Understanding Distraction

Michael A. Knodler JrNortheast Safety Conference

October 24, 2017

UMassAmherst Transportation Center

Distraction= Bad

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University of Massachusetts Transportation Center

About UMassSafe

Support highway safety through combined approach

Scientific data-driven problem identification, program design, and

evaluation

Traditional highway safety practices

(engineering, enforcement &

education)

Arbella Insurance Human Performance Lab

University of Massachusetts Transportation Center

Background – Massachusetts Laws 

Text Messaging

Drivers are not allowed to manipulate any electronic device to send, write or read messages. The 

penalty is $100 for the first offense, $250 for the second and $500 for subsequent every 

offense5. 

Junior Operators (CDL) Commercial Drivers License

Drivers under 18 years of age, are not permitted to use cell phones while 

driving. The first offense is $100 with a 60‐day license suspension, the second is 

$250 with 180‐day suspension, and the third 

is $500 with 1‐year suspension5.

Mobile device use prohibited is nationwide while operating vehicles that require a CDL. These larger vehicles require 

specific licensure resulting in greater penalties, 

including fines of up to $2,750 or loss of CDL for 

repeat offenders6. 

Distraction Background

Types of Distraction

PhysicalVisual Cognitive

Taking your eyes off the

road

Taking your hands off the

wheel

Taking your focus off the driving task

• Text messaging• Dialing• GPS navigation• Billboards

• Text messaging• Dialing• Hand-held

conversation• Eating• Smoking

• Text messaging• Dialing• Hand-held

conversation• Hands-free

conversation• Stress

Research Approach

DistractionUnderstand Prevalence

Impact of Data 

Collection Methods

Quantifying Impacts

Mitigating Distraction

Pedestrian Distraction

Nationwide Belt Usage

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2003

Saf

ety

Bel

t Use

Rat

es

Primary Secondary

Source: Glassbrenner, D. Safety Belt Use in 2003 - Use Rates in the States and Territories , NHTSA Technical Report March 2004

Occupant Configuration

Here at UMass….• Annual Study• ≈140 Locations

Direct Observation

Observation survey of drivers’ cell phone and seat belt use in MA Controlled intersections Teams of two Day time observations (7AM‐7PM) 1 hour in duration

Large scale statewide observations 100+ observation locations 15,000+ drivers observed Allows for demographic comparisons

Summary of Drivers’ Cell Phone Use Prevalence ‐ 2017

Drivers’ Cell Phone Use: Passenger Gender

Conclusions of Drivers’ Cell Phone Use

Among variables the cell phone use of: Females > males Teens > adults > elders CMV > average No passenger > passenger No seat belt use > seat belt use Weekday > weekend

Limitations Observations at controlled intersections Observers may deter drivers from using cell phones

Drivers’ Cell Phone Use: Intersection Type

0%

1%

2%

3%

4%

5%

6%

7%

8%

Text Messaging Handheld

Percen

t of O

bserved Ce

ll Ph

one Use

Stop Signalized

Examination of Observation Methods

Controlled intersections 10 Signalized  2 Stop Sign 

Upstream vs. intersection Video data collection Link individual vehicles

Upstream

Intersection

Percent Text Messaging 

Influence of Intersection Type

Influence of Observer

Signalized intersection Two observations No observer Observer at intersection 

Constant variables Same locations Day of week Time of day Weather

The Influence of an Observer

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

4.5%

Upstream Intersection Upstream Intersection

Text Messaging Percentage Talking Percentage

Percen

t of O

bserved Ce

ll Ph

one Use

No Observer One Observer at the Intersection

Mobile Distraction Field Observations

Research team observed driver behavior from a probe vehicle Site information (roadway type, date, weather, time) was be recorded

17 driving periods with 89 segments and 1575 recorded observations

Observed variables of interest:

