ABSTRACTS OF RESEARCH CONDUCTED BY
JEFF MUTTART & WHERE TO OBTAIN COPIES
Driving
Simulator Evaluation of Driver Performance During Hands-Free Cell Phone
Operation in a Work Zone: Driving Without a Clue
Estimating Driver Response Times -
(Book)
Driver
Response Choice: A Comparison of Actual Responses to Decision-Making Theory
DRIVE3:
A Simplified Method for Estimating Driver Response
Evaluation
of Methods for Estimating Driver Response Times
Development
and Evaluation of Driver Response Time Predictors Based upon Meta Analysis.
The
Influences of Age-Related Development upon Children Pedestrian & Bicyclists
Collisions
Effects
of Retroreflective Material Upon Pedestrian Identification at Night
Factors that
Influence Drivers’ Response Choice Decisions in Video Recorded Crashes
Relationship between Relative Velocity Detection and Driver Response Times in
Vehicle Following Situations
Quantifying Driver Response Times Based upon Data
from Research and Real Life
http://ppc.uiowa.edu/driving-assessment/2005/final/papers/03_Muttartformat.pdf
Driving Simulator
Evaluation of Driver Performance during Hands-Free Cell Phone Operation in a
Work Zone: Driving without a Clue.
http://wzsafety.tamu.edu/docs/trb_papers07/07-2873.pdf
Research Conducted in Conjunction with the
Maryland Association of Traffic Accident Investigators [MdATAI]
www.crashconference.com/mdatairesearch.pdf
Estimating Driver Response
Times, Chapter 14 of the Handbook of Human factors in Litigation edited by Noy &
Karkowski in 2005. Published by Taylor & Francis Publishing. To
order contact CRC Press, LLC, PO Box 409267, Atlanta, GA 30384-9267. Or
call 800-272-7737.
| Accession No |
01033517 |
| Publication Date |
20050000 |
| Abstract |
The objective of this chapter is to address driver response time and
the factors that influence it. Previous research has reported driver
response times from 0.5 to 10 seconds for various tasks. The chapter
offers reasons for different response time results. The author found in
previous research that different results can be explained and quantified
by methodology and substantive variables. Therefore, a mean response
time for various tasks can be estimated. The precision of such estimates
are also addressed in the chapter. |
Driver
Response Choice: A Comparison of Actual Responses to Decision-Making Theory
http://www.aspaci.org.au
Video
recordings of real life traffic incidents and driver response choice research
was obtained and analyzed based upon the response choice of the drivers.
The data obtained from these drivers was compiled based upon lane
selection, direction of the hazard, response time and response choice.
The responses in these emergency situations were then compared with
psychological decision-making theories. It
was found that drivers responded very much as predicted by behaviorist theories.
Behavior
theories that were exhibited by drivers included, functional fixedness,
difference reduction, operant conditioning, means ends analysis and mental set
(insight, framing). Thus, drivers
used heuristic rather than algorithm methods of decision making.
Drivers are not likely going to select a “lesser” collision and are
more likely to steer away from hazards. Drivers
are not inclined to steer left when in the left lane or steer right when in the
right lane. Experienced drivers are
more likely to brake even if steering seems to be the most logical response.
Horn use occurred in 13.7% of the emergency driver response incidents and was
much more likely in sideswipe type response scenarios (18.1%) than in any other
crash configuration. Drivers in the
center lane are more likely to use the horn than drivers in outside lanes and
horn use was associated with a slight increase in response time.
The
results of this research were compared to previous published works. Applications
in forensic settings and as a baseline when evaluating the behavior of those
recovering from medical conditions related to that of “normal” was also
addressed. The limitations of these
findings were also discussed.
Top
DRIVE3:
A Simplified Method for Estimating Driver Response
http://www.aspaci.org.au
Driver Response in Various
Environments Estimated Empirically [DRIVE3]
is a computer program that allows the user to estimate a response time based
upon a number of environmental factors. The program is based upon how
drivers have responded in 145 research studies and 147 real life responses of
drivers faced with emergency response scenarios (nearly half of which involved a
crash). The DRIVE3 program was used to estimate the
response time of the 147 real life responders and was accurate within 35% more
than 70% of the time if utilizing both DRT estimation methods offered by the
program.
