Motivations for Speeding

Volume I:

Summary Report
DISCLAIMER

This publication is distributed by the U.S. Department of Transportation, National Highway 

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Recommended APA Citation Format:

Richard, C. M., Campbell, J. L., Lichty, M. G., Brown, J. L., Chrysler, S., Lee, J. D., Boyle, L., 

& Reagle, G. (2012, August). Motivations for speeding, Volume I: Summary report. (Report No. 

DOT HS 811 658). Washington, DC: National Highway Traffic Safety Administration. 
 

i

Technical Report Documentation Page 

1. Report No. 2. Government Accession No. 3. Recipient’s Catalog No. 

DOT HS 811 658 

4. Title and Subtitle 5. Report Date 

Motivations for Speeding, Volume I: Summary Report August 2012 

6. Performing Organization Code 

7. Author(s) 8. Performing Organization Report No. 

Christian M. Richard, John L. Campbell, Monica G. Lichty, James L. 

Brown, Susan Chrysler (Texas Transportation Institute), John D. Lee 

(University of Wisconsin, Madison), Linda Boyle (University of 

Washington), George Reagle (George Reagle and Associates) 

 

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) 

Battelle Memorial Institute 

505 King Avenue 

Columbus, Ohio 43201-2696 

 

11. Contract or Grant No. 

DTNH22-06-D-00040, Task#2 

12. Sponsoring Agency Name and Address 13. Type of Report and Period Covered 

National Highway Traffic Safety Administration 

Office of Behavioral Safety Research 

1200 New Jersey Avenue SE. 

Washington, DC 20590 

FINAL REPORT 

August 2008 – December 2011 

14. Sponsoring Agency Code 

 

15. Supplementary Notes 

Geoffrey Collier (initial) and Randolph Atkins (final) were the Contracting Officer’s Technical 

Representatives (COTRs). 

16. Abstract 

This is Volume I of a three-volume report. It contains the results of a study that examined the speeding 

behavior of drivers in their own vehicles over the course of three to four weeks of naturalistic driving in 

urban (Seattle, Washington) and rural (College Station, Texas) settings. The purpose of this research 

was to (1) identify the reasons why drivers speed, (2) model the relative roles of situational, 

demographic, and personality factors in predicting travel speeds, (3) classify speeders, and (4) identify 

interventions, countermeasures, and strategies for reducing speeding behaviors. Data collected from 

164 drivers included 1-Hz recordings of vehicle position and speed using a GPS receiver, responses to 

a battery of a personal inventory questionnaires, and daily driving logs that captured trip-specific 

situational factors. Vehicle speed and position data were combined with road network data containing 

validated posted speed information to identify speeding episodes. The descriptive analysis of speeding 

data provided evidence for different types of speeding behaviors among individual drivers including (1) 

infrequent or incidental speeding, which may be unintentional; (2) trip-specific situational speeding; (3) 

taking many trips with a small amount of speeding per trip (i.e., casual speeding); and (4) habitual or 

chronic speeding. Regression models were developed to identify predictors of “any” speeding (logistic 

regression) and amount of speeding (linear regressions). Significant predictors included demographic 

variables such as age and gender, situational factors such as time-of-day and day-of-week, and key 

personal inventory factors such as attitudes towards reckless driving. In addition, focus group 

discussions were conducted with a subset of study participants who were classified as “speeders” and 

“non-speeders” to identify key attitudes and beliefs towards speeding and the effectiveness of potential 

countermeasures. 

17. Key Words 18. Distribution Statement 

Speeding, Speed Selection, Free-flow Driving, Speeding 

Countermeasures, Unsafe Driving 

Document is available from the 

National Technical Information 

Service www.ntis.gov 

19 Security Classif. (of this report) 20. Security Classif. (of this page) 21 No. of Pages 22. Price 

Unclassified Unclassified 40 

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

Acknowledgements

The Motivations for Speeding project involved a collaborative effort among numerous contributors, and 

only the key researchers are formally included as Final Report authors. We specifically acknowledge the 

efforts and contributions of Diane Williams, Mark Tianow, Ashley Loving, Elizabeth Jackson, Ta Liu, 

and Dale Rhoda of Battelle, and of the following researchers at Texas Transportation Institute: Katie 

Connell, Laura Higgins, and Marshall Ward.
 

Table of Contents 

Background and Introduction ................................................................................................................... 1

General Technical Approach ..................................................................................................................... 3

Overview ................................................................................................................................................ 3

Defining speeding and selecting speed thresholds ................................................................................. 3

Definition of speeding ............................................................................................................................ 5

Results of the Descriptive Analyses ........................................................................................................... 7

Seattle – Percentage of “free-flow” driving time spent in different speed bands relative to the 

posted speed ............................................................................................................................... 7

Texas – Percentage of “free-flow” driving time spent in different speed bands relative to the 

posted speed ............................................................................................................................... 7

Types of speeding – Seattle and Texas ................................................................................................... 9

Results of the Inferential Analyses .......................................................................................................... 15

A summary of the regression modeling ................................................................................................ 15

Basic logistic regression: Who is speeding? ......................................................................................... 15

Logistic regression with personal inventory variables: Who is speeding? ........................................... 16

Basic linear regression: Who speeds the most? .................................................................................... 18

Linear regression with personal inventory variables: Who speeds the most? ...................................... 18

Focus Groups ............................................................................................................................................. 21

Conclusions ................................................................................................................................................ 23

What is the relative importance of situational factors, demographics, and personality in predicting 

episodes of speeding? .............................................................................................................. 23

Situational factors .......................................................................................................................... 24

Demographic factors ...................................................................................................................... 24

Personality ...................................................................................................................................... 26

What are the subtypes of drivers with respect to speeding? ................................................................. 28

To what extent are these classes of speeders defined by demographics? ............................................. 29

To what extent is the predilection to speed correlated with the tendency towards other unsafe 

driving acts? ............................................................................................................................. 29

What attitudes, habits, and behaviors are directly or indirectly related to possible 

countermeasures, so that subgroups can be compared on these measures? ............................. 30

References .................................................................................................................................................. 32

iii
iv

List of Figures

Figure 1. Overview of the approach for studying speeding in drivers and identifying countermeasures.....2

Figure 2. Theoretical speed ranges................................................................................................................4

Figure 3. Illustration of how driving data are extracted from a trip and categorized into free-flow and 

speeding time. ..................................................................................................................................6

Figure 4. Types of speeding based on the scatter plot of the primary dependent measures..........................9

Figure 5. Seattle – Scatter plots of types of speeders in each zone.............................................................11

Figure 6. Texas – Scatter plots of types of speeders in each zone. .............................................................14
v

List of Tables

Table 1. Study design and allocation of participants.....................................................................................3

Table 2. Percentage of “free-flow” driving time in Seattle spent in different speed bands relative to 

the posted speed for each demographic group on 30 to 35 and 55 to 60 mph roads........................7

Table 3. Percentage of “free-flow” driving time in Texas spent in different speed bands relative to the 

posted speed for each demographic group on 30 to 35, 55 to 60, and 70 mph roads.......................8

Table 4. Summary of the five logistic regression models conducted with trip and demographic 

variables. ........................................................................................................................................15

Table 5. Summary of the five logistic regression models conducted with trip, demographic, and 

factor-score variables. ....................................................................................................................16

Table 6. Summary of the significant variables in the five linear regression models conducted with trip 

and demographic variables.............................................................................................................18

Table 7. Summary of the significant variables in five linear regression models conducted with trip, 

demographic, and factor-score variables........................................................................................19

Table 8. Summary of driver responses to the countermeasures presented in the focus groups. .................22

Table 9. Summary of demographic differences based on the likelihood of any speeding on a trip............25

Table 10. Summary of factor-score variables that were significant predictors of speeding behavior and 

variables that approached significance...........................................................................................27

Table 11. List of models in which self-report personal inventory factors related to unsafe acts were 

predictors of speeding. ...................................................................................................................30
1

BACKGROUND AND INTRODUCTION

Although speeding is one of the most significant contributors to crash severity and traffic fatalities, 

attempts to address this problem through a variety of approaches have not led to a significant reduction 

in speed-related fatalities (National Highway Traffic Safety Administration (NHTSA), 2005). NHTSA’s 

(2007) report, “Countermeasures That Work,” lists speeding countermeasures that have been 

demonstrated to be effective, most of which focus on enforcement or punishment to reduce speeding. A 

limitation with these types of countermeasures is that they may not be as effective with some driver

groups, such as risk-taking young males, who rarely consider the potential consequences of their 

behaviors (McKenna & Horswill, 2006). Similarly, National Cooperative Highway Research Program 

(NCHRP) Report 500 provides several engineering-based countermeasures to address speeding 

(NCHRP, 2005). These countermeasures can be effective in reducing speeding at specific locations, but

they can be expensive and only cover small parts of the transportation network, and other non-speeding 

drivers are also affected by these countermeasures. Thus, there is a strong interest in new 

countermeasures to better target identifiable groups of speeders using approaches that specifically and 

effectively address the reasons for their speeding.

The purpose of this research effort was to:

• Identify the reasons why drivers speed;

• Model the relative roles of situational, demographic, and personality factors in predicting travel 

speeds;

• Classify speeders; and

• Identify interventions/countermeasures and strategies for reducing speeding behaviors.

