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Wrong Way Driving (WWD) 

Low-Cost Safety 

Improvements 

Workshop

Evaluation of Low-Cost Safety Improvements Pooled Fund Study

Raul Avelar, Research Scientist, Texas A&M Transportation 

Institute

March 2022
Disclaimer

This presentation was created and is being presented by a contractor. The views 

and opinions expressed in this presentation are the presenter’s and do not 

necessarily reflect those of the Federal Highway Administration or the U.S. 

Department of Transportation (USDOT). The contents do not necessarily reflect 

the official policy of the USDOT.

The U.S. Government does not endorse products or manufacturers. Trademarks 

or manufacturers’ names appear in this presentation only because they are 

considered essential to the objective of the presentation. They are included for 

informational purposes only and are not intended to reflect a preference, 

approval, or endorsement of any one product or entity.

2
Abbreviations

B/C Benefit / Cost

CMF Crash modification factor

DNE Do not enter

MUTCD Manual on Uniform Traffic Control Devices. (1)

OW One way

POE Point of entry

Signed Displaying vertical signage

TCD Traffic control device

Unsigned Not displaying any vertical signage

WWD Wrong-way driving

WW Wrong way

All images source: FHWA.(1)

(1)Federal Highway Administration (FHWA). 2009. Manual on Uniform Traffic Control Devices for Streets and Highways. Washington, DC: FHWA. 3
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Developing CMFs and Economic 

Effectiveness for WWD Crashes

Study Overview
Study Objectives

► The first objective of this study is to quantify the safety 

effectiveness of WWD countermeasures like CMFs.

► The second objective of this study is to assess the economic 

viability of WWD countermeasures.

5
WWD Crash Characteristics

► WWD crashes represent a small portion of total freeway crashes.

► WWD crashes are usually head-on or fixed-object collisions.

► WWD crashes lead to more fatalities and injuries than other 

crash types.

6
CMFs And Economic Effectiveness for WWD 

Countermeasures

This study focused on freeways and found the following:

► WW crashes are extremely rare.

► WW crash occurrences are often recorded miles from the WW 

POE.

► WW crashes could be prevented by varying features or applying 

treatments at the POE.

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Developing CMFs and Economic 

Effectiveness for WWD Crashes

Data Collection
WWD Crashes on TX and FL Freeways

Route 

Class 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Avg. Total

Texas -- 148 132 173 156 173 210 192 113 187 207 169.1 169.1

Florida 43 47 27 40 37 43 48 38 -- -- -- 40.4 323

► Phase I

► Phase II

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Data Collection Protocol

1

Identify WWD 

crashes and their 

locations.

2

Identify a “corridor” 

length starting from 

the WWD crash 

location 

downstream to a 

maximum distance.

3

Identify all POEs 

for the WWD 

crash along that 

corridor.

4

Collect geometric 

and TCD data at 

each POE.

5

Review crash 

narratives to 

determine the 

POEs.

© 2022 Texas A&M Transportation Institute

10
Sample of Data Collection Flow

© 2021 GoogleEarth.

11
WWD Countermeasures At POE

Conventional TCDs:

► Signs.

► Markings.

Geometric features:

► Ramp and cross-road 

features.

► Frontage road access.

Mandatory and optional signs and markings at the exit terminal (Source MUTCD)

Source: FHWA.(1)

(1)Federal Highway Administration (FHWA). 2009. Manual on 

Uniform Traffic Control Devices for Streets and Highways. 

Washington, DC: FHWA. 

12
WWD Countermeasures At POE

Conventional TCDs:

► Signs.

► Markings.

Geometric features:

► Ramp and cross-road 

features.

► Frontage road access.

13
Identifying WWD Crashes and Entry Points

© 2020 GoogleEarth. Modified by Texas A&M Transportation Institute.

From which POE did each crash come?

14
Review of Crash Narratives in TX

The intent of this review of text and 

narrative diagrams was to quantify 

relationships between POE and crash 

location.

