interest 

(geometry and access management modifications, and 

installation of TCDs at freeway entry points). The study 

design was retrospective, whereby a robust database of 

case corridors (i.e., corridors in the vicinity of WWD 

crash locations) was supplemented with noncase corridors 

(i.e., corridors with no record of WWD crashes assembled 

in a similar way as the case corridors). The research team 

also decided to implement propensity score (PS)-based 

strategies to balance covariates in the analysis.

A cross-sectional retrospective evaluation enabled a 

large database to be developed. However, a retrospective 

analysis has the following important implications for 

the data collection and analysis stages:

• The countermeasures that can be evaluated 

are determined after the data collection, when 

an assessment of the prevalence of potential 

countermeasures is possible, because the data 

are collected on the basis of the history of WWD 

crashes rather than on the site characteristics.

• The retrospective distribution of variables 

(including the outcome variable) depends on the 

ratio of case-to-noncase locations and is different 

from the unconditional distributions obtained from a 

prospective design. Therefore, statistical methods used 

in risk analysis are appropriate to analyze retrospective 

designs because they produce odds ratios known to 

be independent of the two design types discussed. 

The unit of analysis for the evaluation was individual 

points of entry (POEs) because the countermeasures 

under evaluation would be identified at these locations. 

This decision required additional analytical efforts in 

sequence because the data collected included cases 

in which uncertainty existed about the true POE 

associated with each crash corridor.

DATA AND ANALYSIS METHODS

The empirical analyses were conducted using the 

statistical methods appropriate to the characteristics of the 

assembled datasets. The research team used generalized 

linear mixed model variants (binomial mixed) to obtain 

the safety effectiveness estimates of interest. For the 

analysis, the research team adopted the framework of 

PS weighting, setting the target population of sites at 

the overlap population as proposed by Li, Morgan, and 

Zaslavsky (2018). This choice of target population has a 

desirable small-sample exact balance property, and the 

corresponding weights minimize the asymptotic variance 

of the weighted average treatment effect within their class 

of weights (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 upper quartile group 

(in terms of number of signs) or in the lower quartile group.

When estimating the average treatment effect of the 

countermeasure, this approach effectively curbs undue 

comparisons of sites from the lower quartile that are 

unlikely or ineligible to be in the upper quartile.

Due to the limitation of having a moderate two-State 

database with a relatively small number of crashes with 

confirmed POEs, the analyses were performed in two 

phases. In the first phase, only the subset of corridors that 

had crashes with confirmed POEs were considered. In the 

second phase, the analyses encompassed the larger database 

of corridors that had both crashes with known POEs and 

crashes with sets of their potential POEs weighted by the 

factors found linked to crash risk in the first phase.

DATA

The following three common data categories are required 

for crash-based evaluations:

• Crash data (including WWD flags and variables).

• Roadway inventory data.

• Traffic data.

Initially, the research team examined a variety of data 

sources and potential study locations that could yield 

enough data for the evaluation. The team procured 

candidate locations that would permit at least a few 

hundred WWD crashes for analysis, to be consistent 

with the selected retrospective cross-sectional study 

design. Table 1 shows the data sources that were pursued 

from Texas and Florida, the two States selected for the 

evaluation. The data sources in Florida are unrestricted, 

whereas access to Texas crash data is not available to 

the general public. However, the Texas DOT (TxDOT) 

allowed the research team access to these data sources.

For data manipulation and integration, the research team 

worked with GIS software (EsriTM 2019), spreadsheets, 
4

text files, and relational database software, where 

appropriate. Crash and countermeasure geolocations were 

the keys to data collection, reduction, and integration. 

However, any data without geolocation were integrated 

using appropriate database software and languages 

(Microsoft 2018). Finally, the data collection task included 

a quality assurance step by each team member and a final 

quality control step before the data analysis was conducted.

Definition of Crash Corridors

Because a limited number of WWD crashes matched 

to their corresponding POE was anticipated since the 

beginning of the study, the research team defined the 

following three stages of data collection for the study:

• In stage 1, case corridors were defined for each 

WWD crash, starting from the location of the crash, 

and continuing down in the wrong-way direction 

of travel until reaching a distance, D. Detailed data 

were then collected for all POEs within each WWD 

crash corridor.

• In stage 2, data were collected to determine whether 

the WWD crash in a corridor could have originated 

from one of the corridor’s POEs. This determination 

required reviewing individual crash narratives in 

search of clues about the POE for each crash.