Specifically, this effort focused on identifying “who” is speeding, “what” contributes to their decisions 

to speed, and it attempted to identify “which” countermeasures can be most effectively focused at those 

drivers who account for a significant proportion of the speeding problem. 

The research involved a comprehensive approach for studying speeding in drivers and identifying 

appropriate countermeasures (see Figure 1 below). The field data collection portion drew upon existing 

research to identify driver and situational factors associated with speeding, and used this information to 

develop data collection tools, including a personal inventory questionnaire, a Global Positioning System 

(GPS)-based, on-road speed recorder, and daily driving logs that capture situational factors. The 

information provided by these tools was used to address the objectives listed above. Following Phase 1 

(the field data collection), information about speeder subtypes was used to conduct focus group studies 

(Phase 2) on a subset of participants that matched speeder subtypes. This qualitative information 

addresses the broader project goal of obtaining driver attitudes and behaviors related to 

countermeasures.
2

Figure 1. Overview of the approach for studying speeding in drivers

and identifying countermeasures.

Equipment 

Requirements

Identify 

Countermeasures

Lit

Review

Countermeasure 

Options

Focus Groups

Driver Views on 

Countermeasures

5

Situational Data

Daily Logs

Driver, 

Demographic, 

Urban/Rural 

Characteristics

Survey

Risky Behaviors

Speed/Driving Data

On-road Data

Relative

Importance of 

Situational, 

Demographics and 

Personality 

1

Classification of 

Speeder Subtypes

2

Predictiveness of 

Demographics on 

Speeder Subtype

3

Correlation

between Speeding 

and Unsafe Driving 

Acts

4

•Road Class

•Posted Speed

GIS Data

= Research Questions

Plan Study

Conduct

Conduct Field Study Focus Groups

Define Driving 

Measures

Identify Driver 

Factors
3

GENERAL TECHNICAL APPROACH

Overview

This research was a naturalistic field study in which volunteer drivers from Seattle, Washington, and 

College Station, Texas, agreed to have a GPS unit installed in their vehicles for 3 to 4 weeks. The GPS 

data obtained in the study was used to compare the drivers’ speeds to the posted speed limits, referred to 

here as the posted speeds (PSs), associated with the roads that they were driving on at any given point in 

time. These comparisons between actual speeds and posted speeds formed the basis for the analyses and 

findings described in this report.

A total of 164 participants fully completed the study requirements in Seattle and College Station. Table 1

shows the number of subjects as a function of age, gender, and location.

Table 1. Study design and allocation of participants.

Geography Urban (Seattle, Washington) Rural (College Station, Texas)

Gender Male Female Male Female

Age 18-25 35-55 18-25 35-55 18-25 35-55 18-25 35-55

Subjects 21 25 21 21 20 16 21 19

Defining speeding and selecting speed thresholds

A critical activity in this study was to develop a framework and rationale for defining speeding, and then 

to develop a way to implement the definition using the current data set. As a starting point, we focused 

on developing a behavioral framework that could segment driving into different categories reflecting 

different attitudes about speeding. In particular, four different behavior types were identified that 

correspond to different speed bands relative to the posted speed. These include: 

• Type 1: Driver is intentionally trying to stay below posted speed, which forms the control 

condition (ranges up to posted speed + natural speed variability).

• Type 2: Driver is not concerned with or constrained by posted speed, but is trying to avoid 

getting a speeding ticket (ranges from posted speed to enforcement speed).

• Type 3: Driver is unconcerned or consciously willing to accept some risk (safety and speeding 

ticket) to drive faster (ranges from enforcement speed to what is clearly unsafe).

• Type 4: Driver is traveling at a speed that most other drivers would clearly identify as “reckless”

or too fast for conditions with a clear disregard for the posted speed.

These four types, translated into actual speed ranges (for a 60 mph posted speed), are shown in Figure 2 

below (blue arrow bars), with hypothetical distributions showing speed variability around those ranges.
4

Figure 2. Theoretical speed ranges.

The speeding of interest in this study was defined to be Type 3 speeding (driving 10-20 mph above the 

posted speed) and Type 4 speeding (driving over 20 mph above the posted speed). These values were 

chosen primarily to provide round numbers to define the speeding categories, and give reasonable 

cutoffs for the underlying attitudes and motives that we were trying to capture. As described in more 

detail in Volume II of this report, the initial analyses of the data determined that there were too few Type 

4 speeding events to be analyzed. Therefore, although they likely represent different behaviors, Type 3 

and Type 4 speeding events were combined and are referred to simply as “speeding events” or “speeding 

time” hereafter. Accordingly, the threshold for categorizing driving as “speeding” in this report is 

10 mph above the posted speed.

Defining “speeding” was a necessary first step of the data analyses. However, it still lacks a way to 

reflect exposure and raises the question, “amount of speeding relative to what?” Without a way to 

estimate overall exposure, directly comparing the amount of speeding across drivers, roadway types, 

geographic locations, etc., becomes problematic. To address this problem, a new variable was 

constructed from the data set—“free-flow time”—that could serve as the denominator to the “speeding

time” numerator in our subsequent analyses. The free-flow time variable was carefully defined and 

constructed, with the objective of making sure that the measure reflected a driver’s opportunity to speed. 

For example, time spent warming up a vehicle was not included, nor was time spent idling at a stoplight.

A less obvious consideration is that a drivers’ decisions about speed selection include many instances in 

which the vehicle’s speeds are constrained by traffic control devices or traffic congestion—instances in 

which drivers clearly have no opportunity to speed. If not accounted for, travel time that represents 

stopped time or slower driving could end up being overrepresented in terms of chronological time 

relative to episodes in which drivers are able to select their desired speed, since drivers take longer to 

travel the same distance in the former case. Consequently, comparing the time that drivers spent 

speeding to the total amount of time they drove likely underestimates their speeding exposure by a 

substantial amount because their trip data is “diluted” by a large amount of inconsequential time when 

they were stopped or had no chance to speed.

Type 1

Posted 

Speed

Enforcement 

Speed

60 mph 70 mph 80 mph

Type 2 Type 3 Type 4
 

The approach used to address this dilution of data was to remove from the data set all of the recorded 

time in which drivers clearly had no opportunity to speed. To generate the free-flow driving time 

variable, all driving that was 5 mph or more below the posted speed was removed (e.g., on a roadway 

with a posted speed of 35 mph, only driving time in which the vehicle was traveling at 30 mph or greater 

was retained). Relative to the complete data set, driving time was also filtered to remove driving that 

occurred on unvalidated (i.e., uncertain posted speed) roads, and due to other data-integrity issues, such 

as missing GPS data points. 

In addition, preliminary reviews of the data set using speeding time divided by free-flow time to 

generate a “percentage time speeding” measure revealed considerable variation in speeding behavior 

across trips and trip-level factors such as time-of-day, day-of-week, as well as (as expected) drivers, 

speed bands, and locations. Thus, the inferential analyses examined speeding, in which individual trips 

were nested within drivers. 

Definition of Speeding 

Figure 3 below provides an illustration of how driving data are extracted from a trip and categorized into 

free-flow and speeding time. The topmost bar shows a hypothetical trip, with the purple-shaded regions 

representing free-flow driving on 30 to 35 mph roads, and the green-shaded regions representing freeflow driving on 55 to 60 mph roads. The dark purple and green show the time during which drivers are 

speeding. The unshaded portions of the trip bar represent driving that is excluded from the data for a 

variety of reasons (e.g., driving too slow, driving on roads with unvalidated posted speeds, or roads with 

ineligible posted speeds). The middle set of bars shows the driving periods that are counted as free-flow 

time. Free-flow driving that occurred on different parts of the trip is combined into a single value per trip 

(i.e., part A in the figure). Note that speeding time is also counted as free-flow driving. The bottom set of 

bars represents the speeding time during a trip. All speeding, even if it occurred at different times, is 

combined into a single value per trip (i.e., part B in the figure). 

Speeding on a single trip was specifically defined as: 

5

ൌ ൬

(speedband(Speeding ࢋ࢓࢏ࢀ	ࢍ࢔࢏ࢊࢋࢋ࢖ࡿ

ሺ܌ܖ܉܊܌܍܍ܘܛሻ

ሻ܌ܖ܉܊܌܍܍ܘܛሺࢋ࢓࢏࢚	࢝࢕࢒ࢌࢋࢋ࢘ࡲ

	࢖࢏࢚࢘	ࢎࢉࢇࢋ	࢘࢕ࢌ	 ൰
 

Figure 3. Illustration of how driving data are extracted from a trip 

and categorized into free-flow and speeding time. 

