Author
Morris, Nichole L.; Jennifer L. Coope; Alice Ton; Nichole L.Peter Easterlund; John and Paul Plummer
Benefit Summary HTML

This study examined how implementing automated speed enforcement (ASE) may influence driver attention and behavior in work zones by replicating a work zone in a driving simulator and having volunteers drive through it under different speed enforcement conditions: no enforcement (control), police car present, ASE, and ASE with dynamic “your speed” signs (ASE+DSDS).

Participants were screened to ensure they had no cognitive or physical constraints that could limit their performance, had at least two years of driving experience, and drove at least 4,000 miles each year. Participants were categorized by age:

Driving Study Participants

Ages N (Males/Females)
Younger Drivers 18-30 20 (10 M/10 F)
Middle-Aged Drivers 41-53 20 (10 M/10 F)
Older Drivers 63-77 20 (11 M/9 F)


The simulated work zone was a 9.2 mile long segment of U.S. Route 169 between Jordan and Belle Plaine, Minnesota and composed of: a 2 mile introductory area, 1.2 mile transition area, 5 mile activity area, and 1 mile conclusion area. The activity section was divided into four subsections: upstream (of enforcement), enforcement, downstream 1, and downstream 2.

The primary task during the experiment was to follow a lead vehicle at a close, but safe, distance along the route while adhering to the 55 m/hr work zone speed limit. The lead vehicle drove at a constant speed of 55 m/hr initally, then changed speed using a sine function (mean of 55 m/hr, min of 40 m/hr, max of 70 m/hr). Participants were told some of their incentive would be deducted if they exceeded the speed limit. This was done to motivate participants to avoid speeding, though no actual deduction was taken. Participants performed the experimental drives wearing eye-tracking glasses to monitor gaze and fixation.

The secondary task was used to give participants another activity to pursue while driving. It was comprised of a matrix of arrows around a central “target” arrow. Pressing the target arrow started the task: each arrow around the target arrow rotated for up to 1.5 seconds and then the participant had to press the keypad button corresponding to the number of peripheral arrows matching the direction of the target arrow. Participants could choose how many of the tasks they wished to complete.

All four experimental drives were performed consecutively in a randomized order. Prior to starting, participants were reminded they would complete four drives, told that they would have two surveys administered between each drive, and reminded of the payment scheme for the speed infractions.

Analyses of driving performance, distraction and attention measures were carried out within 3x4 mixed model ANOVA with age group (young, middle-aged, and older) as a between-subjects measure and speed enforcement type (control, ASE, ASE+DSDS, and police presence) as a within-subjects measure. Driving performance and eye tracking results were analyzed and are described by work zone segment: transition, upstream, enforcement, and downstream zones.

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This study examined how implementing automated speed enforcement (ASE) may influence driver attention and behavior in work zones by replicating a work zone in a driving simulator and having volunteers drive through it under different speed enforcement conditions: no enforcement (control), police car present, ASE, and ASE with dynamic “your speed” signs (ASE+DSDS).

The simulated work zone was a 9.2 mile long segment of U.S. Route 169 between Jordan and Belle Plaine, Minnesota and composed of: a 2 mile introductory area, 1.2 mile transition area, 5 mile activity area, and 1 mile conclusion area. The activity section was divided into four subsections: upstream (of enforcement), enforcement, downstream 1, and downstream 2.

The primary task during the experiment was to follow a lead vehicle at a close, but safe, distance along the route while adhering to the 55 m/hr work zone speed limit. The lead vehicle drove at a constant speed of 55 m/hr initally, then changed speed using a sine function (mean of 55 m/hr, min of 40 m/hr, max of 70 m/hr). Participants were told some of their incentive would be deducted if they exceeded the speed limit. This was done to motivate participants to avoid speeding, though no actual deduction was taken. All four experimental drives were performed consecutively in a randomized order.

A secondary task was used to give participants another activity to do while driving. The participant would press a target arrow, causing peripheral arrows to rotate for about 1.5 seconds. When they stopped, the participant had to identify how many peripheral arrows matched the target. Participants could choose how many secondary tasks to complete.

Analyses of driving performance, distraction and attention measures using a 3x4 mixed model ANOVA with age group (young, middle-aged, and older) as a between-subjects measure and speed enforcement type (control, ASE, ASE+DSDS, and police presence) as a within-subjects measure. Driving performance and eye tracking results were analyzed and are described by work zone segment: transition, upstream, enforcement, and downstream zones.

Other Reference Number
MN/RC 2016-06
Pages
101
Priority Research Area
Publication Sort Date
Publisher
Minnesota Department of Transportation
Result Type
Source ID
1552
Title
Examining the Impact of ASE (Automated Speed Enforcement) in Work Zones on Driver Attention
UNID
5056395EF91EF40785257F7E006B408B
Source Review
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