Re: NYPD Tickets 9,016 Drivers In Cellphone Crackdown

On Mar 15, 8:44 am, richardcas...@xxxxxxxxxxxxx (Richard Casady)
wrote:
On Sun, 15 Mar 2009 07:43:10 +0000, James Hogg <Jas.H...@xxxxxxxxxxxx>
wrote:

On Sat, 14 Mar 2009 18:03:36 -0000, "D. Spencer Hines"
<pant...@xxxxxxxxxxxxx> wrote:

NYPD CELL PHONE CRACKDOWN YIELDS THOUSANDS OF TICKETS

ASSOCIATED PRESS

Posted: 3:11 pm
March 13, 2009

NEW YORK - New York City police issued a whopping 9,016 tickets during a
24-hour crackdown on phoning-while-driving.

The Nanny State strikes again.

While one car accidents are common in the rural areas, it is hard to
have a wreck without damaging the property of another in NYC.
[well, Manhatten] So the city has more standing to *** in, than, say,
a rural sheriff in Montana. Some say that on average, talking on the
phone is as dangerous as being drunk. Drunks often have what's left of
their mind on driving. Talkers never do.

Casady


There are a couple of charts that are only vivible on the cite.


http://www.psych.utah.edu/AppliedCognitionLab/DrivingAssessment2003.pdf

FATAL DISTRACTION? A COMPARISON OF THE CELL-PHONE
DRIVER AND THE DRUNK DRIVER
David L. Strayer, Frank A. Drews, & Dennis J. Crouch
Department of Psychology
380 S. 1530 E. Rm 502
University of Utah
Salt Lake City, UT 84112, USA
E-mail: David.Strayer@xxxxxxxx
Summary: We used a high-fidelity driving simulator to compare the
performance of
cell-phone drivers with drivers who were legally intoxicated from
ethanol. When
drivers were conversing on either a hand-held or hands-free cell-
phone, their
reactions were sluggish and they attempted to compensate by driving
slower and
increasing the following distance from the vehicle immediately in
front of them. By
contrast, when drivers were legally intoxicated they exhibited a more
aggressive
driving style, following closer to the vehicle immediately in front of
them and
applying more force while braking. When controlling for driving
difficulty and time
on task, cell-phone drivers exhibited greater impairment than
intoxicated drivers.
It is estimated that over 100 million cellular subscribers in the
United States use their phone
while driving (Cellular Telecommunications Industry, 2003; Goodman et
al., 1999).

