July 18, 2023 • 24 mins
Disasters – whether natural or man-made – can cripple transportation systems. Sophisticated modeling can go a long way in minimizing disruptions and restoring routine conditions.
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Episode Transcript
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Bernie Fette (host) (00:14):
Hello and welcome to Thinking
Transportation. Conversationsabout how we get ourselves and
the things we need from oneplace to another. I'm Bernie
Fette with the Texas A&MTransportation Institute.
Extreme events take manydifferent forms. With weather
calamities -- hurricanes andsuch -- we have the benefit of
(00:37):
advanced warning throughaccurate forecasting. Other
catastrophic occurrences likeearthquakes or terrorist
attacks happen suddenly withlittle or no warning at all.
What all such events share incommon though, is that their
impacts can be long-lasting andfar-reaching. In this episode,
(00:58):
we're visiting with someone whoworks to model those events to
better understand their impactson transportation networks and
help organizations developcontingency plans for how to
recover and resume normaloperations. Jeff Shelton is a
research scientist at TTI.
Welcome to ThinkingTransportation, Jeff. It's
(01:20):
great that you could share someof your time with us.
Jeff Shelton (guest) (01:22):
Thank you, Bernie . It's a pleasure
to be here.
Bernie Fette (01:25):
I was hoping we could start maybe with some
definitions. How would youexplain your work? Just for
someone who doesn't have a clueabout what traffic modeling is.
Someone at your high schoolreunion, for instance.
Jeff Shelton (01:41):
So we all encounter traffic every day
when we drive, right? Right.
And sometimes we experiencecongestion. Sometimes we
experience work zones.
Sometimes we experienceaccidents. Modeling is trying
to take existing conditions ofthe roadway network of traffic
and put it into a computersimulation. Now, if you wanna
(02:06):
do a prediction of something,then you can change the model
to predict what's gonna happen.
For instance, if there is goingto be an expansion of the
freeway and you're gonna have awork zone for six months, how
is that gonna impact traffic?
You know, if there's anaccident on the freeway and
you're delayed 20 minutes,you'll probably still take the
(02:27):
freeway tomorrow. But ifthere's a work zone for six
months, you may take adifferent route. So simulation
modeling is trying to predictthose changes in traffic
patterns on what people mightdo, given certain conditions
that happen on the roadway.
What I do is I build a computermodel and it could be something
as simple as an intersection,or it could be something as
(02:49):
large as the city of Houston.
You know, I've built severalcities around Texas, all the
major cities I've built, andthose are a little more
cumbersome, tedious, timeconsuming to build. But you get
a bigger picture of how trafficis running in your city. What I
try to do is just recreateexisting conditions and then
(03:11):
change the model to predictsome sort of future event .
Whether it's a work zone, anaccident, an improvement in the
roadway. Are you gonna charge atoll that you didn't charge
before? So I'm kind of tryingto make predictions of traffic.
Bernie Fette (03:24):
I can't help but ask you, does anybody ever ask
you if you work with a crystalball ?
Jeff Shelton (03:30):
My wife tells people I play video games for a
living.
Bernie Fette (03:35):
Okay .
Jeff Shelton (03:35):
I tell her it's a little more elaborate than
that. It's no, it's no crystalball. It's, it's just a lot of
school, a lot of knowledge, alot of experience. Knowing how
traffic behaves. And then youbuild the models and you put, I
like to call 'em "what if"scenarios. What if okay , the
freeway is expanded, or if it'sclosed down for a certain time,
(03:57):
what happens to traffic?
Bernie Fette (03:59):
Okay. Another way I was thinking of asking about
this that might help peopleunderstand modeling. Are there
any modeling parallels in otherindustries, you know, other
than transportation where thereare people like you whose job
it is to predict outcomes orimpacts, maybe financial
industry, real estate, thingslike that?
Jeff Shelton (04:21):
Yeah, I mean, first thing that comes to mind
to me is the weatherman. Youknow, meteorologists, they
predict the weather based oncertain data that they get. You
know, so they look at existingconditions. They have computer
models that kind of give 'emall this data and then they
make a forecast for the nextday or for the next week. So
they can kind of tell you is itgonna rain or what's the
(04:43):
percentage of chance of rain?
Um, what the temperature'sgonna be like. Is it gonna be
sunny? Is it gonna be cloudy?
Right. You know , so that'skind of a parallel. They're
kind of making a prediction ofwhat the weather's gonna be
like. And I'm trying to make aprediction of what traffic's
gonna be like. Another parallelthat came to my mind is my wife
works in a restaurant, she's arestaurant manager. And so she
needs to predict how manyguests are gonna come to the
(05:06):
restaurant that day or thatweek. And with that she can
predict how much food toprepare, how many people to
have on staff. So she's takingall this data to make a
prediction about the future.
Bernie Fette (05:17):
Okay. In your work, you make a lot of
references to what you callextreme events. What exactly
constitutes one of those? Maybeyou could give us some
examples.