• In stage 3, the research team removed WWD crash 

corridors from the roadway network layer for a given 

facility of interest. Then, the research team selected 

random points of the remaining facilities of interest 

and considered the starting points of non-WWD 

corridors to be included in the analysis. Data for 

all POEs within each non-WWD corridor were then 

collected as in stage 2.

The definition of the distance, D, was based on 

reported traveled distances on freeways according to 

NCHRP Report No. 881 (Finley et al. 2018). The research 

team determined that D = 6 mi represents roughly the 

85th percentile traveled distance between the POE and 

crash locations.

Corridors with confirmed crash-POE matches were 

analyzed during phase 1. The research team then used the 

results from the phase 1 analysis to produce crash risk 

estimates that would imply weights for each candidate 

POE in relation to a particular WWD crash for the phase 2 

analysis. The phase 1 analysis produced estimates of WWD 

crash risk as a function of POE geometry, time of day, and 

position of the POE relative to the WWD crash location. 

In the phase 2 analysis, the research team analyzed all 

available corridors with POEs that were related to given 

WWD crashes. These data were weighted by the risk for 

WWD crashes based on the phase 1 analysis results.

ANALYSIS

Risk models with PS weights were developed for each 

WWD crash with known POEs. The weights were 

developed from PS models to represent the likelihood of 

a POE to belong to the upper quartile of the distribution 

of the number of select TCDs in display. CMFs were 

estimated by using the corresponding model coefficients 

in linear contrasts, which were representative of the 

conditions of interest, as appropriate.

Phase 1 Results

The following key groups of variables were considered in 

the model development for phase 1:

• The distance between each WWD crash and its 

confirmed POE.

• Annual average daily traffic.

• Multiple variables coding the time of day for each 

WWD crash.

• The type of traffic control at each POE.

• Variables coding geometric features of POEs.

After a round of modeling with the set of corridors 

with confirmed POEs, the research team arrived at the 

following results:

• Longer distances between the cross street and the 

beginning of a freeway off-ramp are most likely 

linked to a reduction in WWD crash risk.

• Time of day of WWD crashes influences the impacts 

of three other risk factors:

○ WWD crash/POE distance.

Table 1. Available data sources in Texas and Florida.

STATE CRASH DATA TRAFFIC DATA ROADWAY DATA

Texas CRIS database, 2000–2019 

(TxDOTa 2022)

CRIS database, 2000–2019 

RHiNo database, 2014–2019. 

(TxDOTa 2022)

RHiNo database, 2000–2019. 

(TxDOTb 2022)

Florida FDOT Open Data Hub, 2013–2016 

(FDOT 2022)

FDOT Open Data Hub (2013–2016) 

(FDOT 2022)

FDOT Open Data Hub 

(FDOT 2022)

CRIS = Crash Records Information System; FDOT = Florida DOT; RHiNo = Roadway-Highway Inventory.
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○ Number of driveways and T intersections not 

signalized at frontage roads.

○ Distinction between POEs connected to divided 

cross streets.

• WWD crash/POE distance is inversely proportional to 

the risk for WWD crashes. However, the interaction 

terms of this risk factor imply the following shifts in 

that trend:

○ The inverse relationship between WWD crash risk 

and WWD crash/POE distance is strengthened 

during the daytime. Shorter WWD crash/POE 

distances are more likely to occur during daytime 

than during nighttime.

○ The daytime risk of WWD crashes decreases with 

distance at a slower rate when additional driveways 

or unsignalized T intersections are present in POEs 

at frontage roads. This finding is intuitive because 

additional driveways or intersections represent 

increased chances of WWD maneuvers.

• In general, the frontage road configuration seems less 

prone to WWD crashes, but this trend varies with other 

variables in the model that are specific to frontage 

roads that have estimates in different directions. For 

example, the number of access points not signed on 

the frontage road is linked to an increase in WWD 

crash risk, whereas the number of signed access points 

is linked to a reduction in WWD crash risk.

• Regarding the facility type of the cross street, 

no difference in crash risk between divided and 

undivided cross streets at night exists, but the risk for 

a WWD crash during the daytime was significantly 

lower at divided facilities than at undivided facilities.