Total trip time

Free-flow time

Speeding time

30-35 mph Free-flow

55-60 mph Free-flow 55-60 mph Speeding

30-35 mph Speeding

Excluded from analysis

Time

A

B

6

The speeding measure was used to calculate average speeding for individual drivers. The specific 

definition of this measure is shown below: 

Average Speeding(speedband)

ሺ܌ܖ܉܊܌܍܍ܘܛሻ

ሻ܌ܖ܉܊܌܍܍ܘܛሺࢋ࢓࢏࢚	࢝࢕࢒ࢌࢋࢋ࢘ࡲ

ൌ ࢋࢍࢇ࢘ࢋ࢜࡭ ൬࢙࢖࢏࢚࢘	࢒࢒ࢇ	࢘࢕ࢌ	 ൰ ࢋ࢓࢏ࢀ	ࢍ࢔࢏ࢊࢋࢋ࢖ࡿ

Average speeding across all of an individual’s trips provides a global measure of the amount of speeding 

those drivers did in a speed band for the entire data collection period. In some ways, it is an incomplete 

measure because it does not count every second of driving in the same way (i.e., it gives short and long 

trips the same weight in the calculation of overall time speeding). We found, however, that this aspect 

actually leads to a better representation of regular driving patterns relative to pooling speeding and freeflow time across all trips (i.e., weighting each second across trips the same). The reason for this is that 

very long trips that reflect atypical driving patterns (i.e., like recreational travel) can skew the results by 

contributing as many seconds of driving as potentially dozens of short, more-typical trips. 
7

RESULTS OF THE DESCRIPTIVE ANALYSES

Seattle – Percentage of “free-flow” driving time spent in different speed bands 

relative to the posted speed

Table 2 shows the distribution of speeds relative to the posted speed across all of the Seattle drivers in a 

demographic group--shown in 5-mile-per-hour bands (e.g., for the category combining 30 mph and 35 

mph roads,“PS+5” represents driving at speeds between 35 to 39.99 mph on a 30 mph road and between

40 to 44.99 mph on a 35 mph road). The bold line between the “PS-5” (mph) and “PS” categories 

indicates the posted speed. Table 2 indicates that the majority of driving occurred within ±5 mph of the 

posted speed. The speeding that did occur was predominately between 10 and 15 mph above the posted 

speed. Almost no speeding occurred at speeds faster than 20 mph above the posted speed. In terms of 

this overall characterization of driving speed, there are some differences between demographic groups, 

but these differences are small.

Table 2. Percentage of “free-flow” driving time in Seattle spent in different speed bands

relative to the posted speed for each demographic group on 30 to 35 and 55 to 60 mph roads.

30-35 mph Roads Travel Speed (mph)

PS-5 PS PS+5 PS+10 PS+15 PS+20 PS+25

Older Females 38.8% 43.7% 14.8% 2.5% 0.2% 0.0% 0.0%

Older Males 34.0% 43.5% 17.8% 3.7% 0.8% 0.1% 0.0%

Younger Females 33.2% 42.3% 20.7% 3.5% 0.2% 0.0% 0.0%

Younger Males 31.9% 44.9% 19.6% 3.1% 0.4% 0.0% 0.0%

Overall 34.4% 43.6% 18.3% 3.2% 0.4% 0.0% 0.0%

55-60 mph Roads

PS-5 PS PS+5 PS+10 PS+15 PS+20 PS+25

Older Females 34.8% 42.4% 19.9% 2.6% 0.3% 0.0% 0.0%

Older Males 31.6% 46.2% 17.6% 3.6% 0.7% 0.1% 0.0%

Younger Females 25.1% 39.9% 28.2% 5.9% 0.7% 0.1% 0.0%

Younger Males 24.1% 42.4% 27.3% 5.1% 0.8% 0.2% 0.0%

Overall 28.9% 42.8% 23.1% 4.4% 0.7% 0.1% 0.0%

Texas – Percentage of “free-flow” driving time spent in different speed bands 

relative to the posted speed

Table 3 shows the distribution of speeds relative to the posted speed across all Texas drivers in a 

demographic group—shown again in 5-mile-per-hour bands. Table 3 indicates that the majority of 
8

driving occurred within ±5 mph of the posted speed. As in Seattle, the speeding that did occur was 

predominately between 10 and 15 mph above the posted speed. Almost no speeding occurred at speeds 

faster than 20 mph above the posted speed in the 30 to 35 and 70 mph speed bands. Although the 

differences were still small, it does appear that the 55 to 60 mph speed band had higher amounts of 

speeding at 15 to 20 and 20 to 25 mph above the posted speed. One aspect of the driving environment 

that may have contributed to the faster driving on 55 to 60 mph roads is that many of these roads are 

physically similar to 70 mph roads in terms of road geometry and design, but they have a lower posted 

speed (e.g., because they are in rural-urban transition areas). Thus, based on roadway characteristics and 

cues, drivers may feel like they can travel at faster speeds on those roads.

Table 3. Percentage of “free-flow” driving time in Texas spent in different speed bands

relative to the posted speed for each demographic group on 30 to 35, 55 to 60, and 70 mph roads.

30-35 mph Roads Travel Speed (mph)

PS-5 PS PS+5 PS+10 PS+15 PS+20 PS+25

Older Females 42.7% 45.1% 10.3% 1.7% 0.1% 0.1% 0.0%

Older Males 37.4% 42.8% 16.9% 2.5% 0.3% 0.0% 0.0%

Younger Females 44.7% 43.5% 10.9% 0.9% 0.1% 0.0% 0.0%

Younger Males 51.4% 39.3% 7.2% 0.8% 0.6% 0.3% 0.3%

Overall 44.0% 42.6% 11.5% 1.5% 0.3% 0.1% 0.1%

55-60 mph Roads

PS-5 PS PS+5 PS+10 PS+15 PS+20 PS+25

Older Females 27.9% 44.8% 13.6% 10.0% 3.5% 0.2% 0.0%

Older Males 31.9% 40.6% 19.3% 6.5% 1.7% 0.1% 0.0%

Younger Females 38.8% 39.2% 16.5% 4.5% 1.0% 0.0% 0.0%

Younger Males 31.6% 40.8% 20.9% 5.5% 1.0% 0.1% 0.1%

Overall 30.9% 42.5% 16.4% 7.8% 2.4% 0.1% 0.0%

70 mph Roads

PS-5 PS PS+5 PS+10 PS+15 PS+20 PS+25

Older Females 47.4% 47.0% 4.8% 0.6% 0.1% 0.0% 0.0%

Older Males 48.0% 41.6% 9.7% 0.6% 0.1% 0.0% 0.0%

Younger Females 49.1% 42.1% 8.1% 0.5% 0.1% 0.0% 0.0%

Younger Males 41.2% 42.3% 15.1% 1.2% 0.2% 0.1% 0.0%

Overall 47.3% 44.7% 7.2% 0.6% 0.1% 0.0% 0.0%

Note: The bold line represents the posted speed limit.
9

Types of Speeding – Seattle and Texas

Information about driver speeding patterns was obtained using two different speeding measures, 

including (1) the percent of trips with any speeding, and (2) the average speeding per trip. These 

measures provide complementary information about driver speeding since they differently reflect 

persistent and situational elements of speeding behavior. Accordingly, we used the two measures to 

characterize different types of speeding at the level of individual drivers. This was done by plotting 

individual driver values for each of the two measures in a scatter plot (for each speed band separately).

A generic example of this approach is provided in Figure 4. In particular, the scatter plot can be divided 

up into different zones that reflect different types of speeding behavior. Zone boundaries are set at 20

percent, but these are arbitrary at this point. They are only included to illustrate potential differences

between groups of drivers. These boundaries divide the drivers into four groups, described below:

• Zone A: Speeding occurs on a small number of trips and for only a small portion of those trips, if 

at all. Speeding here could be unintentional or incidental.

• Zone B: A high level of speeding per trip exists, but it only occurs on a small proportion of trips 

taken. These drivers usually do not speed, but have a few trips with a lot of speeding. This could 

reflect situational speeding (i.e., being late).

• Zone C: Speeding happens on a larger proportion of trips. However, speeding generally occurs 

for just a small part of the trip. This may reflect more systematic behaviors or tendencies.

• Zone D: In this zone, drivers speed regularly and for relatively large portions of their trips. These 

drivers may be characterized as habitual speeders.

Figure 4. Types of speeding based on the scatter plot of the primary dependent measures.

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

0% 20% 40% 60% 80% 100%

Average Speeding per Trip

Percent of Trips with Any Speeding

Small amount of speeding on a 

regular basis [Regular/Casual]

A lot of 

speeding on 

a few trips

[Situational]

Incidental 

speeding

Habitual speeding

A

B

C

D
10

Using this approach, the corresponding scatter plots for the two Seattle speed bands are shown in 

Figure 5, below. Individual drivers are plotted as separate circles on the graphs. An important element in 

these graphs is that the size of each data point corresponds to the number of trips with driving in the 

relevant speed band for that driver. This makes it easier to discount “spurious” data points that may not 

reflect true behavioral patterns because they represent just a small number of observations. Another 

informative element of the scatter plots is the text that indicates the number of points at the origin (i.e., 

“N=9” for the 30 to 35 mph roads and “N=5” for the 55-to-60 mph roads), which represents the number 

of drivers that had no speeding on any trips in that speed band. In Seattle, there were 9 drivers with no 

speeding on 30 to 35 mph roads and 5 drivers that had no speeding on 55 to 60 mph roads.

0%

10%

20%

30%

40%

50%

0% 20% 40% 60% 80% 100%

Average Speeding per Trip

Percent of Trips with Any Speeding

Seattle 30-35 mph

OF

OM

YF

YM

N = 9

Older Female

Younger Male

Older Male

Younger Female
11

Figure 5. Seattle – Scatter plots of types of speeders in each zone.