Jeff Shelton (05:29):
There could be a lot of different kinds of
extreme events, Bernie. Youknow, in Minneapolis,
Minnesota, the bridge collapsedthere.
Bernie Fette (05:37):
And that was the I-35 bridge over uh ,
Jeff Shelton (05:39):
I-35 over the Mississippi River. Yeah, it was
under construction. They hadadded weight on the bridge. It
was old and it collapsed. Weconsider that an extreme event
because how is traffic gonnaoperate now that that roadway,
I mean, because that served,you know, a lot of traffic. A
lot of cars cross that bridgeevery day and now that bridge
(06:01):
isn't there. So how doestraffic gonna react? Where is
it gonna go? You know, extremeevents could be in all kinds of
forms. It could be a tornado,it could be an earthquake,
flood , forest fires. Arefinery explosion.
Bernie Fette (06:16):
Yeah. And could be a man-made event as well,
right? A terrorist attack .
Jeff Shelton (06:21):
Terrorist attack could also be considered an
extreme event.
Bernie Fette (06:26):
I'm curious, the example you gave about the I-35
bridge in Minneapolis, didsomebody model that disaster
before it happened? Or was thatan example of an event that
surely could have used a bit ofmodeling before it happened?
Jeff Shelton (06:41):
Well, all cities model their traffic. So they
have a model of that traffic.
Now, they did not predict whatwas gonna happen. They didn't
know that bridge was gonnacollapse, so they didn't have
that. So they modeled itafterwards. To see what the
impact of traffic would bewithout that bridge in place.
So, okay , where is trafficgonna reroute to basically, and
how bad is it gonna be at theseother routes?
Bernie Fette (07:02):
Yeah, that sounds like they really could have
benefited from some of the workthat you and your colleagues
do.
Jeff Shelton (07:07):
Yes. If they had done some sort of advanced,
what-if scenarios of theseevents happening, they could
have maybe had some morecontingency plans in place for
rerouting traffic.
Bernie Fette (07:19):
Not to overlook the fact that in the wake of
that disaster, the relevantoperating agencies surely did
move quickly to fix thesituation and to replace that
bridge.
Jeff Shelton (07:31):
Yeah, I think it was up and running in about a
year. I mean, because of theamount of traffic that goes
through that bridge, it'sheavily used. So they needed
that thing up and running soon.
So yeah , almost immediatelythey started the process of
reconstruction.
Bernie Fette (07:45):
Let's stay on what-if scenarios. You apply
that phrase to when you tackleone of those projects, how do
you go about it? I think youtold me when we spoke about
this before, that you startwith a computer model of a
particular city or a region.
Can you walk us through thesteps that you take? Maybe even
(08:07):
using a specific example, howdo you go about your work?
Jeff Shelton (08:11):
Okay , so I'll , I'll give you an example. We
had our MPO ask us about anextreme event on the border. U.
S.-Mexico border. I live in ElPaso, so Juarez, Mexico is
right across the street. And wehave four points of entry. One
of them is in Sants Teresa ,New Mexico, but that's right
down the street. So we considerthat part of our neighborhood.
We have four ports of entrythat have traffic going back
(08:33):
and forth throughout the day.
And of course, CBP has toinspect every vehicle that
comes in. So of course there'sa long line of cars waiting to
come in, so they're allcongested. So our MPO asked us,
well what happened if thebridge in the center, it's
called Bridge of the Americasor BTOA for short -- What if
there was an extreme event andwe lost that bridge? And you
(08:56):
know, they're interested in theeconomic impact, but before you
can do that, you have to do itfrom a traffic perspective. So
they wanted to know what wouldhappen short term and long term
. And so what I do is Iactually have to build a
computer simulation of El Pasoand Juarez together in the
computer. We have a planningmodel in El Paso that our MPO
(09:19):
builds and then in Mexico, inJuarez, their MPO also has a
planning model that they use.
So what we did is we kind ofmarried those two models
together to have one big model.
So traffic goes back and forthacross the bridge cuz the , the
models are basically truncatedat the border. Okay, okay , you
have the bridge, but you don'thave traffic on the other side
for both models. So what we didis we actually married 'em , so
(09:40):
they talked to each other. Sotraffic flows back and forth.
So once we built the model andwe calibrated it, when
calibration means your modelkind of simulating actual
traffic conditions and it looksrealistic, that's what
calibration means. So once wedo that, then we ran a scenario
of shutting down the BOTAbridge northbound, and we did
(10:01):
it for an extended period oftime and they wanted to know
what was the impact. So ofcourse, short term , like the
day that the incident happened,you know, traffic was just
chaotic. It was a gridlockaround the port of entry
because it was closed, peoplecouldn't get across. It was
basically just gridlock allaround the port. But after
about a month of being closed,people know it's closed. So
(10:22):
they start rerouting to otherbridges. And that's where it
becomes interesting becausepeople are looking for their
shortest route, time dependentroute to get to where they're
going. All people kind of dothat. You want to get there as
quickly as possible. So that'swhat the model's trying to do.