© 2022 Texas A&M Transportation Institute

15
Characteristic Observation

Average 

(mi) 

Minimum 

(mi) 

Maximum 

(mi) 

Std dev 

(mi) 

Overall 192 0.71 0.0 5.21 0.97 

In-Depth Review of 

TX Crash Narratives

► The researchers searched to 

identify POEs in narratives from 

2011 through 2020 (1,300+ TX 

crash narratives).

► The POE could be identified for a 

total of 192 crashes in that period.

► The period from 2016-2019 was 

supplemented with non-crash 

corridors and used for analysis. Characteristics of WWD Distances Travel prior to Crash

16

© 2022 Texas A&M Transportation Institute

NT

NT

NT

NT
WWD Crash Databases

Two databases:

► Phase I database: 68 WWD crashes and 406 POEs.

► Phase II database: 891 WWD crashes and 2,843 POEs.

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Developing CMFs and Economic 

Effectiveness for WWD Crashes

Methodology and Analysis
Analyses at a Glimpse

Databases used for the two-stage analysis:

1. Phase 1: A smaller database of locations with POEs known for 

every crash.

2. Phase 2: A larger database of crashes linked to a set of 

potential originating POEs.

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Phase 1 Analysis: POE Known for Crash

The phase 1 analysis included:

► A binomial regression of WWD crash risk (i.e., probability of WWD crash).

► Non-WWD crash locations (randomly selected).

The explanatory variables included:

► Distance traveled from POE to WWD crash location.

► POE geometric features.

► Time of day.

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Phase 1 Results

► Longer WWD travel distances are more likely at night.

► Higher WWD risk with increasing number of driveways and 

unsigned T-intersections (frontage roads).

► Lower WWD risk with increasing number of signed Tintersections (frontage roads).

21
Phase 2

The phase 2 analysis included:

► A binomial regression of WWD crash risk.

► The phase 1 locations (i.e., crash risk = 1.0 for actual POE ; the crash 

risk = 0.0 for all other potential POEs nearby).

► The non-WWD crash locations (WWD risk for all potential POEs = 0.0).

► The total combined risk of WWD crash nearby POEs defined as 0.85 

(i.e., farthest linked POE can be as far as the 85th percentile of known 

WWD travel distances).

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Phase 2 Data Subsets

► Data were subdivided by State (FL and TX).

► State data were subdivided into four categories by POE type:

 Freeway exits at frontage roads intersecting divided roadways.

 Freeway exits at frontage roads intersecting undivided roadways.

 Freeway exits at intersections on divided roadways.

 Freeway exits at intersections on undivided roadways.

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Phase 2 Data Subsets

► Analyses yielded various statistically significant results in TX.

► Analyses yielded only one statistically significant result in FL 

dataset. 

► FL dataset was about one-third the size of the TX database.

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Phase 2 Results Overview for Frontage 

Roads

► Number of driveways directly proportional to WWD crash risk.

► Number of signed T-intersections inversely proportional to 

WWD crash risk.

► Types of TCDs with statistically significant reductions of WWD 

crash risk:

 WW sign.

 OW sign.

 Stop bar at point of intersection.

 Lane path (turning lanes). 25
Phase 2 Results Overview for Off-Ramps 

at Intersections

► WWD crash risk increases with an increasing number of lanes 

at an intersection located on an undivided street.

► TCDs associated with reduced WWD crash risk:

 DNE Signs.

 Pavement markings: 

• Stop bar.

• Lane use arrow.

• WW arrow.

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Developing CMFs and Economic 

Effectiveness for WWD Crashes

CMF Results
Countermeasures at Frontage Roads

Countermeasure Crash Type CMF

Lower Limit

95 Percent 

Confidence

Upper Limit

95 Percent 

Confidence

Signif.

Remove driveway from 

frontagea Day WWD 0.870 0.823 0.919 ***

Remove driveway from 

frontagea Night WWD 0.912 0.849 0.980 *

Add signs at a frontage 

T- intersectiona Day WWD 0.622 0.912 0.450 *

Additional WW sign at 

cross-street 

intersectiona

Day WWD 0.490 0.296 0.810 **

Lane path through 

cross-street 

intersectiona

Night WWD 

crashes 0.387 0.186 0.803 *

* Significant at the 95.0 percent confidence level.