A distinguishing pattern of the 30 to 35 mph scatter plot is that there are multiple drivers in each zone.

Another finding is that the counts of males are higher in both the “Situational” (Zone B) and “Habitual” 

(Zone D) speeder zones, females are higher in “Incidental” (Zone A), and males and females are fairly 

equally distributed in the “Regular/Casual” zone (Zone C).

The pattern on 55 to 60 mph roadways is clearly different from the pattern on the lower speed roads. In 

particular, most drivers have many trips with a little speeding on those trips. Longer trips in this speed 

band can increase the likelihood of a driver having any speeding, which may account for the higher 

concentration of drivers in Zone C. Nevertheless, engaging in some speeding on a trip is a regular 

occurrence for many drivers. There is also no evidence of “Situational” speeding among the group of 

drivers that speed infrequently (i.e., Zone B is empty), but there is a small group of “Habitual” speeders.

The scatter plots for Texas are shown in Figure 6, below. A notable finding for this location is that the 

scatter plot pattern for each speed band is markedly different from the others. Over one-third of the 

sample had no speeding on these 30 to 35 mph roads. There is also more of a “vertical” distribution of 

data points, which indicates that speeding is relatively uncommon on these roads. However, some 

drivers made a few trips where they sped for significant portions of the trips.

On 55 to 60 mph roadways, most drivers have fewer trips compared to the 30 to 35 mph speed band

(i.e., smaller dots). In general, the speeders occupy a more central part of the graph than in other speed 

bands, with moderate amounts of speeding per trip occurring on a fairly regular basis (30-50% of trips).

Another finding is that females make up the bulk of the “incidental” speeder group (i.e., Zone A).

0%

10%

20%

30%

40%

50%

0% 20% 40% 60% 80% 100%

Average Speeding per Trip

Percent of Trips with Any Speeding

Seattle 55-60 mph

OF

OM

YF

YM

N = 5

Older Female

Younger Male

Older Male

Younger Female

Few trips with free-flow (< 5) Many trips with free-flow (≈100)
12

Similar to the Seattle 55 to 60 mph speed band, there is no evidence of “situational” speeding among the 

group of drivers that speed infrequently (i.e., Zone B is empty).

On 70 mph roadways, most drivers also have fewer trips compared to the 30 to 35 mph speed band (i.e., 

smaller dots). Speeding per trip was substantially lower in this speed band than in any other. The lower 

speeding per trip numbers may reflect the possibility that these averages are depressed by longer trips 

taken on these roads.

In summary, the scatter plots show clear evidence for different types of speeding. Moreover, these types 

are expressed differently based on the types of driving they are associated with (i.e., different locations 

and speed bands), which suggests that roadway and driving conditions have an important impact on 

speeding behavior.
13

0%

10%

20%

30%

40%

50%

0% 20% 40% 60% 80% 100%

Average Speeding per Trip

Percent of Trips with Any Speeding

Texas 30-35 mph

OF

OM

YF

YM

N = 29

Older Female

Younger Male

Older Male

Younger Female

Few trips with free-flow (< 5) Many trips with free-flow (≈100)

0%

10%

20%

30%

40%

50%

0% 20% 40% 60% 80% 100%

Average Speeding per Trip

Percent of Trips with Any Speeding

Texas 55-60 mph

OF

OM

YF

YM

N = 15

Older Female

Younger Male

Older Male

Younger Female

Few trips with free-flow (< 5) Many trips with free-flow (≈100)
14

Figure 6. Texas – Scatter plots of types of speeders in each zone.

0%

10%

20%

30%

40%

50%

0% 20% 40% 60% 80% 100%

Average Speeding per Trip

Percent of Trips with Any Speeding

Texas 70 mph

OF

OM

YF

YM

N = 17

Older Female

Younger Male

Older Male

Younger Female

Few trips with free-flow (< 5) Many trips with free-flow (≈100)
15

RESULTS OF THE INFERENTIAL ANALYSES

A summary of the regression modeling

A mix of logistic and linear regression approaches were used to address two key questions: (1) who is 

speeding? (logistic regression using a binary variable that describes whether or not any speeding 

occurred on a given trip), and (2) who speeds the most? (linear regression using the amount of speeding

per trip, only on trips with any speeding, as the dependent measure). For each of these two approaches, 

we conducted two sets of analyses, the first using demographic (age and gender) and trip (time-of day, 

day-of-week) variables only, and the second using personality inventory variables as well.

Below, the models for the five speed-bands are combined into a single table for each type of regression 

conducted (logistic and linear regression), to facilitate direct comparisons of the findings. An 

abbreviated nomenclature is used to indicate which predictor variables are significantly greater than the 

comparison levels (i.e., Younger Male > Older Female means Younger Males were significantly more 

likely than Older Females to have any speeding in the logistic regression). In addition, to provide a 

better comparison of which personal inventory items are significant across models, each item was 

characterized using a brief phrase. More precise descriptions are available in the main inferential 

statistics results section and the complete personal inventory with response scales is provided in 

Appendix C in Volume III of this report.

Basic logistic regression: Who is speeding?

A summary of the five logistic regression models conducted with trip and demographic variables is

shown in Table 4 below. Because this is an exploratory study with a relatively small sample size, we 

have also included some variables in the tables that are borderline statistically significant at p<.10. This

is useful for comparing results across analyses, especially since the linear regression models in later 

sections had lower statistical power because several drivers had to be excluded for having no speeding.

Table 4. Summary of the five logistic regression models conducted with trip and demographic variables.

Seattle 30-35 mph Seattle 55-60 mph Texas 30-35 mph Texas 55-60 mph Texas 70 mph

Demographic 

variables

− Younger Males > 

Older Females*

− Younger Males > 

Older Males*

− Younger Females >

Older Females†

− Younger Males > 

Older Females**

− Younger Males > 

Older Males*

− Younger Females >

Older Females**

− Younger Females >

Older Males**

− None − Younger Males >

Older Females**

− Younger Males >

Older Females*

− Older Males > Older 

Females†

− Younger Males > 

Younger Females*

Trip variables 

(MR=morning 

rush hour)a

− Wkend > Wkday*

− 9am-3pm > MR†

− Wkend > Wkday**

− MR > 12am-5am***

− 9am-3pm > MR†

− MR > 7pm-12am†

− Wkend > Wkday*

− MR > 9am-3pm**

− MR > 3pm-7pm**

− MR > 7pm-12am*

− None − None

***p<.001; **p<.01; *p<.05; †p<.10

alogFFT (amount of Free-flow time per trip was significant in all models)
16

Demographic Variables: A general pattern in the logistic regression models is that Young Males and 

Young Females (in Seattle) are more likely than older drivers to have any speeding in a trip. This pattern 

is more consistent with Young Males in both locations, and it appears that Young Females may exhibit 

qualitatively different speeding behavior in Seattle and Texas (i.e., Young Females drive more like 

Young Males in Seattle, but more like Older Females in Texas).

Trip Variables: The odds of having any speeding are greater on weekend trips in the more built-up areas 

(Seattle and in-town in Texas). There are also some time-of-day effects in these areas, in which there is 

more likely to be any speeding during the morning rush hour comparison time period than during certain 

other times (this finding is discussed later). An exception to this is the midday time period in Seattle, 

which sometimes is more likely to have speeding than the morning rush hour. Another pattern is that trip 

variables were not significant predictors on Texas high-speed roads (55 to 60 and 70 mph). This might 

reflect generally lower traffic volumes on these roads.

Logistic regression with personal inventory variables: Who is speeding?

A summary of the five logistic regression models conducted with demographic, socioeconomic, trip, and 

factor-score variables is shown in Table 5 below.

Table 5. Summary of the five logistic regression models

conducted with trip, demographic, and factor-score variables.

Seattle 30-35 mph Seattle 55-60 mph Texas 30-35 mph Texas 55-60 mph Texas 70 mph

Demographic 

variables

− Younger Males > 

Older Females**

− Younger Females > 

Older Females**

− Younger Females > 

Older Females***

− None − Younger Males > 

Older Females†

− Younger Females > 

Older Females*

− Older Males > Older 

Females†

Socioeconomic 

variables

− None − Family Vehicle > 

Truck or Sports 

Car†

− None − Income [15-45K] > 

Income [0-15K]*

− Income [45-75K] > 

Income [0-15K]***

− Income [75K+] > 

Income [0-15K]***

− College Degree > 

No College Degree*

− Truck or Sports Car 

> Family Vehicle*

Trip variables 

(MR=morning 

rush hour)a

− Wkend > Wkday† − Wkend > Wkday**

− MR > 12am-5am***

− Wkend>Wkday** − 9am-3pm > MR*

− 3pm-7pm > MR†

− None

Factor score 

variables

associated with 

increased odds 

of any speeding

− Reckless/Road 

Rage*

− Inattention***

− Bad Driving* − Safety† − Reckless*

− Normative Beliefs***

− Reckless**b

− Road Rage***b

− Normative Beliefs**

Factor score 

variables

associated with 

decreased odds 

of any speeding

− Subjective Norms†

− Opportunity***

− Temptation***

− Subjective Norms*

− Opportunity*

− None − Bad Driving*

− Temptation***

− Dangerous 

Driving**

− None

***p<.001; **p<.01; *p<.05; †p<.10

alogFFT (amount of free-flow time per trip was significant in all models)

bReckless and Road Rage were two separate factors in Texas
17

Demographic variables: The inclusion of the socioeconomic variables and factor scores did not affect 

most of the significant demographic predictors in Seattle and Texas; however, some significant variables 

were eliminated on the high-speed roads in both locations (particularly Young Males). Additionally, 

when the socioeconomic and factor scores were considered, Younger Females were significantly more 

likely to speed relative to Older Females in the Texas 70 mph model.