It's looking for a shortestpath to get across the bridge.
And since the border bridge isclosed, they have to reroute to
other bridges. And there we cansee very apparently whether or
not the bridges can handle theexcess traffic. And we saw very
(10:45):
clearly that it couldn't.
Bernie Fette (10:46):
Right. And in that case, most of the traffic
that is at the border crossingis trying to come into the
United States? Or is it apretty even split between the
two?
Jeff Shelton (10:56):
Uh, it's pretty even split going back and
forth. And I just heard a stattoday that 70 percent of the
vehicles crossing are Mexican.
They come into the U. S. towork and then they cross back
to go home. And then 30 percentare U.S., so.
Bernie Fette (11:12):
That's a good example. Did you have another
one that you might like toshare?
Jeff Shelton (11:16):
Yeah, we've been thinking about modeling
something near the coast. Texasroutinely gets hit by
hurricanes, you know, in theGulf. And those are very large
extreme events. Those are notinstantaneous, like an
earthquake that may shut downthe bridge cuz it collapsed .
This is more, you have a littlebit of advanced warning, but
hurricanes move. So where youtell people to evacuate from
(11:39):
and when you tell 'em toevacuate is, you know, it's not
an exact science, but we'retrying to use these models to
give as realistic as possible,how people would react given
this condition, this hurricane,especially people that live
closest to the coast need toevacuate. The question becomes,
do you evacuate them earlierthan people further inland?
(12:03):
Where do you evacuate 'em to?
Do you evacuate 'em inside thecity in someplace safe? Or do
you evacuate 'em out of thecity? Do you open up roadways?
I don't know if you've heard ofcontraflow lanes, Bernie.
Contraflow lanes has basicallyopened up the opposite
direction of traffic to thefreeway. So you have northbound
and southbound in Houston andI-45, you open up the
(12:26):
southbound and you turn itaround, you make everything
northbound. So you have doublethe capacity to go out just to
get everybody out of the cityas quickly as possible. Right.
So we can do that with modelingalso.
Bernie Fette (12:35):
Right. To run from the storm. Okay . Right.
Yeah, that's a particularlyinteresting example because you
had talked earlier about theparallel of weather
forecasting, so it sounds likeyou would be making your
predictions on top of anotherset of predictions. Correct.
Jeff Shelton (12:51):
.
Bernie Fette (12:52):
So just to throw an added challenge in there,
can you talk a little bit aboutthe operational impacts that
you've helped to predict? And Iguess specifically what I'm
asking about is what that meansin terms of people having to
change commute routes and alsowhat those impacts might mean
for shippers for people incommercial enterprises.
Jeff Shelton (13:15):
I live in El Paso and Interstate 10 runs right
through the heart of El Paso.
The Interstate goes coast tocoast, actually from California
to Florida, but it goes rightthrough the heart of El Paso.
And this section in El Paso isabout 60 years old. I mean, the
freeway, you know, everythinghas a shelf life, right? So
they are about to reconstructI-10 and they're gonna do it in
(13:39):
sections. And the first sectionthey wanna do is around the
downtown portion of El Paso andI-0, and they have three
different design alternatives.
They want to know which oneperforms the best. So we take
their designs and we put 'em inthe model and we run all three
(14:02):
scenarios to see how trafficreacts, where people are going.
If the roadways that theychange , are they reconfigured?
Can they handle the traffic nowthat it's rerouting? And also
some of the construction maynot be warranted. And let me
explain that a little further.
(14:22):
So they have the designdrawings, and at one
interchange they have thosethings, those Texas U-turns,
you know, where you just , youcan , yes . You don't have to
go through a light , just makea U-turn real quick. And so
when I'm running this model forEl Paso and I 10, I found
actually the one right by ouroffice right here. Nobody was
taking those U-turns, there wasvirtually no traffic. I mean,
(14:43):
zero on two of those scenarios.
And so when I'm gonna presentto the DOT Department of
Transportation, they need toknow that because why are you
gonna spend millions of dollarson these U-turns? If nobody's
gonna use it, wouldn't thatmoney be better spent somewhere
else?
Bernie Fette (15:00):
Right, right. Can you talk a little bit more
about what your work isintended to lead to? You talked
a little about contingencyplans. Can you talk a little
about what might be included inthose contingency plans?
Jeff Shelton (15:15):
I guess contingency plans for regional
stakeholders. So if there is anextreme event, you have to be
able to inform the public onwhich routes to take.
Bernie Fette (15:28):
Uh-huh,
Jeff Shelton (15:28):
You know, what's closed,
Bernie Fette (15:30):
Right.