** Significant at the 99.0 percent confidence level.

*** Significant at the 99.9 percent confidence level. 

a. Base condition: frontage road intersecting 

divided cross street.
Countermeasures at Intersections with Off-Ramps

Countermeasure Crash Type CMF

Lower Limit

95 Percent 

Confidence

Upper Limit

95 Percent 

Confidence

Signif.

Ramp lane removal from 

intersectiona

Day WWD 

crashes 0.435 0.232 0.814 *

Ramp lane removal from 

intersectiona

Night WWD 

crashes 0.561 0.326 0.965 ~

Lengthening 

100 ft of off-rampa

Day WWD 

crashes 0.928 0.852 1.010 ~

Adding STOP_Bar, WW 

Arrow, or LANEUSE 

Arrowa

Day WWD 

crashes 0.189 0.028 1.299 ~

Adding STOP_Bar, WW 

Arrow, or LANEUSE 

Arrowa

Night WWD 

crashes 0.227 0.055 0.944 *

Adding DNE signb Day WWD 

crashes 0.358 0.177 0.723 **

Adding DNE signb Night WWD 

crashes 0.307 0.149 0.630 **

~ Significant at the 90.0 percent confidence level.

* Significant at the 95.0 percent confidence level.

** Significant at the 99.0 percent confidence level.

a. Base condition: frontage road intersecting 

divided cross street.

b. Base condition: off-ramp at undivided cross 

street intersection.
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Developing CMFs and Economic 

Effectiveness for WWD Crashes 

Economic effectiveness
Estimation of B/C Ratios for Redundant 

WW Sign

► WWD daytime crashes averaged 0.491 in 3 yr (per combined 

TX and FL dataset).

► WWD daytime crash reduction of an estimated 51 percent for 

each additional WW sign in a corridor.

► Sign and post replacement assumed to occur every 2 yr and 

yielded the following B/C ratios:

 B/C ratio = 30.27 for adding 1 redundant WW sign at each POE.

 B/C ratio = 22.52 for adding 2 redundant WW signs at each POE.

 B/C ratio = 14.00 for adding 4 redundant WW signs at each POE.

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Estimation of B/C Ratios for Redundant 

DNE Sign

► WWD nighttime crashes averaged 0.714 in 3 yr (per combined 

TX and FL dataset).

► WWD nighttime crash reduction of 69 percent for each 

additional DNE sign in a corridor. 

► Sign and post replacement assumed to occur every 2 yr

yielded the following B/C ratios:

 B/C ratio = 56.22 for adding 1 redundant DNE sign at each POE.

 B/C ratio = 38.00 for adding 2 redundant DNE signs at each POE.

 B/C ratio = 21.4 for adding 4 redundant DNE signs at each POE.

32
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Developing CMFs and Economic 

Effectiveness for WWD Crashes 

Conclusions
Recommendations for Frontage Roads

► Recommendations for access management along frontage road 

segments:

 Fewer driveways and unsigned T-intersections between off-ramp and 

cross-road intersections.

 Appropriate signs at frontage T-intersections.

► Recommendations for at frontage-road/crossroad intersections:

 Consider the use of optional OW and WW signs. 

 Apply and enhance pavement markings: 

• Stop bars.

• Lane paths for turning lanes.

34
Concluding Remarks and Recommendations for 

Off-Ramps at Intersections

► Off-ramp geometric design considerations include the following facts:

 Fewer ramps at intersections are linked to a lower risk of WWD crashes.

 Shorter ramps are linked to an increased risk of WWD crashes.

► TCDs to consider include the following:

 Vertical signage:

• DNE signs.

• Possibly others (not supported by statistical analyses).

 Pavement markings and delineation:

• Stop bars.

• WW arrows.

• Lane use arrows.

35
Questions? 

Discussion?

36
Thank You!

37
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CONTACT

Roya Amjadi

Federal Highway Administration

roya.amjadi@dot.gov