Socioeconomic variables: A range of socioeconomic variables were significant in the different speed 

bands. Owning a family vehicle was a significant predictor of decreased odds of any speeding on 55 to

60 mph roadways in Seattle. Significant predictors of decreased odds of speeding in Texas were lower 

income ($0 to $15K, 55 to 60 mph speed band only), no college degree (70 mph speed only), and 

driving a family vehicle (70 mph speed band only).

Trip Variables: Fewer trip variables were significant than in the base models without the factor scores.

The same pattern of significance for weekend trips still occurred. In addition, the mid-morningtime 

periods were significant in the Texas 55 to 60 mph speed band, and the evening time period began to 

approach significance.

Factor-score variables: Controlling for demographic variables, several factor-score variables were 

significant predictors of whether or not there was any speeding on a trip. As factor-score variables 

associated with increased odds of speeding, Reckless and/or Road Rage1 were significant in three of the 

five speed bands. On the higher speed roads in Texas, Normative Beliefs2 was also associated with 

increased odds of speeding. This may be an indication that drivers who reported being influenced by 

those individuals did not drive with them during the study.

Multiple factor scores were also associated with decreased odds of speeding. In Seattle, Subjective 

Norms (people who are important to them think they should keep near the speed limit) and Opportunity 

(likely to drive the speed limit when there is a limited opportunity to speed) indicated decreased odds of 

speeding in both speed bands. Another interpretation of the latter finding is that those drivers who 

reported being likely to speed when there was not a good opportunity to do so, had greater odds of 

having any speeding on a trip. The factor representing behavior and control beliefs related to temptation 

to speed (Temptation)^3 was a predictor in both 55 to 60 mph speed bands, suggesting that drivers who 

indicated resisting the temptation to speed had lower odds of speeding.3 Bad Driving and Dangerous 

Driving4 also appeared on 55 to 60 mph roadways in Texas. This is a somewhat counterintuitive result, 

since Bad Driving and Dangerous Driving suggest that poorer driving skills and dangerous driving 

habits indicate a decreased likelihood of speeding. However, it could also reflect the possibility that 

speeders believe that they are better drivers than everyone else.

 

1 Reckless Driving is defined as more frequently committing dangerous violations such as racing, driving drunk, and tailgating; Road Rage 

is defined as expressing hostile or angry actions towards other drivers. Note: these definitions are based on question wording in the 

Personality Inventory Questionnaires used in this study.

2 Normative Beliefs are how different groups of people such as parents/kids, spouses, police, etc., would influence drivers to comply with

the speed limit.

3 Temptation refers to being more likely to drive near the speed limit in situations when roadway factors provide temptation to speed.

4 Bad Driving refers to more frequently making driving mistakes that show a lack of skill; Dangerous Driving refers to risk taking 

behaviors (higher factor loadings indicate more dangerous driving behaviors).
18

These patterns illustrate a practical difficulty in broadly applying the current modeling approach in all of 

the speed-band conditions. In particular, drivers in Texas were more heterogeneous than in Seattle in 

terms of their driving patterns across participants. For example, Older Females did substantially more 

driving in outlying rural areas, while many younger drivers did most of their driving in-town on lower 

speed roads. The personal inventory questions, however, do not make specific references to either 

driving environment. Thus, participants may answer the questions with one type of driving/speed band 

in mind (e.g., they may be cautious drivers in town on 30 to 35 mph roads), but if they behave 

differently in other driving environments/speed bands (e.g., driving faster to cover longer rural distances 

on 55 to 60 mph roads), those same questions could be associated with different types of behaviors. An 

example of this could be the Texas 55 to 60 model in which the Bad Driving factor was associated with 

decreased odds of speeding. The specific items associated with this factor (i.e., red-light running, 

missing yield signs, etc.) are actions that would be more commonly expected to occur in-town on 30 to

35 mph roads rather than on 55 to 60 mph roads in rural areas.

Basic linear regression: Who speeds the most?

A summary of the five linear regression models conducted with trip and demographic variables is shown 

in Table 6 below.

Table 6. Summary of the significant variables in the five linear regression models

conducted with trip and demographic variables.

Seattle 30-35 mph Seattle 55-60 mph Texas 30-35 mph Texas 55-60 mph Texas 70 mph

Demographic 

variables

− None − None − None − None − None

Trip variables

(MR=morning rush 

hour)a

− MR > 3pm-7pm* − Wkend > Wkday**

− MR > 12am-5am*

− None − MR > 12am-5am* − None

***p<.001; **p<.01; *p<.05; †p<.10

alogFFT (amount of free-flow time per trip was significant in all models)

Demographic variables: None of the demographic variables were significant predictors of the proportion 

of speeding on a trip. This finding is discussed further in the section on linear regression models with 

factor-score variables below.

Trip variables: There is an inconsistent set of trip variables included across the speed bands at both sites.

In Seattle and Texas on the 55 to 60 mph roadways, there was a higher proportion of speeding in 

morning rush hour trips than in nighttime trips. On 30 to 35 mph roadways in Seattle, there was a larger 

proportion of speeding during the morning rush hour than during the afternoon period. In the Seattle 55

to 60 mph speed band, trips taken on the weekend had more speeding than those during the weekdays, 

but otherwise this variable was not a significant predictor in other speed band models.

Linear regression with personal inventory variables: Who speeds the most?

A summary of the five linear regression models conducted with demographic, socioeconomic, trip, and 

personal inventory variables is shown in Table 7 below.
19

Table 7. Summary of the significant variables in five linear regression models

conducted with trip, demographic, and factor-score variables.

Seattle 30-35 mph Seattle 55-60 mph Texas 30-35 mph Texas 55-60 mph Texas 70 mph

Demographic 

variables

− None − None − None − None − None

Socioeconomic 

variables

− None − None − None − None − None

Trip variables 

(MR= morning 

rush hour)**

− MR > 9am-3pm†

− MR > 3pm-7pm*

− MR >7pm-12am*

− Wkend > Wkday**

− 12am-5am > MR**

− 9am-3pm > MR**

− 3pm-7pm > MR**

− 7pm-12am > MR**

− None − MR > 12am-5am* − None

Factor variables

associated with 

larger proportion 

of any speeding:

− None − None − Reckless†

− Road Rage**

− Normative Beliefs** − None

Factor variables

associated with 

smaller 

proportion of any 

speeding:

− Temptation†

− Subjective Norms†

− Temptation***

− Subjective Norms**

− Dangerous 

Driving*

− Bad Driving† − None

***p<.001; **p<.01; *p<.05; †p<.10

alogFFT (amount of Free-flow time per trip was significant in all models)

Demographic variables: The demographic variables were poor predictors of the proportion of speeding 

in a trip. Part of this may arise from the loss of statistical power that occurred once drivers and trips with 

no speeding were removed from the data set. However, another factor may be that the set of “high 

speeders” in each location contains drivers from all demographic categories (see also the scatter plots 

presented above). Taken together, this pattern of results suggests that the factors that predict how much 

speeding a driver does are more complicated than a simple demographic-category membership.

Socioeconomic variables: None of the socioeconomic variables were significant predictors of the 

proportion of speeding.

Trip variables: In Seattle, more of the trip variables were significant in the linear regressions, whereas in 

Texas they stayed the same. For the time-of-day variables in Seattle, more time periods were associated 

with a decreased proportion of speeding compared to morning rush hour, on 30 to 35 mph roadways.

This may be related to the type of trips that drivers make during this time. In particular, drivers going to 

work may be under greater pressure to avoid being late and consequently more willing to drive faster if 

they get any opportunity to do so. However, on the 55 to 60 mph roadways in Seattle, driving at other 

times was associated with more speeding than driving during morning rush hour.

Factor-score variables: Most of the models had factor variables that were significant predictors of the 

proportion of speeding during a trip. Many of these variables are the same as they are in the logistic 

regression models, though in general the majority of the factors from the logistic regression models were 

not significant in the linear regression models. In Seattle, both speed bands had the same predictive 

factors. Temptation (resisting temptation to speed) and Subjective Norms (people who are important to 

them think they should keep near the speed limit) predicted a smaller proportion of speeding. The Texas 
20

30 to 35 mph model showed that the Reckless factor (dangerous violations) and Road Rage (hostility 

and anger toward others) both predicted a greater proportion of speeding, while Dangerous Driving 

(dangerous driving habits) predicted a smaller proportion of speeding. The inclusion of Dangerous 

Driving as a predictor of a decreased proportion of speeding is counterintuitive.