Jeff Shelton (15:31):
So if we can use a simulation model to predict
what would happen if an extremeevent occurred, then we can
give that to, for instance, thecity of El Paso. They may be
able to retime some signaltimings that are not adequate
for this rerouting. You know,if there's an accident on the
freeway and people are alldiverting off of one ramp,
(15:52):
they're gonna have to gothrough this intersection. You
may have to re-time and addadditional green time on that
phase just to get all thattraffic exiting the ramp. You
know, so it's just, you know,helping them make decisions in
an orderly fashion just to helppeople get to their destination
quicker.
Bernie Fette (16:07):
It sounds like on top of a certain number of
uncertainties about things thatyou can't really control,
you've also got the fact thatwe're talking about people --
whose actions can not always beas predictable as you might
like. But I guess you in thatcase, kind of have to go on
patterns, kind of like youmentioned with those Texas
(16:28):
U-turns and how you noticedthat even though the option was
there, there were a lot ofpeople who weren't taking
advantage of that particularoption.
Jeff Shelton (16:35):
Yeah. One of the jokes that we have as modelers,
we say the hardest thing tomodel is human behavior.
Bernie Fette (16:41):
Yeah. I'm curious about any surprises that you
might have encountered in yourwork. Have you ever gone into a
modeling task, a predictivetask, expecting to see one
outcome and instead seeingsomething entirely different?
Jeff Shelton (16:58):
Yeah, we did.
This was actually a researchproject we did seven, eight
years ago, maybe a little bitlonger than that. And it had to
do with the new autonomousvehicles coming out now,
autonomous and connectedvehicles. So the cars talk to
each other, they inform eachother at the distance, their
speed. It's called cooperativeadaptive cruise control. Right
now cars have adaptive cruisecontrol, ACC and then you add
(17:21):
that C on their cooperative.
Adaptive cruise control isbasically the cars talking to
each other and not just, youknow, reacting to traffic
conditions. If it sees trafficahead, it slows down by itself.
No. This is actually carstalking to each other, telling
'em their speed, theirlocation, their direction,
everything. And so the questionwas, if we have this technology
in the future, will it helpalleviate congestion? And we
(17:42):
thought, yeah, it probablycould. Let's model it and find
out. We've modeled severaldifferent scenarios. Each
scenario, we increased thenumber of connected vehicles in
the model. And the moreconnected vehicles that we
simulated, it seemed liketraffic conditions got worse.
Okay . And we thought theywould get better. We thought
the speeds would improve. Thetravel times would shorten, and
(18:04):
it was the total opposite. Wesaw that the connected vehicles
messaging would send a messageback to another connected
vehicle behind it that in turnwould broadcast a message to a
vehicle behind that one. Andthen the message just
propagates back further andfurther. And the more vehicles
that are connected sending thismessages, the more vehicles
that are slowing down. So itended up slowing down traffic
(18:25):
more, and that was the totalopposite of what we thought
would happen.
Bernie Fette (18:28):
Right. It's a pretty sharp contrast. So
you've got this mix, this moshpit of different kinds of cars,
latest technology in manycases, but you know, 10, 15
year old cars at the same time.
Jeff Shelton (18:40):
Yeah. And so I heard one person say that,
well, we'll just make allvehicles automated connected in
the future. Well , I don'tthink that's ever gonna happen.
You know, they've been tryingto ban smoking for how long and
people still smoke, you know?
Yeah, yeah . If you drive aFerrari or a 1968 Shelby
Mustang, do you want it todrive itself or do you want to
drive it?
Bernie Fette (19:00):
Yeah.
Jeff Shelton (19:01):
Right. ,
when I'm talking about modeling
like huge cities like the cityof Houston, the one limiting
factor right now that I thinkwe're encountering is model
runtime. So to run Houston andyou know, you have to run it
over multiple iterations. Andwhat I mean by that, you
(19:22):
simulate it for a whole day andit looks at the travel patterns
of the vehicles, and then you ,you simulate it again and it's
like another day of learning.
You know, like if you move to anew city and you don't know
your route to work, you trydifferent routes until you find
the one that's best and getsyou the quickest. That's how
the model works. Mm-hmm.
. And so we'resimulating that and we do
Houston about 15 iterationsuntil we get what we call
(19:45):
convergences. But thatbasically means the model's
stabilized. Okay. And thattakes three days to run that.
Bernie Fette (19:52):
And that's where you get to start noticing some
of those patterns that youtalked about earlier.
Jeff Shelton (19:57):
RIght. And when I say three days burning , I'm
talking about three days on avery good computer. If you just
use an average computer, I usea a different computer. Three
days, took 12 days on thatcomputer.
Bernie Fette (20:08):
Okay. Well, I'm glad for what you shared there
because my next question had todo with what you hope to see in
terms of future advancements.
The technology has moved alongquite a bit to help you do your
work better, faster, et cetera. So then is it pure computer
muscle, computer capacity thatwhere you see the biggest needs
(20:31):
for improvement or, or thereother things that you think
would improve the modeling thatyou and your colleagues do?
