(logo whooshing) (soft ethereal music)
- [Narrator] Many rear-end crashes occur
when traffic is backed up on a roadway.
End-of-queue warning systems are currently used
to mitigate the issue and rely on sensors
and digital signs to alert drivers,
but these systems are less accurate
when sensor coverage is limited or not available.
The Texas Department of Transportation
and researchers with the Texas A&M Transportation Institute
have explored using additional data sources
to enhance queue detection systems
and improve their accuracy.
- Most of these systems have been using existing sensors
which are already deployed on the freeway,
but there are many locations
where these sensors are not available
or they are not densely deployed enough
to be able to detect and monitor the changes
in these queuing situations.
This led to this project that we were trying
to find alternate data sources, including third-party data,
for example, be used here in data from INRIX,
and also the potential use of connected vehicle data.
- A lot of times, you don't know
where the queues will be happening
because of the traffic incidents,
because of the other things,
so that's why we got this project
that would allow us to use multiple data sources.
- The research was done both in the lab and in the field.
First, we started with reviewing the state of the practice
in terms of queue warning systems,
and also, we looked at recent deployments,
and we also looked at,
what are the available new data sources which we could use?
And then after this, we developed a detailed design
of the proposed queue warning system,
which could combine multiple data sources.
We implemented it in a Python environment,
and then we conducted simulation-based evaluations.
- We developed a prototype
and deployed the prototype queue warning systems
in three corridors.
There were two in Houston district,
Interstate 610 and Interstate 69,
and there was another one in Bryan College Station
on State Highway 6, so these deployments allowed us
to observe the queue warning system performance.
- By combining multiple data sources,
we were able to improve the accuracy of the queue detection
and also reduce the latency so we could detect the beginning
of the formation of a queue quicker
so we could provide more up-to-date
and more accurate queue warning information to the drivers,
which improves their safety,
so it reduces the potential of rear-end crashes.
- I believe the queue warning system is all set
for full-scale deployment.
As a matter of fact, there's already a system up and running
that uses only third-party data along a 50-mile stretch
of I-20 in the Odessa district.
- [Narrator] For more information
and to find the publications for this project,
please visit the TxDOT Research Library
at the link shown below.