Top
Evaluation of
Methods for Estimating Driver Response Times. ITAI / AIRIL 2003 Conference
Proceedings, Stratford-upon-Avon, England. Copies can be obtained through
ITAI at
www.itai.org
An investigation was made of three methods for
estimating driver response times. These
methods include obtaining a similar study and making adjustments, using
predictive formulas that were developed from a meta-analysis of numerous driver
response research papers and using a rule-of-thumb estimating method. The strengths, limitations and precision of each method are
addressed and a methodology for estimating driver response times is suggested.
Top
Development and
Evaluation of Driver Response Time Predictors Based upon Meta Analysis. Report
#2003-01-0885, pp 1-21, Warrendale, PA:
Society of
Automotive Engineers, (2003).
To order:
http://www.sae.org/servlets/productDetail?PROD_TYP=PAPER&PROD_CD=2003-01-0885
The goal of this research was to develop
mathematical equations that would estimate the response times of drivers in
various situations. This research involved two studies.
The first study involved the development
of a series of equations that predict driver response times [DRTs]. Compiling a
database of over 130 studies that measured DRT and coding for over 20
methodology and substantive variables was the source from which the equations
were developed. Multiple Stepwise Linear Regression analysis was performed on
the database. The analysis produced an empirical equation that revealed which
variables and methods were statistically significant predictors of DRTs. The
analysis showed that when all research data was analyzed together an accurate
predictor could not be developed. However, when the database was divided into
smaller sets based upon where the target emerged, empirical equations were
developed. Each of six equations reached statistical significance.
The second study compared the prediction results of the equations from the first
study, to the results obtained from time/position analysis of the video record
of actual traffic incidents at an intersection in Kentucky. The meaning,
limitations and significance of the analysis results are discussed and compared
to previously published prediction models. The equations predicted response
times within 1/2 of a second 74 percent of the time, and within 3/4 of a second
90 percent of the time.
Top
The
Influences of Age-Related Development upon Children Pedestrian & Bicyclists
Collisions, Accident Reconstruction Journal, January 2000. ARJ phone:
301-843-1371
The extent to which motor,
cognitive, and neural development, as well as environmental factors influence
the abilities of children pedestrians were examined.
Age-related development and environmental influences were compared with
the type and number of children pedestrian collisions.
There is an apparent correlation between pedestrian age, accident
location, and living conditions.
Cognitive, motor and neural
development continue to improve into adolescence and is not at a level to safely
negotiate roads with traffic until after age 8 and continues to be difficult for
children up to age 14 years at intersections and other complex locations.
Simplified safety strategies that utilize environmental cues at the
actual crossing locations should be taught.
All pedestrians should be encouraged to wear safety clothing.
Road improvements are also discussed.
Top
Effects
of Retroreflective Material Upon Pedestrian Identification at Night, Accident
Reconstruction Journal, January 2000. ARJ phone 301-843-1371
This research explored which of 3 retroreflective colors and 1
non-reflective color (yellow) would be most quickly identified in a real traffic
situation at night. Subjects age 17
to 70 recognized the pedestrian with the fluorescent red-orange traffic vest
quickest (p = .039), when compared to white and fluorescent lime colored
retroreflective safety vests and a pedestrian with no vest.
Nearly 1/4 of the subjects were not able to detect the pedestrian without
a safety vest and subjects identified the vested pedestrians from nearly twice
the distance than those that did identify the "no-vest" pedestrian.
Research in support of a safety clothing standard is discussed.
Top
Factors that Influence Drivers’ Response Choice
Decisions in Video Recorded Crashes, Society of Automotive Engineers (Technical
paper 2005-01-0426)
www.sae.org
Video
recordings of real life traffic crashes and near crashes were analysed for
driver response choice. These responses were compared to problem solving
theories.
In emergency
situations drivers were likely to make relatively quick decisions. By
allocating limited time to the decision, an algorithmic approach (that considers
the probabilities of all options) is not possible in most cases. Instead a
driver will decide upon a response using an intuitive (heuristic) approach.
Intuitive decision-making is quicker and rule-of-thumb based but has predictable
limitations.