Jeff Shelton (20:37):
Well, in addition to the improvement of the model
performance, but I think justthe algorithms that are built
into the models. You know, thealgorithm that predicts
shortest path from where you'recoming from to where you're
going. Okay. That's origin anddestination. That's what we
call the model. Okay. Andthere's algorithms to predict
that and they can always bebetter.
Bernie Fette (20:59):
So basically doing the work faster and doing
it with more precision is whatyou hope to move toward in the
future.
Jeff Shelton (21:06):
Correct.
Bernie Fette (21:06):
You know, for as long as I've known you, you've
always appeared to be reallyexcited about the work that you
do. So what exactly is it thatmotivates you to show up to
work every day ?
Jeff Shelton (21:19):
Well, I, I love modeling. Yeah. Like I said
before, my wife says I playvideo games for a living,
Bernie Fette (21:25):
.
Jeff Shelton (21:26):
Sometimes I do look at it that, I mean , um,
it's a lot more challengingthan playing a video game
because you're actuallypredicting real-life
conditions. But it is kind ofneat to see your predictions
come out to reality. And I'llgive you an example. So the
Texas Department ofTransportation asked us to do a
(21:48):
little study out on the eastside of El Paso where traffic
was spilling back from theintersection onto the freeway
in the afternoon. So I went outthere, I looked at it, I built
a model for it, and I gave themseveral alternative design
changes they could do to helpimprove traffic. And they
(22:08):
implemented those things. Soit's really cool to go out
there and see your predictionand your recommendations get
put to life in real-worldconditions.
Bernie Fette (22:17):
Yeah. Really great example of public
service, honestly. JeffShelton, research scientist at
TTI. Jeff, thanks very much forspending some time with us, and
thanks for your hard work andyour enthusiasm.
Jeff Shelton (22:31):
Thanks, Bernie. I really appreciate it.
Bernie Fette (22:35):
Nearly all of us encounter roadway traffic every
day, either as a driver or apassenger. Those experiences
might involve congestion orroadway maintenance. At some
point, it's a safe bet thatthose things are going to
happen, and we have at leastsome idea of when. But some
extreme events like earthquakesand bridge failures occur
(22:57):
without warning, and there's noway to forecast those in either
case. However, we can predictmore accurately than ever
before the nature and severityof their transportation
impacts, along with how wemight best respond to them. In
the absence of a crystal ball,that might just be the next
(23:19):
best thing. Thanks forlistening. Please take just a
minute to give us a review,subscribe and share this
episode, and please join usagain next time for a visit
with Ben Edelman. And a closelook at the latest trends in
crashes involving pedestrians.
Thinking Transportation is aproduction of the Texas A& M
(23:42):
Transportation Institute, amember of the Texas A&M
University System. The show isedited and produced by Chris
Pourteau. I'm your writer andhost, Bernie Fette. Thanks
again for listening. We'll seeyou next time .
Hello and welcome to Thinking
Transportation. Conversationsabout how we get ourselves and
the things we need from oneplace to another. I'm Bernie
Fette with the Texas A&MTransportation Institute.
Extreme events take manydifferent forms. With weather
calamities -- hurricanes andsuch -- we have the benefit of
(00:37):
advanced warning throughaccurate forecasting. Other
catastrophic occurrences likeearthquakes or terrorist
attacks happen suddenly withlittle or no warning at all.
What all such events share incommon though, is that their
impacts can be long-lasting andfar-reaching. In this episode,
(00:58):
we're visiting with someone whoworks to model those events to
better understand their impactson transportation networks and
help organizations developcontingency plans for how to
recover and resume normaloperations. Jeff Shelton is a
research scientist at TTI.
Welcome to ThinkingTransportation, Jeff. It's
(01:20):
great that you could share someof your time with us.
Jeff Shelton (guest) (01:22):
Thank you, Bernie . It's a pleasure
to be here.
Bernie Fette (01:25):
I was hoping we could start maybe with some
definitions. How would youexplain your work? Just for
someone who doesn't have a clueabout what traffic modeling is.
Someone at your high schoolreunion, for instance.
Jeff Shelton (01:41):
So we all encounter traffic every day
when we drive, right? Right.
And sometimes we experiencecongestion. Sometimes we
experience work zones.
Sometimes we experienceaccidents. Modeling is trying
to take existing conditions ofthe roadway network of traffic
and put it into a computersimulation. Now, if you wanna
(02:06):
do a prediction of something,then you can change the model
to predict what's gonna happen.
For instance, if there is goingto be an expansion of the
freeway and you're gonna have awork zone for six months, how
is that gonna impact traffic?
You know, if there's anaccident on the freeway and
you're delayed 20 minutes,you'll probably still take the
(02:27):
freeway tomorrow. But ifthere's a work zone for six
months, you may take adifferent route. So simulation
modeling is trying to predictthose changes in traffic
patterns on what people mightdo, given certain conditions
that happen on the roadway.