Phase 2 Results

Based on the results in phase 1, the research team 

incorporated the rest of the corridors for the next phase 

of analysis. Because the amount of the Texas data 

permitted subsets of appropriate sizes, the analyses 

were conducted by light condition (daytime or 

nighttime), facility type (either frontage road or ramps 

at intersections), and type of cross street (divided or 

undivided). From the developed models, CMFs were 

estimated for interventions involving geometry, access 

management, and TCDs. Table 2 summarizes statistically 

significant CMF estimates regarding geometry and access 

management interventions.

CMFs in Table 2 indicate that lengthening off-ramps 

and removing driveways from frontage roads could 

potentially reduce nighttime WWD crashes, whereas 

applying appropriate signage at T intersections and 

removing driveways at frontage roads were found to be 

effective against daytime WWD crashes. Next, Table 3

summarizes CMF estimates for WWD TCDs.

All CMFs in Table 3 indicate crash reductions for various 

base conditions and lighting levels. Next the research 

team performed similar analyses for the Florida database. 

However, given the reduced size of that database, only 

data representing off-ramps arriving at cross-street 

intersections were analyzed. Table 4 summarizes 

statistically significant results from the Florida analyses.

Although the magnitudes of the estimates are slightly 

more conservative for Florida, the estimates’ directions 

are consistent with the findings in Texas.

Table 2. WWD crash CMFs for geometric features in Texas.

GEOMETRIC 

FEATURE

WWW 

CRASH TYPE CMF LOWER LIMIT (95% CL)

UPPER LIMIT 

(95% CL)

LOWER LIMIT 

(90% CL)

UPPER LIMIT 

(90% CL) SIGNIF.

Additional 100 ft of frontage 

or off rampa Night 0.972 0.946 0.990 0.950 0.995 *

Driveway on frontage 

road removala Day 0.873 0.821 0.928 0.829 0.919 ***

Driveway on frontage 

road removala Night 0.925 0.849 1.007 0.861 0.994 ~

Add signage to T intersection 

on frontage roada Days 0.635 0.442 0.912 0.468 0.862 *

~Significant at the 90.0 percent confidence level (CL).

*Significant at the 95.0 percent CL.

***Significant at the 99.9 percent CL. a

Base condition: frontage road intersecting divided cross street (urban and suburban).

Signif. = significance.
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Economic Effectiveness

The research team estimated the economic viability 

of applying additional WW signs at potential POEs, 

with benefits derived from the ability of the sign to 

prevent WWD crashes. The estimated B/C ratio is 

29.08 for this treatment. The team found the treatment 

to be beneficial, even when adding redundant signs. 

Repeating the calculations for the installation of 

two additional WW signs instead of one resulted 

in a B/C ratio of 21.96, or a B/C ratio of 13.83 

when four such additional signs were installed.

Table 3. WWD crash CMFs for TCDs at frontage roads in Texas.

TCD WWD 

CRASH TYPE CMF LOWER LIMIT (95% CL)

UPPER LIMIT 

(95% CL)

LOWER LIMIT 

(90% CL)

UPPER LIMIT 

(90% CL) SIGNIF.

Additional WW_Signa Day 0.509 0.330 0.787 0.353 0.735 **

LANE_Patha Night 0.416 0.201 0.860 0.225 0.767 *

STOP_Bara Night 0.129 0.026 0.652 0.033 0.505 *

One additional WW_Signb Night 0.153 0.028 0.637 0.037 0.833 *

Two additional 

WW_Signsb Night 0.227 0.051 0.797 0.065 1.009 ~

DNE, WW, or 

OW additional signc Night 0.623 0.402 0.965 0.431 0.900 *

STOP_Bar, WW_Arrow, or 

LANEUSE_Arrowc Day 0.189 0.028 1.299 0.037 0.958 ~

Presence of STOP_Bar, 

WW_Arrow, or LANEUSE_Arrowc Night 0.227 0.051 1.014 0.064 0.800 ~

Additional DNE_Signd Day 0.321 0.162 0.639 0.180 0.573 **

Additional 

DNE_Signd Night 0.640 0.424 0.905 0.452 0.966 *

~Significant at the 90.0 percent CL.

*Significant at the 95.0 percent CL.

**Significant at the 99.0 percent CL. a

Base condition: frontage road intersecting divided cross street (urban and suburban).

b

Base condition: frontage road intersecting undivided cross street (urban and suburban).

c

Base condition: off-ramp intersecting divided cross street (urban and suburban).

d

Base condition: off-ramp intersecting undivided cross street (urban and suburban).

OW = ONE WAY.

Table 4. WWD crash CMFs for geometric features at nonfrontage roads in Florida.

