Lesson

Collect traffic and travel time data from all traffic lanes because travel times can vary substantially between lanes.

The Georgia Department of Transportation studied three different travel speed monitoring technologies in work zones.


02/01/2016


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Lesson Learned

Collect traffic and travel time data from all traffic lanes because travel times can vary substantially between lanes. Travel time differentials exist across freeway work zone travel lanes, which means placement of data collection technologies may bias collected speeds towards the specific lanes that are monitored or from which more data are collected due to the nature of the technology. The speed differential across lanes can introduce significant bias into monitored travel time results. It is important to set up equipment to capture representative traffic conditions when multiple lanes are involved and the speeds vary across lanes.

Specific examples of how this impacted the results include:
  • ALPR travel times were longer because the system detected more vehicles in the slower (outside) lanes, which obstructed the view of vehicles in the inside lanes. The ALPR is biased toward capturing travel times of vehicles in the lane closest to the camera location. The iCone has a similar limitation because it has a low profile.
  • Video data collected from overpasses were biased toward the faster moving lanes because large vehicles and trucks obstructed many license plates in the slower lanes.
  • The iCone takes spot speed measurements and assumes the measurements are representative of conditions between those points, so long distances between devices could yield unreliable estimates.
  • Slower-moving vehicles are more likely to be detected by Bluetooth as they are in the field of detection (300 ft radius from the sensor) for longer than fast moving vehicles. Though Bluetooth strategies a less impacted by placement because they are not visually gathering data.


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Source

Work Zone Technology Testbed

Author: Suh, Wonho, et al.

Published By: Georgia Department of Transportation, Office of Research

Source Date: 02/01/2016

URL: http://g92018.eos-intl.net/eLibSQL14_G92018_Documents/11-15.pdf

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Janet Fraser
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janet.fraser@noblis.org


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Lesson ID: 2017-00781