Drivers
exhibited functional fixedness in that they did not select a “lesser” collision
and nearly 40% of horn use was for chastising other drivers rather than for
avoidance. Drivers exhibited difference reduction in that they were more likely
to steer away from hazards. Also, drivers exhibited operant conditioning in
that as the complexity of the situation increased, the likelihood of braking as
a response increased as well. Therefore, this research shows that drivers’
decisions were governed by intuition and that drivers will not likely consider
all possible alternatives in the short time available in an emergency situation.
The results of
this research were compared to previous research. Applications in forensic
settings, for driver education, and as a baseline when evaluating driver choice
for Intelligent Transportation Systems purposes is addressed.
Top
Relationship between Relative Velocity Detection
and Driver Response Times in Vehicle Following Situations, Society of Automotive
Engineers (Techical Paper 2005-01-0427).
.
www.sae.org.
Co-Authors: William F. Messerschmidt, &
Larry G. Gillen
Several
previous studies report driver response times when responding to a lead
vehicle. There have also been other studies that examined and measured the
ability of drivers to detect the relative velocity of a lead vehicle. This
study attempts to determine how the relative velocity detection threshold and
driver response times fit together.
There may be a
significant difference between the times at which a lead vehicle is visible
versus when it is perceivable as an immediate hazard. This research involved
two parts; the first analyzes the raw data reported in previous research. The
second part involved measuring responses of subjects using a laptop simulator.
The goal of both parts of this research was to compare the subtended angular
velocity [SAV] with the response times of drivers to determine if there is a
point (threshold) at which response times level off at a fast rate.
As theorized,
driver response times remained high until the SAV of the lead vehicle increased
to over 0.006 radians per second (in Part I) and to 0.0066 r/s (Part II), after
which response times remained relatively fast. An in-depth analysis of driver
response to a lead vehicle is discussed.
| Title |
Driving Simulator Evaluation of Driver Performance During Hands-Free
Cell Phone Operation in a Work Zone: Driving Without a Clue |
| Accession No |
01045020 |
| Authors |
Muttart, Jeffrey W
 ;
Fisher, Donald L
;
Knodler Jr., Michael A
;
Pollatsek, Alexander
|
| Conference Title |
Transportation Research Board 86th Annual Meeting
|
Corp. Authors
/ Publisher |
Transportation Research Board
|
| Publication Date |
20070000 |
| Description |
14p; Figures(5); References(15); Tables(1) |
| Media Type |
CD-ROM |
| Languages |
English |
| Abstract |
Crashes continue to be a problem in work zones. Analyses have
indicated that rear-end and sideswipe crashes are the most frequent.
Investigators have hypothesized that distractions are often the cause of
both types of crashes. These distractions will only increase as more
drivers attend to other tasks, such as cell phone conversations. In
order to address this issue, virtual worlds that reflect various work
zone geometries were developed for an advanced driving simulator. These
worlds contained 32 virtual work zones, and 38 drivers navigated through
these worlds. On one portion of a trip, the drivers were asked to
respond to a series of short sentences, which mimicked a hands-free cell
phone conversation; on the other portion of the trip, no sentences were
read to the drivers. A lead vehicle ahead of the participant driver
braked occasionally in the work zone activity area. Braking scenarios
involved either the lead vehicle stopping after an advanced clue that
traffic ahead was going to stop or the lead vehicle stopping for no
apparent reason, most often after passing a roadside obstacle (potential
distracter). Drivers not engaged in a cell phone task were able to
reduce their speed earlier in response to a slowing lead vehicle than
were drivers engaged in the cell phone task. Drivers on the cell phone
were also more likely to brake hard and less likely to make a mirror
glance when changing lanes. The results strongly suggest that cell phone
use reduces driver awareness and will increase the two major types of
crashes in work zone activity areas, which are rear end and sideswipe
collisions. |
| TRT Terms |
Accident analysis
;
Cellular telephones
;
Deceleration
;
Distraction
;
Driving simulators
;
High risk drivers
;
Rear end collisions
;
Side collisions
;
Work zone safety
|
| Subject Areas |
I83 Accidents and the human factor;
H52 HUMAN FACTORS;
H51 SAFETY |
| Report Number |
07-2873 |
| Availability |
Transportation Research Board Business Office
|
| URLs |
|
| Document Source |
Source Data: Transportation Research Board Annual Meeting 2007 Paper
#07-2873
|
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