Because of
safety concerns associated with cell phone use while driving, several
legislative efforts have been
made to restrict cell phone use on the road (Hahn, Tetlock, & Burnett,
2000; Hahn & Dudley, in
press). In most cases, the legislation restricts the use of hand-held
phones but permits the use of
hands-free phones while driving. In fact, several researchers have
reported that driving is impaired
by concurrent cell phone use (Alm & Nilsson, 1995; Briem & Hedman,
1995; Brookhuis, De Vries,
& De Waard, 1991; McKnight & McKnight, 1993; Strayer & Johnston, 2001;
Strayer, Drews, &
Johnston, 2003); however, the precise impact of cell-phone driving on
traffic safety is unknown. In
their seminal article, Redelmeier and Tibshirani (1997) reported
epidemiological evidence
suggesting that “the relative risk [of being in a traffic accident
while using a cell-phone] is similar
to the hazard associated with driving with a blood alcohol level at
the legal limit” (p. 465). If this
finding can be substantiated in a controlled laboratory experiment,
then these data would be of
immense importance for public safety.
Here we report the result of a controlled study that directly compared
the performance of
drivers who were conversing on a cell-phone with the performance of
drivers who were legally
intoxicated with ethanol. We used a car-following paradigm in which
participants followed an
intermittently braking pace car while they were driving on a multi-
lane freeway. Three conditions
were studied: single-task driving (baseline condition), driving while
conversing on a cell-phone
(cell-phone condition), and driving with a blood alcohol concentration
of 0.08 wt/vol. (alcohol
condition). The driving tasks were performed on a high-fidelity
driving simulator.
Method
Participants. Forty-one adults (26 male and 15 female) participated in
the IRB approved
study. Participants ranged in age from 22 to 45, with an average age
of 25.7. All had normal or
corrected-to-normal vision and a valid driver’s license.
Stimuli and Apparatus. A PatrolSim high-fidelity driving simulator,
manufactured by GE ISim
was used in the study. A freeway road database simulated a 24-mile
multi-lane beltway with on
and off-ramps, overpasses, and two and three-lane traffic in each
direction. A pace car, programmed
to travel in the right-hand lane, braked intermittently throughout the
scenario. Distractor vehicles
were programmed to drive between 5% and 10% faster than the pace car
in the left lane, providing
the impression of a steady flow of traffic. Unique driving scenarios,
counterbalanced across
participants, were used for each condition in the study. Measures of
real-time driving performance,
including driving speed, distance from other vehicles, and brake
inputs, were sampled at 30 Hz and
stored for later analysis. Cellular service was provided by Sprint
PCS. The cell-phone was
manufactured by LG Electronics Inc. (model TP1100). For hands-free
conditions, a Plantronics
M135 headset (with ear piece and boom microphone) was attached to the
cell-phone. Blood alcohol
concentration levels were measured using an Intoxilyzer 5000,
manufactured by CMI Inc.
Procedure. The experiment was conducted in three sessions on different
days. The first
session familiarized participants with the driving simulator using a
standardized adaptation
sequence. The order of subsequent alcohol and cell-phone sessions was
counterbalanced across
participants. In these latter sessions, the participant’s task was to
follow the intermittently braking
pace car driving in the right-hand lane of the highway. When the
participant stepped on the brake
pedal in response to the braking pace car, the pace car released its
brake and accelerated to normal
highway speed. If the participant failed to depress the brake, they
would eventually collide with the
pace car. That is, like real highway stop and go traffic, the
participant was required to react in a
timely and appropriate manner to a vehicle slowing in front of them.
In the alcohol session, participants drank a mixture of orange juice
and vodka (40% alcohol
by volume) calculated to achieve a blood alcohol concentration of 0.08
wt/vol. Blood alcohol
concentrations were verified using infrared spectrometry breath
analysis. Participants then drove in
the car-following scenario while legally intoxicated.
In the cell-phone session, three counterbalanced conditions were
included: single-task
baseline driving, driving while conversing on a hand-held cell phone,
and driving while conversing
on a hands-free cell phone. In both cell-phone conditions, the
participant and a research assistant
engaged in naturalistic conversations on topics that were identified
on the first day as being of
interest to the participant. To minimize interference from manual
components of cell phone use, the
call was initiated before participants began driving.
Results and Discussion
In order to better understand the differences between conditions,
driving profiles were
created by extracting 10 second epochs of driving performance that
were time-locked to the onset of
the pace car’s brake lights. Each time that the pace car’s brake
lights were illuminated, the data for
the ensuing 10 seconds were extracted and entered into a 32 X 300 data
matrix (i.e., on the jth
occasion that the pace car brake lights were illuminated, data from
the 1st 2nd, 3rd, …, and 300th
observations following the onset of the pace car’s brake lights were
entered into the matrix
X[j,1],X[j,2],X[j,3]… X[j,300]; where j ranges from 1 to 32 reflecting
the 32 occasions in which the
participant reacted to the braking pace car). Each driving profile was
created by averaging across j
for each of the time points. We created profiles of the participant’s
braking response, driving speed,
and following distance.
Figure 3: Distance Profile
Time (sec)
0 1 2 3 4 5 6 7 8 9 10
Distance (Meters) 25
26
27
28
29
30
Alcohol
Baseline
Cell Phone
Figure 2: Speed Profile
Speed (MPH)
51
52
53
54
55
56
57
Alcohol
Baseline
Cell Phone
Figure 1 presents the braking profiles. In the
baseline condition, participants began braking within
1 second of pace car deceleration. Similar braking
profiles were obtained for both the cell phone and
alcohol conditions. However, compared to baseline,
when participants were legally intoxicated they tended
to brake with greater force, whereas participant’s
reactions were slower when they were conversing on a
cell phone.1
Figure 2 presents the driving speed profiles. In
the baseline condition, participants began decelerating
within 1 second of the onset of the pace car’s brake
lights; reaching minimum speed 2 seconds after the
pace car began to decelerate, whereupon participants