What I do is I build a computermodel and it could be something
as simple as an intersection,or it could be something as
(02:49):
large as the city of Houston.
You know, I've built severalcities around Texas, all the
major cities I've built, andthose are a little more
cumbersome, tedious, timeconsuming to build. But you get
a bigger picture of how trafficis running in your city. What I
try to do is just recreateexisting conditions and then
(03:11):
change the model to predictsome sort of future event .
Whether it's a work zone, anaccident, an improvement in the
roadway. Are you gonna charge atoll that you didn't charge
before? So I'm kind of tryingto make predictions of traffic.
Bernie Fette (03:24):
I can't help but ask you, does anybody ever ask
you if you work with a crystalball
Jeff Shelton (03:30):
My wife tells people I play video games for a
living
Bernie Fette (03:35):
Okay .
Jeff Shelton (03:35):
I tell her it's a little more elaborate than
that. It's no, it's no crystalball. It's, it's just a lot of
school, a lot of knowledge, alot of experience. Knowing how
traffic behaves. And then youbuild the models and you put, I
like to call 'em "what if"scenarios. What if okay , the
freeway is expanded, or if it'sclosed down for a certain time,
(03:57):
what happens to traffic?
Bernie Fette (03:59):
Okay. Another way I was thinking of asking about
this that might help peopleunderstand modeling. Are there
any modeling parallels in otherindustries, you know, other
than transportation where thereare people like you whose job
it is to predict outcomes orimpacts, maybe financial
industry, real estate, thingslike that?
Jeff Shelton (04:21):
Yeah, I mean, first thing that comes to mind
to me is the weatherman. Youknow, meteorologists, they
predict the weather based oncertain data that they get. You
know, so they look at existingconditions. They have computer
models that kind of give 'emall this data and then they
make a forecast for the nextday or for the next week. So
they can kind of tell you is itgonna rain or what's the
(04:43):
percentage of chance of rain?
Um, what the temperature'sgonna be like. Is it gonna be
sunny? Is it gonna be cloudy?
Right. You know , so that'skind of a parallel. They're
kind of making a prediction ofwhat the weather's gonna be
like. And I'm trying to make aprediction of what traffic's
gonna be like. Another parallelthat came to my mind is my wife
works in a restaurant, she's arestaurant manager. And so she
needs to predict how manyguests are gonna come to the
(05:06):
restaurant that day or thatweek. And with that she can
predict how much food toprepare, how many people to
have on staff. So she's takingall this data to make a
prediction about the future.
Bernie Fette (05:17):
Okay. In your work, you make a lot of
references to what you callextreme events. What exactly
constitutes one of those? Maybeyou could give us some
examples.
Jeff Shelton (05:29):
There could be a lot of different kinds of
extreme events, Bernie. Youknow, in Minneapolis,
Minnesota, the bridge collapsedthere.
Bernie Fette (05:37):
And that was the I-35 bridge over uh ,
Jeff Shelton (05:39):
I-35 over the Mississippi River. Yeah, it was
under construction. They hadadded weight on the bridge. It
was old and it collapsed. Weconsider that an extreme event
because how is traffic gonnaoperate now that that roadway,
I mean, because that served,you know, a lot of traffic. A
lot of cars cross that bridgeevery day and now that bridge
(06:01):
isn't there. So how doestraffic gonna react? Where is
it gonna go? You know, extremeevents could be in all kinds of
forms. It could be a tornado,it could be an earthquake,
flood , forest fires. Arefinery explosion.
Bernie Fette (06:16):
Yeah. And could be a man-made event as well,
right? A terrorist attack .
Jeff Shelton (06:21):
Terrorist attack could also be considered an
extreme event.
Bernie Fette (06:26):
I'm curious, the example you gave about the I-35
bridge in Minneapolis, didsomebody model that disaster
before it happened? Or was thatan example of an event that
surely could have used a bit ofmodeling before it happened?
Jeff Shelton (06:41):
Well, all cities model their traffic. So they
have a model of that traffic.
Now, they did not predict whatwas gonna happen. They didn't
know that bridge was gonnacollapse, so they didn't have
that. So they modeled itafterwards. To see what the
impact of traffic would bewithout that bridge in place.
So, okay , where is trafficgonna reroute to basically, and
how bad is it gonna be at theseother routes?
Bernie Fette (07:02):
Yeah, that sounds like they really could have
benefited from some of the workthat you and your colleagues
do.
Jeff Shelton (07:07):
Yes. If they had done some sort of advanced,
what-if scenarios of theseevents happening, they could
have maybe had some morecontingency plans in place for
rerouting traffic.
Bernie Fette (07:19):
Not to overlook the fact that in the wake of
that disaster, the relevantoperating agencies surely did
move quickly to fix thesituation and to replace that
bridge.
