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(logo whooshing)
(soft ethereal music)

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- [Narrator] Many rear-end crashes occur

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when traffic is backed up on a roadway.

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End-of-queue warning
systems are currently used

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to mitigate the issue and rely on sensors

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and digital signs to alert drivers,

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but these systems are less accurate

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when sensor coverage is
limited or not available.

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The Texas Department of Transportation

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and researchers with the Texas
A&M Transportation Institute

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have explored using
additional data sources

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to enhance queue detection systems

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and improve their accuracy.

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- Most of these systems have
been using existing sensors

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which are already deployed on the freeway,

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but there are many locations

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where these sensors are not available

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or they are not densely deployed enough

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to be able to detect
and monitor the changes

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in these queuing situations.

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This led to this project
that we were trying

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to find alternate data sources,
including third-party data,

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for example, be used
here in data from INRIX,

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and also the potential use
of connected vehicle data.

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- A lot of times, you don't know

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where the queues will be happening

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because of the traffic incidents,

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because of the other things,

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so that's why we got this project

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that would allow us to
use multiple data sources.

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- The research was done both
in the lab and in the field.

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First, we started with reviewing
the state of the practice

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in terms of queue warning systems,

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and also, we looked at recent deployments,

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and we also looked at,

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what are the available new data
sources which we could use?

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And then after this, we
developed a detailed design

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of the proposed queue warning system,

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which could combine multiple data sources.

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We implemented it in a Python environment,

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and then we conducted
simulation-based evaluations.

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- We developed a prototype

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and deployed the prototype
queue warning systems

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in three corridors.

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There were two in Houston district,

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Interstate 610 and Interstate 69,

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and there was another one
in Bryan College Station

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on State Highway 6, so
these deployments allowed us

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to observe the queue
warning system performance.

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- By combining multiple data sources,

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we were able to improve the
accuracy of the queue detection

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and also reduce the latency so
we could detect the beginning

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of the formation of a queue quicker

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so we could provide more up-to-date

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and more accurate queue warning
information to the drivers,

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which improves their safety,

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so it reduces the potential
of rear-end crashes.

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- I believe the queue
warning system is all set

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for full-scale deployment.

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As a matter of fact, there's
already a system up and running

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that uses only third-party
data along a 50-mile stretch

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of I-20 in the Odessa district.

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- [Narrator] For more information

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and to find the publications
for this project,

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please visit the TxDOT Research Library

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at the link shown below.