VEHICLE TRAVEL LANE ACTION DRIVER PASSENGERPassenger Left Lane Passing Male Age 16‐19 Distraction: Holding Position: Adult 1 Child Seated Front Action:SUV Middle Lane Non‐passing Female Age 20‐39 Cell Talk 12:00 Elder 2 Children Seated Back AlertPick‐Up Right Lane Stopped Unknown Age 40‐59 Cell Touch 2:00  /  10:00 Teen 3 Children Cell UseMini‐Van Other: At Crosswalk Age 60+ iPad/Tablet 6:00 Child SleepingCommercial Other: Unknown Other: Other: None Other:

No Distraction

Field Observations—Results 

Summary

Distraction Type Count Percent of

Observations Cell Talk 124 7.87% Cell Touch 74 4.70% No Distraction 1,306 82.92% Other 71 4.51% Total 1,575 100.00%

7.87%4.70%

82.92%

4.51%

Percent of Observations for Total Distraction Type

Cell Talk Cell Touch No Distraction Other

Field Observations—Results 

Vehicle Action: Count & Percent of Observations

Vehicle Action Cell Talk Cell

Touch Other No

Distraction Total Non-Passing 59 23 32 481 595 Passing 39 15 21 408 483 Stopped 3 19 12 67 101 1 Lane 23 17 6 350 396 Total 124 74 71 1,306 1,575

Vehicle Action Cell Talk Cell

Touch Other No

DistractionNon-Passing 9.92% 3.87% 5.38% 80.84% Passing 8.07% 3.11% 4.35% 84.47% Stopped 2.97% 18.81% 11.88% 66.34% 1 Lane 5.81% 4.29% 1.52% 88.38% Total 7.87% 4.70% 4.51% 82.92%

Field Observations—Results 

Holding Position

Holding Position Cell Talk Cell

Touch OtherNo

Distraction Total 12:00 78 24 32 232 366 2:00/10:00 8 12 10 311 341 6:00 19 25 14 170 228 Other 1 6 1 14 22 Not Recorded 18 7 14 579 618 Total 124 74 71 1,306 1,575

Holding Position Cell Talk Cell

Touch Other No

Distraction12:00 21.31% 6.56% 8.74% 63.39% 2:00/10:00 2.35% 3.52% 2.93% 91.20% 6:00 8.33% 10.96% 6.14% 74.56% Other 4.55% 27.27% 4.55% 63.64% Not Recorded 2.91% 1.13% 2.27% 93.69% Total 7.87% 4.70% 4.51% 82.92%

Recommendations for Observation Methods

Signalized intersections Add a cell phone use type “texting while stopped” Observe upstream

Stop sign controlled No change necessary

Influence of observers No change necessary Large scale study may show difference

Vehicle action while moving is arguably more important

Data Utilization

State Police Collision Analysis & Reconstruction

State Police RAMS Direct Crash Data

Arbella Insurance & Training Records

New Data:

Crash Data Analyses

Massachusetts Safe Driving Law Effective September 

30, 2010 Determine crash hot spots using GIS

Where are these crashes occurring?

Changes before and after MA Safe Driving Law?

Crash Trends –pre Safe Driving Law of 2010

11.5%

12.9% 13.3%14.4%

17.1%

20.0%

21.5%

0.0%

2.0%

4.0%

6.0%

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22.0%

0

3000

6000

9000

12000

15000

18000

21000

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27000

30000

2005 2006 2007 2008 2009 2010 2011

Distracted D

riving Crashes as a % of All CrashesN

umbe

r of D

istrac

ted Driving Crash

es per Yea

r

Massachusetts Trends

7:00 A.M. to 7:00 P.M. 7:00 P.M. to 7:00 A.M. Percent of all Crashes

Trends in Distracted DrivingCrashes by Age

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2007 2008 2009 2010 2011

Percen

t of  Distracted Driv

ing Crashe

s 

Num

ber o

f Distracted Driv

ing Crashe

s

Teen Adult ElderTeen Percentage Adult Percentage Elder Percentage

Conclusions Regarding Safety Impacts 

Observed a relationship between cell phone use rate and distracted driving crash rate 

• Teens had the highest distracted driving crash rate and the highest cell phone use rate

• Females had the highest distracted driving crash rate and the highest observed cell phone use rate