Another finding is that the Texas 70 mph model had no significant factor predictor variables. This 

suggests that different types of variables may be stronger predictors of the proportion of speeding on 

these roads (i.e., geometry, roadway design, driving comfort, vehicle power) than the ones investigated 

in the current analysis.
21

FOCUS GROUPS

In Phase 2 of the Motivations for Speeding project, focus groups were conducted to gain insight about 

drivers’ attitudes, beliefs, and motivations regarding speeding. The focus groups also gathered data on 

driver opinions about the challenges and benefits of various countermeasures to identify those that 

would be most effective with both risky speeders and opportunistic speeders. The objectives for the 

focus groups were to:

• Add to our understanding of speeding and speeders, over and above what was learned in Phase 1.

• Develop a more accurate taxonomy of high/low speed driver subgroups.

• Better understand the motives—as well as attitudes and habits—of these subgroups.

• Explore attitudes and behavioral influences pertinent to various countermeasures (e.g., increased 

enforcement, engineering and in-vehicle countermeasures, automated enforcement, and speed 

awareness courses) as well as the acceptance and potential effectiveness of the countermeasures.

• Descriptions of key motivations, attitudes, normative commitment to law, driving habits relevant 

to speeding and speeding countermeasures.

These objectives were addressed in four focus groups held in the two on-road data collection locations 

(two in each location) using drivers that participated in Phase 1 of this project. As best as possible, the 

sessions at each location were separated by speeder type. The methodology and findings are discussed in 

more detail in Volume II of this report. A summary of the key themes of the focus group responses to the 

countermeasures are presented in Table 8, below.

In the focus groups, there were differences in how some drivers interpreted the posted speed limits. In 

particular, the most common views were that the posted speed was either a maximum speed (firm limit), 

a target speed, or more of a minimum speed. Drivers in all groups understood the posted speed 

technically to be a maximum speed, and non-speeders in both locations reported that they considered it 

to be a hard limit. A contrasting view, shared by some drivers in all groups, was that the posted speed 

was more of a target speed than a maximum. A related view, only mentioned in the speeder groups, was 

that the posted speed was more of a minimum speed. In summary, the posted speed is technically 

perceived as a limit, but drivers have different interpretations of how fast it means they can drive.

Almost all drivers believed that they could exceed the posted speed limit before having to worry about 

getting a speeding ticket; however, there were differences in opinions regarding how far above the 

posted speed they could travel (referred to as the Ticket Speed), especially on different types of roads. 

Some drivers in most groups mentioned the relationship between speeding and safety. As their speed 

increases, their safety risk generally increases, but drivers in all groups believed that there was often a 

disconnect between the posted speed/ticket speed, and how fast they thought they could safely travel. 

Drivers identified a few factors that caused them to drive faster. These included situational or tripspecific factors (such as being late for work), social pressure from other drivers, inattention to driving, 

and positive feelings about driving fast. Focus group participants also identified several factors that 

made them less likely to speed. These included ticket or crash risks, social pressure, and critical events 

that they had experienced in the past. 
22

Table 8. Summary of driver responses to the countermeasures presented in the focus groups – participants’ views on 

how and why countermeasures may be effective or ineffective in reducing speeding.

Countermeasure

Effective Ineffective Comments

Higher Penalties and Increased Enforcement

Higher 

Penalties

Target habitual, deliberate speeders One-time higher bill is paid and then 

forgotten

Would be more effective if insurance 

rates increased or licenses were lost

Increased 

Enforcement

Seeing police officers or police cars 

seems to cause speed reductions; 

interaction with police officers is very 

unpleasant

None Erratic driving around police officers 

may increase; could involve a positive 

action such as a free license renewal 

if they’ve received no tickets

Engineering Countermeasures

Speed

Tables

Cause discomfort; unavoidable; can’t 

habituate

Can drive around them in a parking 

lane; can find an alternate route; not 

always effective at higher speeds 

(particularly in an SUV/truck)

May lead to rear-end collisions due to 

unnecessary slowing

Pavement 

Markings

May make noise so police can hear 

that you’re driving too fast

Seem ineffective; may cause speed 

increases due to their design

None

Speed

Displays

Very noticeable; more salient than 

regular speed limit signs; can lessen 

or increase social pressure to reduce 

speed; counter inattention

Forgotten soon after passing them; 

increase speed after passing them

Could be moved around to be in 

unfamiliar locations; should target 

specific areas with many hazards

Vehicle-based Countermeasures

Speed

Limiter

Good for target populations such as 

teens or habitual speeders

None Perhaps the licenses of habitual 

speeders should just be taken away; 

sometimes extra speed is needed to 

pass or avoid hazards; violates driver 

rights and independence

Speed Limit 

Display

Is useful and provides positive

reinforcement; informs drivers of the 

posted speed when it’s unknown or 

when signs are blocked; helps drivers 

manage their speed choice risk

GPS locations are sometimes 

inaccurate; drivers go with the flow of 

traffic anyway; if drivers have decided 

to speed then they’ll still speed; 

redundant with the roadway signs

Might cause driver distraction; 

vehicles could auto-report speeders to 

the police with this kind of information

Fuel Economy 

Display

Like a game; saves drivers money; 

provides real-time driving feedback

Some vehicles just get bad mileage; 

wealthy drivers won’t care

Might cause driver distraction

Automated Enforcement

Automatic 

Enforcement

Drivers would be unable to anticipate 

signs in random locations

The fixed location cameras might just 

lead to speed in other areas; the 

length of time between the infraction 

and the ticket reception is too long

Drivers don’t have a chance to state 

their case with a police officer; 

cameras are not always accurate 

(e.g., red light cameras); may cause 

driver distraction; drivers may get 

tickets when someone else was 

driving their vehicle

Speed Awareness Course

Speed 

Awareness 

Course

Raises awareness of speeding 

behavior

Drivers don’t take the courses 

seriously

Need a higher time or monetary cost; 

should require mandatory in-person 

attendance; should show more 

graphic crash footage; educational for 

all drivers (not just speeders)
23

CONCLUSIONS

This study sought to gather information about driver speeding behavior. Using naturalistic driving data 

combined with data from focus groups, several basic questions about driver speeding patterns were 

examined. These include:

1. What is the relative importance of situational factors, demographics, and personality in 

predicting episodes of speeding?

2. What are the subtypes of drivers with respect to speeding? 

3. To what extent are these classes of speeders defined by demographics?

4. To what extent is the predilection to speed correlated with the tendency towards other unsafe 

driving acts?

5. What attitudes, habits, and behaviors are directly or indirectly related to possible 

countermeasures (e.g., points systems, points reduction classes, checkpoints, automated 

enforcement, crackdowns, traffic calming measures, etc.), so that subgroups can be compared on 

these measures?

The findings related to each of these questions are discussed below.

What is the relative importance of situational factors, demographics, and 

personality in predicting episodes of speeding?

Using the criterion of 10 mph above the posted speed as the working definition of “speeding,” drivers

engaged in speeding behavior during approximately 1 to 10 percent of their free-flow driving. However, 

that amount depended on a number of factors. For example, in Seattle, drivers sped during less than 5

percent of their free-flow driving time overall, and this amount was slightly higher on 55 to 60 mph 

roads than on 30 to 35 mph roads. In Texas, there was very little speeding on 30 to 35 mph (≈2%) and 

70 mph roads (≈1%), but substantially more on 55 to 60 mph roads (≈10%).

These findings provide a useful frame of reference for speeding behavior, but they obscure a more 

important finding, which is the variability in speeding across situational/trip-specific factors, 

demographic groups, and individual driver/personal characteristics. The majority of the analyses in the 

current report were focused on finding ways to quantify speeding behavior and identify different 

speeding patterns. This is a significant challenge with naturalistic data because of the wide range of 

driver-related, roadway-related, and situational/trip factors that cannot be controlled—or even fully 

documented—but are strong determinants of speeding behavior during a trip. Despite this challenge, 

some key trends in terms of speeding behavior were identified, including:

• It is relatively common for drivers to have at least some speeding on a given trip; however, there 

are some drivers that rarely speed.
24

• Younger drivers, especially Young Males, are more likely to have at least some speeding on a 

trip than other demographic groups.

• Certain driver beliefs and attitudes (described below) are associated with increased odds of any 

speeding, and a greater amount of speeding on individual trips.

• In general, there was a small set of habitual speeders in both Seattle and Texas, but this group 

seemed to be defined more by certain beliefs and attitudes, rather than by their demographic 

category.

• There is evidence for different types of speeding behaviors (i.e., situational, habitual, casual, and 

incidental speeders), and their relative prevalence—at least in part—depends on road type.

• Weekend and morning trips are more likely to be associated with speeding.

It is also interesting to consider some common views about speeding that were not borne out by the data:

• Speeding does not appear to be limited to a small group of drivers defined primarily by age (i.e., 

young) and/or gender (i.e., males).

• Speeding does not appear to be limited to a small group of drivers defined primarily by attitudes 

(i.e., reckless, risk-seeking, etc.).

• Speeding does not appear to be limited to rural or high-speed roadways (i.e., 55 to 60, 70 mph).

Speeding is clearly a complex behavior that is strongly influenced by a number of factors. Some of the 

specific situational, demographic, and personal inventory factors related to speeding behavior are 

discussed below. 