Jeff Shelton (07:31):
Yeah, I think it was up and running in about a
year. I mean, because of theamount of traffic that goes
through that bridge, it'sheavily used. So they needed
that thing up and running soon.
So yeah , almost immediatelythey started the process of
reconstruction.
Bernie Fette (07:45):
Let's stay on what-if scenarios. You apply
that phrase to when you tackleone of those projects, how do
you go about it? I think youtold me when we spoke about
this before, that you startwith a computer model of a
particular city or a region.
Can you walk us through thesteps that you take? Maybe even
(08:07):
using a specific example, howdo you go about your work?
Jeff Shelton (08:11):
Okay , so I'll , I'll give you an example. We
had our MPO ask us about anextreme event on the border. U.
S.-Mexico border. I live in ElPaso, so Juarez, Mexico is
right across the street. And wehave four points of entry. One
of them is in Sants Teresa ,New Mexico, but that's right
down the street. So we considerthat part of our neighborhood.
We have four ports of entrythat have traffic going back
(08:33):
and forth throughout the day.
And of course, CBP has toinspect every vehicle that
comes in. So of course there'sa long line of cars waiting to
come in, so they're allcongested. So our MPO asked us,
well what happened if thebridge in the center, it's
called Bridge of the Americasor BTOA for short -- What if
there was an extreme event andwe lost that bridge? And you
(08:56):
know, they're interested in theeconomic impact, but before you
can do that, you have to do itfrom a traffic perspective. So
they wanted to know what wouldhappen short term and long term
. And so what I do is Iactually have to build a
computer simulation of El Pasoand Juarez together in the
computer. We have a planningmodel in El Paso that our MPO
(09:19):
builds and then in Mexico, inJuarez, their MPO also has a
planning model that they use.
So what we did is we kind ofmarried those two models
together to have one big model.
So traffic goes back and forthacross the bridge cuz the , the
models are basically truncatedat the border. Okay, okay , you
have the bridge, but you don'thave traffic on the other side
for both models. So what we didis we actually married 'em , so
(09:40):
they talked to each other. Sotraffic flows back and forth.
So once we built the model andwe calibrated it, when
calibration means your modelkind of simulating actual
traffic conditions and it looksrealistic, that's what
calibration means. So once wedo that, then we ran a scenario
of shutting down the BOTAbridge northbound, and we did
(10:01):
it for an extended period oftime and they wanted to know
what was the impact. So ofcourse, short term , like the
day that the incident happened,you know, traffic was just
chaotic. It was a gridlockaround the port of entry
because it was closed, peoplecouldn't get across. It was
basically just gridlock allaround the port. But after
about a month of being closed,people know it's closed. So
(10:22):
they start rerouting to otherbridges. And that's where it
becomes interesting becausepeople are looking for their
shortest route, time dependentroute to get to where they're
going. All people kind of dothat. You want to get there as
quickly as possible. So that'swhat the model's trying to do.
It's looking for a shortestpath to get across the bridge.
And since the border bridge isclosed, they have to reroute to
other bridges. And there we cansee very apparently whether or
not the bridges can handle theexcess traffic. And we saw very
(10:45):
clearly that it couldn't.
Bernie Fette (10:46):
Right. And in that case, most of the traffic
that is at the border crossingis trying to come into the
United States? Or is it apretty even split between the
two?
Jeff Shelton (10:56):
Uh, it's pretty even split going back and
forth. And I just heard a stattoday that 70 percent of the
vehicles crossing are Mexican.
They come into the U. S. towork and then they cross back
to go home. And then 30 percentare U.S., so.
Bernie Fette (11:12):
That's a good example. Did you have another
one that you might like toshare?
Jeff Shelton (11:16):
Yeah, we've been thinking about modeling
something near the coast. Texasroutinely gets hit by
hurricanes, you know, in theGulf. And those are very large
extreme events. Those are notinstantaneous, like an
earthquake that may shut downthe bridge cuz it collapsed .
This is more, you have a littlebit of advanced warning, but
hurricanes move. So where youtell people to evacuate from
(11:39):
and when you tell 'em toevacuate is, you know, it's not
an exact science, but we'retrying to use these models to
give as realistic as possible,how people would react given
this condition, this hurricane,especially people that live
closest to the coast need toevacuate. The question becomes,
do you evacuate them earlierthan people further inland?
(12:03):
Where do you evacuate 'em to?
Do you evacuate 'em inside thecity in someplace safe? Or do
you evacuate 'em out of thecity? Do you open up roadways?
I don't know if you've heard ofcontraflow lanes, Bernie.
Contraflow lanes has basicallyopened up the opposite
direction of traffic to thefreeway. So you have northbound
and southbound in Houston andI-45, you open up the
(12:26):
southbound and you turn itaround, you make everything
northbound. So you have doublethe capacity to go out just to
get everybody out of the cityas quickly as possible. Right.
So we can do that with modelingalso.
Bernie Fette (12:35):
Right. To run from the storm. Okay . Right.
