Simulator study in Minnesota found drivers heightened their visual attention in work zones with automated speed enforcement with dynamic "your speed" sign speed enforcement.
Evidence suggests drivers were less likely to engage in secondary tasks under these conditions and older drivers more compliant with speed limits.

Examining the Impact of ASE (Automated Speed Enforcement) in Work Zones on Driver Attention

Summary Information

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.


The significant differences in driving performance data primarily existed between age groups, with middle-aged drivers exhibiting optimal driving behaviors, and less between enforcement types.

  • Younger and older drivers were more likely to speed in all enforcement conditions compared to middle-aged drivers. Yet, the difference only reached statistical significance between the middle-age and older driver groups. Older drivers appeared to respond to speed limits best in the presence of ASE+DSDS.
  • The downstream zones provide a good metric for determining the efficacy of speed enforcement in terms of speed coherence. The findings of the secondary task engagement eye tracking analysis in the second downstream zone revealed that ASE+DSDS was the condition that had the fewest secondary task fixations, differing significantly from the number of fixations in the ASE only condition.
  • There was less engagement in the secondary task around enforcement.
  • Fixation data found drivers in the ASE only condition had the fewest speedometer glances. However, the fixation on speedometer data was not significant overall, indicating all enforcement conditions pose little risk for excessive distraction due to speedometer fixation.
  • Drivers fixated on the secondary task display less frequently in the ASE+DSDS condition compared to other enforcement types while traveling in the downstream areas.

While the results do not strongly support the hypothesis that ASE without DSDS improves driver attention in work zones, there is evidence drivers heightened their visual attention in work zones with ASE+DSDS enforcement.

Goal Areas
Deployment Locations