Situational factors

As listed above, there were a number of significant effects associated with pervasive variables like 

age/gender and personality, but it seems likely that situational variables play a key role as well (e.g., 

type of trip, length of trip, presence of others in the car, presence of enforcement, opportunities to speed, 

road conditions, geographic location, etc.). Opportunity to speed is undoubtedly a key factor in this, as 

evidenced by the prevalence of the free-flow time (logFFT) and weekend predictors of speeding. In 

addition, the focus groups indicated that speed seems to be a deliberate choice made with consideration 

of outcomes (tickets, safety, approval of others, thrills, etc.), which highlights the influence of situational 

factors. We have paid the least attention (in terms of our data analyses) to these situational variables; 

some of them cannot be properly measured or characterized with the existing data set. But some can, 

and should be looked at in future analyses.

Demographic factors

A key objective of this research was to examine the extent to which speeding behavior can be predicted 

based on demographics. In terms of the descriptive analyses, this study found that Older Females as a 

group are typically not among the high speeders, whereas Males in general are more likely to speed. 

These findings should be interpreted cautiously; however, since driving patterns differ among the 
25

demographic groups and the inferential analyses indicate that, once trip variables and driving patterns 

are accounted for, these relationships change.

In terms of the inferential analyses, clear and consistent results were found with regard to the

percentage of trips with any speeding. A summary of the significant demographic predictors of any 

speeding is shown in Table 9 below. It is clear from this table that younger drivers, especially Young 

Males, have significantly greater odds of having any speeding than most other demographic groups. In 

Seattle, Young Females show similar significant effects to the Young Males as compared to Older Males 

and Older Females, but not in Texas (as compared to any other group). In Texas, Young Males also have 

significantly higher odds of speeding than Older Females on both 55 to 60 mph roads and 70 mph roads, 

and Older Males exhibited higher odds of speeding relative to Older Females on the 70-mph roads.

There were also other effects that approached significance. In Seattle, Young Females had greater odds 

of speeding than Older Females. In Texas, Older Males were more likely than Older Females to speed.

Table 9. Summary of demographic differences based on the likelihood of any speeding on a trip.

Speed 

Band

Likelihood of Any Speeding

Seattle Texas

30-

35 mph 

− Younger Males 2.4 times more likely than Older Females*

− Younger Males 2.1 times more likely than Older Males*

− Younger Females 1.9 times more likely than Older Females†

− None

55-

60 mph 

− Younger Males 4.0 times more likely than Older Females**

− Younger Males 3.2 times more likely than Older Males*

− Younger Females 4.5 times more likely than Older 

Females**

− Younger Females 3.6 times more likely than Older Males**

− Younger Males 4.1 times more likely than Older Females**

70 mph − Younger Males 3.0 times more likely than Older Females*

− Older Males 2.0 times more likely than Older Females†

− Younger Males 2.8 times more likely than Younger 

Females*

***p<.001; **p<.01; *p<.05; †p<.10

These patterns, however, only seem to hold for the speeding measure related to percentage of trips with 

any speeding. More specifically, the linear regression analyses conducted on speeding per trip indicated 

that none of the demographic variables were significant predictors of the amount of speeding per trip, in 

any speed band. It is unclear at this time why demographic variables are relatively poor predictors of the 

amount of speeding, although the low statistical power arising from the relatively small number of trips 

with any speeding was likely a factor. 

A consistent pattern across most speed bands is that the clusters of habitual-speeders are often 

comprised of drivers from all demographic groups. In this case, it seems plausible that large amounts of 

speeding on a trip reflect certain personality traits, attitudes, or situational variables, rather than simple 

demographic category memberships. This is consistent with the findings from the linear regression 

analyses (i.e., there were significant personal inventory factor predictors found in most linear regression 

models) and the focus groups (i.e., drivers described a wide range of situational variables that affect 

their speeding behavior).
26

An additional set of variables examined in the regression models were the socioeconomic variables such 

as income, education, and vehicle type. In some way, the different sites represent demographic 

differences based on differences in driving experiences and cultural views. The analyses indicated that 

some socioeconomic variables were significant predictors of speeding in the logistic regressions that 

included personal inventory factors, but not the linear regressions. Also, different patterns of results were 

obtained between Seattle and Texas. For example, in Texas, driving a truck or sports car was associated 

with higher odds of any speeding, whereas the opposite was true in Seattle. Additionally, having 

completed a college degree and higher income levels were also associated with increased odds of any 

speeding in Texas. It is unclear if the vehicle-type measure reflected vehicle power or cost, but if it is the 

latter, either directly or indirectly, then there is a trend towards speeding being associated with affluence 

in Texas.

Personality

Driver motivations, beliefs, and attitudes were found to be highly significant predictors of both the odds

of any speeding on a trip (who speeds), and the amount of speeding on a trip (who speeds the most). In 

some instances, these variables were generally stronger predictors than the demographic variables; when 

adding these personality variables to the regression models, many of the demographic variables were 

eliminated from the final regression models.

The Seattle regression models that incorporated factor score variables were more interpretable than the 

Texas models. In Seattle, factors related to dangerous/aggressive driving, inattentive driving, and bad 

driving habits were associated with increased odds of any speeding (see Table 10 below). In contrast,

agreement that important people would approve of their driving near the speed limit and tendencies to 

resist the temptation to speed were associated with decreased speeding (both odds and amount of 

speeding) for this location.

The Texas regression models found some of the same factors to be significant as the Seattle regression 

models. Factors related to dangerous/aggressive driving were associated with increased odds of speeding 

and those related to resisting the temptation to speed were associated with a decreased odds of speeding 

(in some speed bands). However, the Texas data yielded regression models that also had a few 

counterintuitive predictors. In particular, some factors that might be expected to be associated with 

increased odds of speeding were actually associated with decreased odds, and vice versa. For example, 

reporting more frequently making driving mistakes (e.g., missing yield signs, misjudging the road 

surface) that show a lack of skill was associated with decreased odds of speeding and a smaller 

proportion of speeding on 55 to 60 mph roadways. Also, more frequently engaging in dangerous driving 

habits (e.g., accelerating at yellow lights, taking more risks when in a hurry) was associated with 

decreased odds of speeding on 55 to 60 mph roadways and a smaller proportion of speeding on 30 to 35

mph roadways (see Table 10). However, this pattern of results could also be explained if speeders 

simply believe that they are better drivers than everyone else and do not view their own driving as 

unskilled or dangerous.

As detailed in the discussion of these findings in Results chapter in Volume II of this report, these 

patterns in the Texas data illustrate some of the practical difficulties in trying to apply global measures 

of driver factors (i.e., personal inventory questions) to speeding behaviors that occur in specific contexts. 

If participants answered the personal inventory questions with one type of driving in mind (e.g., they 
27

may be cautious drivers in town), but then behave differently in other driving environments (e.g., 

driving faster to cover longer rural distances), then the counterintuitive results could be explained. 

However, the personality predictors become more difficult to interpret for this speed band, since they 

may reflect attitudes and beliefs about a different driving environment than that in which the speeding 

was measured. In other words, when completing the personal inventory, participants may have had some 

specific driving context in mind that was not specified in the inventory questions; however, when we 

examine these personal inventory items in the context of driving on a specific road type, it may not be 

the same context the participant had in mind when doing the inventory.

Table 10. Summary of factor-score variables that were

significant predictors of speeding behavior and variables that approached significance.

Seattle

30-35 mph

Seattle

55-60 mph

Texas

30-35 mph

Texas

55-60 mph

Texas

70 mph

Logistic Regression (Predicting likelihood of any speeding on a trip)

Factor score 

variables associated 

with increased odds 

of any speeding

− Reckless/Road 

Rage*

− Inattention***

− Bad Driving* − Safety† − Reckless*

− Normative Beliefs***

− Reckless**

− Road Rage***

− Normative Beliefs**

Factor score 

variables associated 

with decreased

odds of any 

speeding

− Subjective 

Norms†

− Opportunity***

− Temptation***

− Subjective 

Norms*

− Opportunity*

− None − Bad Driving*

− Temptation***

− Dangerous Driving**

− None

Socioeconomic 

variables

− None − Family Vehicle >

Truck or Sports 

Car†

− None − Income [15-45K] > 

Income [0-15K]*

− Income [45-75K] > 

Income [0-15K]***

− Income [75K+] > 

Income [0-15K]***

− College Degree >

No College 

Degree*

− Truck or Sports 

Car > Family 

Vehicle*

Linear Regression (Predicting average amount of speeding on a trip, for trips that have speeding)

Factor variables

associated with 

larger proportion of 

any speeding:

− None − None − Reckless†

− Road Rage**

− Normative Beliefs** − None

Factor variables

associated with 

smaller proportion of 

any speeding:

− Temptation†

− Subjective 

Norms†

− Temptation***

− Subjective 

Norms**

− Dangerous 

Driving*

− Bad Driving† − None

Socioeconomic 

variables

− None − None − None − None − None

***p<.001; **p<.01; *p<.05; †p<.10

Nevertheless, although the previous section shows that demographic variables may be sufficient to 

predict some speeding trends, the significance of the personal inventory factors suggests a more 

complicated relationship between speeding and demographics. It is likely that there are some attitudes 

and beliefs—which may be thought of as primarily age related—that are more relevant to speeding than 

age alone. This was apparent in the focus group discussions of some older drivers who speed. In contrast 

to the more safety-conscious views of their non-speeder peers, the older speeders expressed views 
28

usually associated with prototypical young speeders – such as describing speeding as an adrenaline rush. 