began a gradual return to pre-braking driving speed.
When participants were legally intoxicated, they
drove slower, but the shape of the speed profile did
not differ from baseline. By contrast, when
participants were conversing on a cell phone it took
them longer to recover their speed following braking.
Figure 3 presents the following distance
profiles. In the baseline condition, participants
followed approximately 28.5 meters behind the pace
car and as the pace car decelerated, the following
distance decreased, reaching nadir approximately 2
seconds after the onset of the pace car’s brake lights.
When participants were legally intoxicated, they
followed closer to the pace car, whereas participants
increased their following distance when they were conversing on a cell
phone.
Table 1 presents the six performance variables that were measured to
determine how
participants reacted to the vehicle braking in front of them. Brake-
onset time is the time interval
between the onset of the pace car’s brake lights and the onset of the
participant’s braking response
(expressed in milliseconds). Braking force is the maximum force that
the participant applied to the
brake pedal in response to the braking pace car (expressed as a
percentage of maximum). Speed is
the average driving speed of the participant’s vehicle (expressed in
miles per hour). Following
distance is the distance between the pace car and the participant’s
car (expressed in meters). Halfrecovery
time is the time for participants to recover 50% of the speed that was
lost during braking
(expressed in seconds). Also shown in the table are the total number
of collisions in each phase of
the study. We used a Multivariate Analysis of Variance (MANOVA)
followed by planned contrasts
to provide an overall assessment of driver performance in each of the
experimental conditions.
1 The data from hand-held and hands-free cell phone conditions were
combined because preliminary
analyses revealed no significant differences between these two modes
of cellular communication (see below for
details).
Figure 1: Braking Profile
Brake Depression %
0 2 4 6 8
10
12
14
16
18
Alcohol
Baseline
Cell Phone
Alcohol Baseline Cell Phone
Total Accidents 0 0 3
Brake Onset Time (msec) 888 (51) 943 (58) 1022 (61)
Braking Force (% of maximum) 69.6 (3.6) 56.4 (2.5) 55.2 (2.9)
Speed (MPH) 52.8 (.08) 54.9 (.08) 53.2 (.07)
Following Distance (meters) 26.5 (1.7) 27.3 (1.3) 28.5 (1.6)
½ Recovery Time (sec) 5.4 (0.3) 5.4 (0.3) 6.2 (0.4)
Table 1. Means and standard errors (in parentheses) for the Alcohol,
Baseline, and Cell-Phone conditions.
We performed an initial comparison of driving while using a hand-held
versus hands-free
cell-phone. Both hand-held and hands-free cell-phone conversations
impaired driving. However,
there were no significant differences in the impairments caused by
these two modes of cellular
communication (F(5,36)=1.33, p>.27). Therefore, we collapsed across
the hand-held and hands-free
conditions for all subsequent analyses reported in this article. The
observed similarity between
hand-held and hands-free cell-phone conversations is consistent with
earlier work (Strayer &
Johnston, 2001; Strayer, Drews, & Johnston, 2003) and suggests that
the impairments to driving are
mediated by a withdrawal of attention from the processing of
information in the driving environment
necessary for safe operation of a motor vehicle
MANOVAs indicated that both cell-phone and alcohol conditions differed
significantly from
baseline (F(5,36)=3.44, p<.01 and F(5,36)=3.90, p<.01, respectively).
When drivers were
conversing on a cell-phone, they were involved in more rear-end
collisions and their initial reaction
to vehicles braking in front of them was slowed by 8.4%, relative to
baseline. In addition, compared
to baseline it took participants who were talking on the cell phone
14.8% longer to recover the speed
that was lost during braking. Drivers using a cell phone attempted to
compensate for their increased
reaction time by driving 3.1% slower than baseline and increasing
their following distance by 4.4%.
By contrast, when participants were legally intoxicated, neither
accident rates, nor reaction
time to vehicles braking in front of the participant, nor recovery of
lost speed following braking
differed significantly from baseline. Overall, drivers in the alcohol
condition exhibited a more
aggressive driving style. They followed 3.0% closer to the pace
vehicle and braked with 23.4%
more force than in baseline conditions. Most importantly, our study
found that accident rates in the
alcohol condition did not differ from baseline; however, the increase
in hard braking that we
observed is likely to be predictive of increased accident rates in the
long run (e.g., Lee et al., 2002).
The MANOVA also indicated that the cell-phone and alcohol conditions
differed
significantly from each other, F(5,36)=4.66, p<.01. When drivers were
conversing on a cell-phone,
they were involved in more rear-end collisions, had a 7.5% greater
following distance, and took
14.8% longer to recover the speed that they had lost during braking
than when they were legally
intoxicated. Drivers in the alcohol condition also applied 26.1%
greater braking pressure than
drivers in the cell-phone condition.
Taken together, we found that both intoxicated drivers and cell-phone
drivers performed
differently from baseline, and that the driving profiles of these two
conditions differed. Drivers in
the cell-phone condition exhibited a sluggish behavior (i.e., slower
reactions) which they attempted
to compensate for by increasing their following distance. Drivers in
the alcohol condition exhibited a
more aggressive driving style, in which they followed closer,
necessitating braking with greater
force. With respect to traffic safety, our data are consistent with
Redelmeier and Tibshirani’s (1997)
earlier estimates. In fact, when controlling for driving difficulty
and time on task, cell-phone drivers
may actually exhibit greater impairments (i.e., more accidents and
less responsive driving behavior)
than legally intoxicated drivers. These data also call into question
driving regulations that prohibit
hand-held cell-phones and permit hands-free cell-phones, because no
significant differences were
found in the impairments to driving caused by these two modes of
cellular communication.
ACKNOWLEDGMENTS
Support for this study was provided through a grant from the Federal
Aviation
Administration (# DTFA0202P09602). We wish to thank the Utah Highway
Patrol for providing the
breath analyzer and GE I-SIM for providing access to the driving
simulator. Danica Nelson, Amy
Alleman, and Joel Cooper assisted in the data collection.
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