Age and Gender Safety Implications

0 5 10 15 200

5

10

15

Cel

l Pho

ne U

se (%

)

Distracted Driving Crash Rate (%)

Male TeenMale Adult

Male Elder

Female Teen

Female Adult

Female Elder

Influence of Speed on Distracted Driving Crashes

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

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5 10 15 20 25 30 35 40 45 50 55 60 65

Percen

t of  Distracted Driv

ing Crashe

s 

Num

ber o

f Distracted Driv

ing Crashe

s

Speed Limit

Number Percent

Crash Data Analysis ‐ Results 

13.95%

54.51%

31.54%

Before MA Safe Driving Law

17.68%

51.82%

30.50%

After MA Safe Driving Law

Mapping Results—EPDO Maps

EPDO: Equivalent Property Damage Only Crash severity point system based on injury status

Max Injury Severity EPDO WeightFatal injury 9.5 Non-fatal injury – Incapacitating 4.5 Non-fatal injury – Non-incapacitating 3.5 Non-fatal injury – Possible 2.5 No injury 1 Not Applicable 1 Not Reported 1 Reported but invalid 1 Unknown 1 Deceased not caused by crash 1

Mapping Results—EPDO Maps

Before MA Safe Driving Law(2008‐2010)

After MA Safe Driving Law(2012‐2013)

Crash Report Narrative Search

UMass Safety Data Warehouse Develop a list of keywords indicating distraction Ex: cell, phone, text, call, etc.

200 random reports pulled 100 distraction classified 100 non‐distraction classified

Team of 7 reviewers 66 double blind reviews 33 distraction/non‐distraction

Crash Report NarrativeSearch Results 

Key Word or Phrase

Number of Distraction Related Narratives Containing Words

Frequency in Non-distraction Related Crash Narratives

Containing Words Distracted 39 1

Phone 17 0 Looked Down 14 1

Cell 10 0 Eyes off the Road* 8 0

GPS 5 0 Change Radio 4 0

Not Paying Attention* 4 0 Adjust 3 0

Looked Away 2 0 Reading* 2 0

Looked At* 2 0 Dropped* 2 1

Rang* 2 0 * Indicates key word/phrase added by review team 

Crash Report Narrative Search—Results

Distraction Classified Non‐Distraction ClassifiedDistraction Related 70 4

Non‐Distraction Related 25 96

Crash Report InformationReviewer Determination

Inexperienced Drivers

McCartt A.T., Shabanova, V.I., and Leaf W.A. (2003). Driving experience, crashes and traffic citations of teenage beginning drivers. Accident Analysis and Prevention, 35, 311-320.

2.3 crashes/10,000 miles

0.4 crashes/10,000 miles

Three Major Problems for Novice Drivers

Hazard Anticipation Tactical Strategic

Attention Maintenance Inside the vehicle Outside the vehicle

Speed Management

Driving Simulator & Eye Tracker

A ‐ videos\NA06_OppTruckLT_Error.mpg

The In‐Vehicle Tasks

Results: Novice & Experienced Drivers

55%

22%

Attention Maintenance Training: FOCAL

Field EvaluationSuburban New Jersey

Results: Trained & Untrained Drivers

 > 2 sec > 2.5 sec > 3 secTrained 59.50% 41.90% 30.20%Untrained 75.90% 60.20% 41.70%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

Percen

t of S

cena

rios

Percentage of Scenarios with a Glance Greater than ThresholdUntrained

Trained

Arbella Insurance: Distractology 101

Scenario Descriptions – “Truck Trick”

Alternative Treatments at Railroad Grade Crossings

Dynamic Envelope Pavement Markings

Distracted Peds - Methodology

1386 pedestrian crossings, 7 treatment types Distraction Vehicle Action – Yield or Don’t Yield Pedestrian Action – Look Before or Crossing?