However, the number of participants in each demographic category was not large enough to examine 

personal inventory predictors of speeding separately within each group and to better map out the 

relationship between these views and speeding behavior.

Another notable finding is that demographic groups also differed in terms of their answers to several of 

the personal inventory questions. This finding was not examined in detail, but the sample personal 

inventory questions presented in the descriptive statistics section show stereotypical differences across 

groups in terms of risk-taking, attitudes, motivations, and behaviors. These differences may explain 

some of the patterns found in the regression models that included factor score variables.

What are the subtypes of drivers with respect to speeding?

Another interesting set of findings involved characterizing drivers based on scatter plots of the 

proportion of trips with any speeding versus the average speeding per trip. Different combinations of 

these measures generally can be mapped to different types of speeders reflecting:

• Incidental/non-speeders: drivers who speed on a small number of trips and for only a small 

portion of those trips, if at all;

• Situational speeders: drivers who speed a lot on a small number of trips;

• Regular/casual speeders: drivers who speed a small amount on a large proportion of their trips; 

and

• Habitual speeders: drivers who speed regularly for relatively large portions of their trips.

It may be possible to map these speeder types to specific situational aspects, such as being late, or more 

enduring factors, such as personality traits associated with chronic speeding. There was also some 

evidence of these speeder types found in the focus group discussions. For example, incidental/nonspeeders, which were targeted in the non-speeder sessions, were more likely to describe the posted speed 

as a maximum limit. Non-speeders also described how if they sometimes ended up speeding because 

they got caught up with the flow of traffic, they would likely slow down once they realized they were 

doing so. A couple of drivers also described engaging in “situational” speeding on long trips only, since 

only on those trips would speeding result in an appreciable time savings. 

However, it should be noted that on 55 to 60 mph roadways in both Seattle and Texas, there were no 

situational speeders. There are multiple possible explanations for this finding. The drivers who fall into 

this group may be pragmatic about their decision to speed, and only speed when they can save a 

noticeable amount of time (i.e., on longer trips). These trips were likely excluded from the analysis set 

because they occurred on roadways that were very far away from the installation sites, and therefore on 

roadways for which the speed limits were not validated and, therefore, the data could not be included in 

the analysis. Another explanation could be that the demarcation for situational speeding indicated in 

these graphs is set too high for this speed band. Alternatively, drivers may be taking advantage of only 

shorter duration speeding in these zones, which may not appear on the graphs due to how this measure

(speeding per trip) is calculated.

At this time, the demarcation into these categories is somewhat arbitrary; however, the relative 

distribution of individual points within the scatter plots is fixed, providing a useful indication of general 
29

patterns. On the other hand, if it were possible to develop a defensible speeding-type classification 

scheme, then this approach could form the basis for a classification-based analyses that could identify 

the personal inventory, driving behavior, and/or other variables that form the defining characteristics of 

each speeder type.

To what extent are these classes of speeders defined by demographics? 

The classes of speeders described above are not well defined by demographics. Drivers from most 

demographic groups are represented in all of the speeder classes. There were differences across speed 

bands in the distribution of drivers across categories; however, with the exception of Older Male 

Situational speeders on Seattle 30 to 35 mph roads, it is difficult to identify clear clustering in speeder 

categories based on demographic group.

Another related finding is that the distributions of individuals across the different speeder types 

depended greatly on the particular speed band. Specifically, different speed bands in each location 

tended to show systematic patterns in terms of where individuals ended up in the scatter plots. These 

patterns may reflect global driving conditions (e.g., trip duration, roadway design) that lead to fast 

driving. The consistency of the classification of individual drivers across different speed bands was not 

examined.

To what extent is the predilection to speed correlated with the tendency towards 

other unsafe driving acts? 

The primary measures of unsafe driving acts collected in this project were the self-report personal 

inventory factors calculated from related item responses on the personal inventory instruments. Three 

factors that had relatively direct mappings to unsafe acts included the following:

• Reckless Driving: Items that deal with dangerous violations such as racing, driving drunk, and 

tailgating

• Road Rage: Items that involve showing hostility and anger to other drivers

• Dangerous Driving: Items related to risk taking behaviors such as accelerating through a yellow 

light, taking risks when in a hurry, and cutting off other drivers

Overall, the findings regarding the relationship between these factors and the predilection towards 

speeding are mixed. In particular, the Reckless Driving and Road Rage factors were among the better 

predictors of increased speeding behavior, across all factors (see Table 11). However, the Dangerous 

Driving factors, based on the DeJoy/Cards Risky Driving Scale’s risk taking items, were associated with 

lower odds of speeding in some of the Texas models, but not associated with higher odds speeding in 

any of the models.
30

Table 11. List of models in which self-report personal inventory factors

related to unsafe acts were predictors of speeding.

Factor Positive Correlation With Speeding Negative Correlation With Speeding

Reckless 

Driving

− Seattle 30-35 mph Logistic Regression*

− Texas 55-60 mph Logistic Regression*

− Texas 70 mph Logistic Regression**

− Texas 30-35 mph Linear Regression†

− None

Road Rage − Texas 70 mph Logistic Regression***

− Texas 30-35 mph Linear Regression**

− None

Dangerous 

Driving

− None − Texas 55-60 mph Logistic Regression**

− Texas 30-35 mph Linear Regression*

***p<.001; **p<.01; *p<.05; †p<.10

Interestingly, the comments from the focus groups do not support the mapping between speeding and 

other unsafe driving acts. When drivers (particularly speeders) discussed their speeding behavior, they 

described how—when they were speeding—they felt that it was safe for the condition of the roadway on 

which they were travelling (e.g., good visibility, wide roads). The crashes or near-misses that they 

experienced generally changed their driving behavior in related driving environments, but not in all 

situations. Additionally, some drivers expressed the opinion that the posted speed is more of a minimum 

speed, or that it is set by considering the worst drivers in the population. Very few drivers expressed 

frustration at other slower-moving vehicles (Road Rage). Therefore, there were some factors correlated 

with speeding behavior that drivers did not mention in the focus groups. However, the focus groups 

were primarily a discussion of general speeding behavior, rather than unsafe acts specifically.

What attitudes, habits, and behaviors are directly or indirectly related to possible 

countermeasures, so that subgroups can be compared on these measures?

The analyses conducted in this project do not provide a satisfactory answer to this question. There was 

simply too much diversity in the factors that were associated with speeding, either in the regression 

analyses, or in the qualitative focus group discussions. Part of this may be due to our decision to select 

participants from a broad driver population, including groups such as Older Females that are not 

typically thought of as speeders. While this decision was consistent with the exploratory nature of this 

study, it likely made it difficult for potentially strong predictors of speeding to stand out among other 

factors because they may have been represented by only a small proportion of participants. Related to 

this is the fact that participants were not screened for markers of speeding behaviors, such as previous 

speeding tickets or certain attitudes. Consequently, there were no more than a few potentially 

“egregious” speeders that might have represented the rebellious or risk-taking attitudes typically 

associated with chronic speeding. Another contributing issue is that the small number of drivers 

participating in the focus groups makes it difficult to sort out central, commonly-held opinions from 

esoteric, isolated views. This leaves us with insufficient information to evaluate adequately the relative 

merits of different countermeasures.

However, one thing that the findings show—in terms of countermeasures—is that no single “catch all” 

solutions emerged in the focus group discussions that could address the multiple facets of speeding as a 

whole. A repeated theme in the focus groups was that countermeasures were limited in terms of their 
31

geographic distribution (i.e., dynamic speed signs and police officers cannot be everywhere that people 

speed all the time). However, there are some ways to improve on these limitations by introducing 

unpredictability into where these measures are located. Another, related limitation of other 

countermeasures is that they are fixed in time rather than location. These include speeding awareness 

classes, public education campaigns, and to some extent infrequent encounters with police. The impact 

of these measures on drivers likely diminishes over time as drivers experience repeated trips without 

new reinforcement of the associated safety beliefs. For example, if a new driver leaves driver education 

successfully “scared” of the risks of speeding, repeated exposure to driving above the posted speed in 

harmless situations (i.e., inattention or keeping up with traffic) may undermine this driver’s original 

concerns.

The one class of countermeasures that is less affected by the limitations described above is in-vehicle 

devices. Since these devices are with the driver all the time, their “impact” is immediate and more 

relevant in time and location, assuming that drivers do not ignore them (in the case of the displays). The 

speed limiter (described to focus group participants as an instrument installed in the engine that would 

limit engine speeds) had low acceptance and is probably impractical to deploy on a wide scale. 

However, the speed limit and fuel efficiency displays are intriguing for three reasons. The first is that the 

participants in the focus groups liked the idea of speed limit and fuel efficiency displays and these 

displays seemed to address some of their desires for countermeasures to provide a reward rather than 

just a punishment (Note: none of these countermeasures were used in this study). The second is that they 

appear to indicate that situational factors may play a greater role in speeding than we originally 

anticipated; specifically, they provide useful and relevant information to the driver at all times, 

regardless of the specific circumstances. And third, they have not been deployed extensively in the 

United States, and at least have the merit of being a new approach to addressing the speeding problem.

They have been shown to provide a significant mean reduction in vehicle speeds in some posted speed 

zones (Regan et al., 2006). 
32

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810 710). Washington, DC: National Highway Traffic Safety Administration. Available 

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