GEOMETRIC 

FEATURE

WWW 

CRASH TYPE CMF LOWER LIMIT (95% CL)

UPPER LIMIT 

(95% CL)

LOWER LIMIT 

(90% CL)

UPPER LIMIT 

(90% CL) SIGNIF.

Remove ramp lanea Night 0.489 0.324 0.737 0.346 0.691 ***

Additional DNE or WW signa Day 0.729 0.522 1.018 0.550 0.966 ~

~Significant at the 90.0 percent CL.

***Significant at the 99.9 percent CL. a

Base condition: frontage road intersecting either divided or undivided cross street.

Similarly, the research team estimated the benefit 

of installing DO NOT ENTER signs. The estimated 

B/C ratio is 55.7 for this treatment. The treatment 

was found to be beneficial, even when adding 

redundant signs. When the research team repeated the 

exercise for the installation of two additional DNE 

signs instead of one, the resulting B/C ratio was 37.4, 

or a B/C ratio of 21.1 when installing four such signs.
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CONCLUSIONS

The objective of this study was to perform rigorous safety 

effectiveness evaluations of WWD crash countermeasures 

at freeways. Specifically, the study focused on the safety 

effectiveness of geometric features, access management 

strategies, and TCDs as potential countermeasures for 

WWD crashes. The study had a retrospective crosssectional design, and the analysis was performed by using 

generalized linear mixed binomial models. The research 

team compiled safety data from 1,460 POEs in Texas and 

644 in Florida, representing 463 and 256 WWD crashes, 

respectively, for 3-yr periods in each State. The team 

supplemented the datasets with non-WWD crash locations 

at each State, yielding 2,722 POEs for evaluation in 

Texas and 697 POEs in Florida for evaluation, with 375 

and 110 WWD crashes, respectively, after data filtering 

and cleaning. Due to the limited number of crashes, the 

evaluations were performed by differentiating between 

only daytime and nighttime conditions. In addition to 

the crash data, the research team collected geometry 

information and traffic volumes at study locations. The 

team could only identify the true POE for a small fraction 

of the data in Texas by reviewing crash narratives, leaving 

the rest of the datasets with unknown origin of the WWD 

maneuver that led to the corresponding WWD crash.

All statistically significant CMFs were found in the 

analyses for the larger database in Texas, and two 

statistically significant CMFs were found in the Florida 

data. For geometric features, significant findings 

corresponded to both frontage roads and off-ramps to 

surface roads in Texas and off-ramps to surface roads 

in Florida. These analyses found that the chances of 

WWD crashes tend to be higher at locations with more 

ramps or ramp lanes and reduced at locations with 

longer ramps. Regarding CMFs for access management, 

the analysis of Texas sites with POEs at frontage roads 

produced CMFs indicating that adding vertical signage 

to frontage T intersections is linked to a statistically 

significant reduction of daytime WWD crashes. The 

same analyses found that WWD crash incidence tends 

to be lower with each removed driveway at frontage 

roads (between off-ramps and crossing roads), in terms 

of both daytime and nighttime WWD crashes. Finally, 

regarding TCDs, the Texas analyses found WWD crash 

reductions associated with adding WW signs, DNE 

signs, STOP signs, OW signs, and pavement markings 

for turning lane paths as well as at locations where 

stop bars are present on frontage road approaches 

when intersecting crossing roads. Similarly for Florida, 

statistically significant CMFs were found for removing 

ramps or ramps lanes at intersections with off-ramps 

and for additional WW and DNE signs at these locations.

The economic evaluation of deploying WW and DNE 

signs found large B/C ratios, with values greater than 1.0 

indicating that these countermeasures are expected to 

produce more safety benefits than costs and therefore their 

installation is justified.

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Researchers—This study was performed under contract number DTFH6116D00039L by Raul Avelar (principal 

investigator) and researchers Boniphace Kutela and Melisa Finley of the Texas A&M Transportation Institute.

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

made to the FHWA divisions and Resource Center.

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

Key Words—Crash modification factor, CMF, wrong-way driving, safety.

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.

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reviews quality issues and adjusts its programs and processes to ensure continuous quality improvement.

SEPTEMBER 2023 FHWA-HRT-22-112

HRSO-20/09-23(WEB)E

Recommended citation: Federal Highway Administration, 

Development of Crash Modification Factors for Wrong-Way Driving 

Treatments (Washington, DC: 2023) https://doi.org/10.21949/1521942