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- [Narrator] Artificial intelligence has shown positives
for organizations in breaking down
large sums of information.
The Center for Transportation Research is investigating
the practical advantages of integrating AI
into operations from the Texas Department of Transportation,
examining potential benefits in this new
and vast data landscape.
- So this was a four-year long project.
So during the first two years,
it was a broad exploration project,
trying to help TxDOT understand what the landscape was
in terms of artificial intelligence and transportation.
So this was five years ago when there were a lot
of emerging data sources, vendors based on machine learning,
artificial intelligence, and we were trying to get
the lay of the land.
The second two years, based on what we found
during the first two years, we realized
that there were some low-hanging fruits for things
that we could do with data that TxDOT was already acquiring,
and that could derive more value from that data.
- They found that there is a great potential
for AI to help our transportation system operators
and owners to address our goals.
For example, in our cases include safety,
alleviate the congestion and include the air quality.
We got tons of data, and the AI technology definitely
would help us to make more informed, data-driven decisions.
- For the first period, the first two years,
we did small prototypes.
So we have some insights concerning the use
of machine learning to identify safety hotspots,
to understand travel patterns,
to do signal timing plans in real time.
And we actually prototype this
short term travel time estimation.
And so for the models that we used over the last two years,
we found that actually machine learning
can help us estimate travel times
up to 40% more accurately
than what you would normally do.
So in these days,
we typically use the current travel time as an estimate
of the travel time that you're going to experience.
And that could be up to 40% wrong in some corridors
and at some time of the day,
and you can do it better using machine learning.
- Some of the models tested in the project show promise
in improving TxDOT's ability to help drivers identify
the shortest route among competing alternatives.
They model, we tested
and gave us some pretty reliable results.
So that would help the drivers make more informed decisions.
And I think that's benefits
that this project will bring to us.
- So if this moves to implementation,
potentially we could be providing more accurate estimates
of travel times in such a way
that drivers may save a few minutes when crossing Austin.
And at the same time, by switching to an alternative route,
all old congestion may get relieved.
So everyone may experience travel time savings.
And also from a traffic management perspective,
maybe having more accurate travel time information
will allow decision makers to maybe,
when they have the ability to adjust timing plans,
if they foresee a long delay on I-35,
that may divert more traffic to the fronts roads
that could improve management in general.
- We can utilize this big amount of data
to help us make more informed decisions.
That's the direction we're going.
One of the important tools
that will help us decipher was including the data,
is the artificial intelligence models.
So that way, we can come up with more reasonable,
accurate estimate of travel times
which would provide a better experience
to our traveling public.
- [Narrator] For more information
and to find the publication for this project,
please visit the TxDOT Research Library
at the link shown below.