50

Crosswalk Treatments

Intersection with Countdown Signal

Raised Crosswalk

Pedestrian Activated Bollards

52

Overhead Flashing Lights T-Intersection

Traditional Crosswalk Flashing Signs, Advanced Yield Markings, Rumble Stripes

Distracted Peds - Field Findings

Presenters: Cole Fitzpatrick, Ian McKinnon 53

Effect of Distraction on Crossing Behavior

Distracted Peds - Field Findings

Crossing Behavior based on Crosswalk Treatment

Distracted Peds - Field FindingsDriver Response to Pedestrian Distraction

Questions

Michael KnodlerDirector, UMass Transportation Center

University of Massachusettsmknodler@umass.edu

www.umasstransportationcenter.org(413) 545‐0228

Experiment Summary

Drivers benefitted tremendously from the addition of the dynamic envelope markings. 

One of the important findings of this experiment is that the visibility of the lights and the presence of a car ahead are significantly important aspects for driver safety in the vicinity of crossings.

The cell phone group fared much better than the radio group.

Distracted Peds –Field Observation Findings

Pedestrian Action

Vehicle Stopped (#)

Total Interactions (#)

Yield Rate(%)

Distracted 357 661 54.0%

Non-Distracted 327 725 45.1%

P-Value: 0.001

Driver Response to Pedestrian Distraction

Questions? 

Crash Data Analysis—Results  Compared crash data from before and after the Massachusetts Safe Driving Law was passed

Description Number of Crashes Before MA Safe Driving Law 437,762 After MA Safe Driving Law 451,312 Total 889,074

Description Distracted

Fault No

Fault Other Fault Total

Before MA Safe Driving Law 43,547 170,139 98,451 312,137 After MA Safe Driving Law 66,729 195,636 115,129 377,494 Total 110,276 365,775 213,580 689,631

Mobile Observation Experiment Research Objectives

1. Identify attributes of observed distracted driving behaviors and determine which behaviors are more common or dangerous.

2. Understand the role and impact of distraction on crashes, and the reliability associated with crash reporting for distraction related crashes.

Research Questions

1. Has the number of drivers engaging in distracted driving and the number of distracted driving crashes decreased since the existence of the Massachusetts Safe Driving Law?

2. Can crash report narratives provide insight to distraction related crashes? Are these events always correctly identified as distraction related?

3. Are there identifiable hot spot locations for distracted driving crashes in Massachusetts?

Crash Report Narrative Search Results 

Distraction Classified Narratives 72 out of 100 narratives contained distraction key words 5 out of 33 blind reviews had contradicting opinions 25 out of the remaining 95 were classified as non‐distracted by 

the reviewers 23 out of these 25 did not have key words present

Crash Report Narrative Search Results 

Non‐Distraction Classified Narratives 5 out of 100 narratives contained distraction key words 4 out of 5 incorrectly classified as distracted Driver contributing codes for 4 “distraction” narratives:

Disregarded traffic signs, signals, or road markings Inattention Wrong side or wrong way Operating vehicle in erratic, reckless, careless, negligent, or aggressive manner.

Conclusions

Field Observations More female distraction than male Steering wheel held at 12:00 or 6:00 is more popular when 

distracted Cell phone use on arterials is more prevalent than downtown 

or interstate roadway type Crash Data Analysis

Cell phone use while driving has increased Quality of crash reporting may have also increased Social pressures to be constantly connected to others via 

technology

Conclusions (Continued)

Crash Data Analysis Mapping Distraction crashes have increased in areas Percent of distraction crashes has increased

Crash Report Narratives Definition of distraction is not always clear Distraction classified reports do not always have key 

words or phrases appear in narratives Non‐distraction crashes may be incorrectly labeled as 

driver inattention or improper driving

Mapping Results ‐ Distracted Crashes

2012 2013

Mapping Results—Percent of Distracted Crashes

2012 2013

Crash Data Analysis Distracted Crashes for 2012 by Age

19,1077

0

200

400

600

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1200

16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98

Num

berofDistractedCrashes

Age

Crash Data Analysis Distracted Crashes for 2012 Compared to % of Licensed Drivers in Distracted Crashes by Age

0

2

4

6

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Age

Num

berofDistractedCrashes

DistractedCrashes %LicensedDriversinDistractedCrashes

Crash Data Analysis Distracted Crashes for 2012 & 2013 with % Crash Increase

18,1363

0

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20

30

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16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94

PercentCrashIncrease

Num

berofDistractedCrashes

Age

2012 2013 %Increase

Field Observations—Results 

Vehicle Type: Count & Percent of Observations

Vehicle Type Cell Talk Cell

Touch Other No

Distraction Total Commercial 10 3 14 101 128 Mini-Van 9 3 3 60 75 Passenger 57 48 40 765 910 Pick-Up 17 7 8 119 151 SUV 31 13 6 261 311 Total 124 74 71 1,306 1,575

Vehicle Type Cell Talk Cell

Touch Other No

DistractionCommercial 7.81% 2.34% 10.94% 78.91% Mini-Van 12.00% 4.00% 4.00% 80.00% Passenger 6.26% 5.27% 4.40% 84.07% Pick-Up 11.26% 4.64% 5.30% 78.81% SUV 9.97% 4.18% 1.93% 83.92% Total 7.87% 4.70% 4.51% 82.92%

Field Observations—Results 

Gender: Count & Percent of Observations

Gender Cell Talk Cell

Touch Other No

Distraction Total Female 58 37 32 537 664 Male 64 36 39 764 903 Unknown 2 1 0 5 8 Total 124 74 71 1,306 1,575

Gender Cell Talk Cell

Touch Other No

DistractionFemale 8.73% 5.57% 4.82% 80.87% Male 7.09% 3.99% 4.32% 84.61% Unknown 25.00% 12.50% 0.00% 62.50% Total 7.87% 4.70% 4.51% 82.92%

Field Observations—Results 

Age Group: Count & Percent of Observations

Age Group Cell Talk Cell

Touch Other No

Distraction Total 16-19 4 1 1 13 19 20-39 70 54 36 610 770 40-59 46 18 27 507 598 60+ 4 0 7 173 184 Unknown 0 1 0 3 4 Total 124 74 71 1,306 1,575

Age Group Cell Talk Cell

Touch Other No

Distraction16-19 21.05% 5.26% 5.26% 68.42% 20-39 9.09% 7.01% 4.68% 79.22% 40-59 7.69% 3.01% 4.52% 84.78% 60+ 2.17% 0.00% 3.80% 94.02% Unknown 0.00% 25.00% 0.00% 75.00% Total 7.87% 4.70% 4.51% 82.92%

ConclusionsObjective 1: Observe 

Prevalence

Objective 2: Examine Observation Methods

Objective 3: Assess Safety Impacts 

Improved understanding of the magnitude of drivers’ cell phone use among demographics, locations, and driving conditions.

This evaluation can serve as an aid to focus countermeasures aimed to reduce distracted driving.

Evaluate Drivers’ Cell Phone Use

Higher use at signalized intersections as a result of stopped conditions. Observer influence is not significant.

Improved understanding of the prevalence of distracted driving crashes.

Opportunity for Countermeasures

Targeted countermeasures are recommended for populations and conditions that had a higher driving cell phone use and higher distracted driving crash rates: Advertisement based

Females Teens Teen females

Enforcement based Roadways with speed limits 25‐45 Signalized intersections Weekday afternoons CMV

Recommendations for Future Research

Combine cell phone and seat belt observations Continue annually in Massachusetts Encourage other states to combine studies

Improve the quality and accuracy of future observations of cell phone use Include drivers who only text message while stopped as a 

separate category Large scale study to determine the influence of an observer on 

drivers’ cell phone use  Examine non‐intersections to determine the influence of speed 

limit, sinuosity and urbanicity on drivers’ cell phone use.

Drivers’ Cell Phone Use: Intersection Type

0%

2%

4%

6%

8%

10%

12%

14%

Roundabout Signalized Stop Yield

Percen

t of O

bserved Ce

ll Ph

one Use

Handheld Text Messaging

Drivers’ Cell Phone Use: Apparent Race 

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10%

Black Hispanic Other White

Percen

t of O

bserved Ce

ll Ph

one Use

2011 Text Messaging 2012 Text Messaging 2011 Talking 2012 Talking

Comparing to NHTSA

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

All Drivers Female Male Teen Adult Elder

Percen

t of D

rive

rs'O

bserve

d Ta

lking

Talking

NHTSA 2010 MA 2011 MA 2012

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

All Drivers Female Male Teen Adult Elder

Percen

t of D

rive

rs'O

bserve

dTe

xt M

essa

ging

Text Messaging

NHTSA 2010 MA 2011 MA 2012

Drivers’ Cell Phone Use: Age and Gender

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10%

Male Female Male Female

Pad  Talk

Percen

t of O

bserved Ce

ll Ph

one Use Teen Adult Elder

Findings

Results 2012Characteristic

Number Observed Percentage Pad  Percentage Talk

Percentage Combined 

All Drivers 17,677 1.4% 5.6% 7.0%

GenderFemale 7,867 1.5% 6.7% 8.2%Male 9,757 1.4% 4.7% 6.1%

AgeAdult 14,967 1.5% 6.1% 7.6%Elder 1,758 0.3% 1.0% 1.3%Teen 905 3.3% 6.0% 9.3%

Apparent Race

Black 1,006 2.2% 9.1% 11.3%Hispanic 789 2.3% 6.0% 8.2%Other 827 1.2% 5.9% 7.1%White 15,000 1.3% 5.3% 6.7%

Belt UseBelted 12,938 1.2% 5.5% 6.7%Not Belted 4,638 2.0% 5.8% 7.8%

Vehicle Type

Commercial  1,008 1.6% 7.4% 9.0%Minivan 1,106 0.8% 5.6% 6.4%Passenger 9,092 1.4% 5.1% 6.5%Pickup 1,556 0.9% 4.6% 5.5%SUV 4,875 1.7% 6.5% 8.2%

License PlateMA  16,372 1.4% 5.7% 7.2%NH 325 0.6% 4.6% 5.2%Other 936 1.3% 4.2% 5.4%

PassengerGeneral 4,021 0.4% 1.4% 1.9%None 13,612 1.7% 6.8% 8.6%

Time of DayBefore 10 AM 2,152 1.0% 4.6% 5.6%10AM to 3PM 9,382 1.5% 5.1% 6.6%After 3PM 6,143 1.4% 6.8% 8.2%

Day of WeekWeekend 2,363 1.0% 3.2% 4.2%Weekday 15,314 1.5% 6.0% 7.5%

Road TypeLocal 12,032 1.4% 5.4% 6.7%Primary 1,867 1.3% 6.7% 8.0%Secondary 3,778 1.6% 5.8% 7.5%

Region 

Region 1 2,275 1.4% 4.9% 6.3%Region 2 1,801 0.7% 4.8% 5.6%Region 3 3,052 1.3% 4.1% 5.3%Region 4 1,844 1.7% 5.5% 7.2%Region 5 4,270 1.7% 7.6% 9.3%Region 6 2,984 1.7% 5.6% 7.3%Region 7 1,451 1.0% 5.0% 6.0%

Distracted Driving Crashes by Age and Gender 

0%

5%

10%

15%

20%

25%

05000100001500020000250003000035000400004500050000

Teen Adult Elder Teen Adult Elder

Male Female

Percen

t of  Distracted Driv

ing Crashe

s 

Num

ber o

f Distracted Driv

ing Crashe

s

Number of Distracted Driving CrashesPercent of All Crashes that Involved Distracted Driving

Speed & Time

Summary of Drivers’ Cell Phone Use Prevalence ‐ 2017

Field Observations—Results 

Distraction Type “Other”Distraction Type “Other” Count Driver was applying makeup 6 Driver was eating 23 Driver was reading papers 4 Driver was smoking 12 Driver was touching GPS 2 Driver was using Bluetooth 3 Driver was wearing headphones 3 Miscellaneous 6 No description provided 12 Total 71