Yeah, that's a particularlyinteresting example because you
had talked earlier about theparallel of weather
forecasting, so it sounds likeyou would be making your
predictions on top of anotherset of predictions. Correct.
Jeff Shelton (12:51):
Bernie Fette (12:52):
So just to throw an added challenge in there,
can you talk a little bit aboutthe operational impacts that
you've helped to predict? And Iguess specifically what I'm
asking about is what that meansin terms of people having to
change commute routes and alsowhat those impacts might mean
for shippers for people incommercial enterprises.
Jeff Shelton (13:15):
I live in El Paso and Interstate 10 runs right
through the heart of El Paso.
The Interstate goes coast tocoast, actually from California
to Florida, but it goes rightthrough the heart of El Paso.
And this section in El Paso isabout 60 years old. I mean, the
freeway, you know, everythinghas a shelf life, right? So
they are about to reconstructI-10 and they're gonna do it in
(13:39):
sections. And the first sectionthey wanna do is around the
downtown portion of El Paso andI-0, and they have three
different design alternatives.
They want to know which oneperforms the best. So we take
their designs and we put 'em inthe model and we run all three
(14:02):
scenarios to see how trafficreacts, where people are going.
If the roadways that theychange , are they reconfigured?
Can they handle the traffic nowthat it's rerouting? And also
some of the construction maynot be warranted. And let me
explain that a little further.
(14:22):
So they have the designdrawings, and at one
interchange they have thosethings, those Texas U-turns,
you know, where you just , youcan , yes . You don't have to
go through a light , just makea U-turn real quick. And so
when I'm running this model forEl Paso and I 10, I found
actually the one right by ouroffice right here. Nobody was
taking those U-turns, there wasvirtually no traffic. I mean,
(14:43):
zero on two of those scenarios.
And so when I'm gonna presentto the DOT Department of
Transportation, they need toknow that because why are you
gonna spend millions of dollarson these U-turns? If nobody's
gonna use it, wouldn't thatmoney be better spent somewhere
else?
Bernie Fette (15:00):
Right, right. Can you talk a little bit more
about what your work isintended to lead to? You talked
a little about contingencyplans. Can you talk a little
about what might be included inthose contingency plans?
Jeff Shelton (15:15):
I guess contingency plans for regional
stakeholders. So if there is anextreme event, you have to be
able to inform the public onwhich routes to take.
Bernie Fette (15:28):
Uh-huh,
Jeff Shelton (15:28):
You know, what's closed,
Bernie Fette (15:30):
Right.
Jeff Shelton (15:31):
So if we can use a simulation model to predict
what would happen if an extremeevent occurred, then we can
give that to, for instance, thecity of El Paso. They may be
able to retime some signaltimings that are not adequate
for this rerouting. You know,if there's an accident on the
freeway and people are alldiverting off of one ramp,
(15:52):
they're gonna have to gothrough this intersection. You
may have to re-time and addadditional green time on that
phase just to get all thattraffic exiting the ramp. You
know, so it's just, you know,helping them make decisions in
an orderly fashion just to helppeople get to their destination
quicker.
Bernie Fette (16:07):
It sounds like on top of a certain number of
uncertainties about things thatyou can't really control,
you've also got the fact thatwe're talking about people --
whose actions can not always beas predictable as you might
like. But I guess you in thatcase, kind of have to go on
patterns, kind of like youmentioned with those Texas
(16:28):
U-turns and how you noticedthat even though the option was
there, there were a lot ofpeople who weren't taking
advantage of that particularoption.
Jeff Shelton (16:35):
Yeah. One of the jokes that we have as modelers,
we say the hardest thing tomodel is human behavior.
Bernie Fette (16:41):
Yeah. I'm curious about any surprises that you
might have encountered in yourwork. Have you ever gone into a
modeling task, a predictivetask, expecting to see one
outcome and instead seeingsomething entirely different?
Jeff Shelton (16:58):
Yeah, we did.
This was actually a researchproject we did seven, eight
years ago, maybe a little bitlonger than that. And it had to
do with the new autonomousvehicles coming out now,
autonomous and connectedvehicles. So the cars talk to
each other, they inform eachother at the distance, their
speed. It's called cooperativeadaptive cruise control. Right
now cars have adaptive cruisecontrol, ACC and then you add
(17:21):
that C on their cooperative.
Adaptive cruise control isbasically the cars talking to
each other and not just, youknow, reacting to traffic
conditions. If it sees trafficahead, it slows down by itself.
No. This is actually carstalking to each other, telling
'em their speed, theirlocation, their direction,
everything. And so the questionwas, if we have this technology
in the future, will it helpalleviate congestion? And we
(17:42):
thought, yeah, it probablycould. Let's model it and find
out. We've modeled severaldifferent scenarios. Each
scenario, we increased thenumber of connected vehicles in
the model. And the moreconnected vehicles that we
simulated, it seemed liketraffic conditions got worse.
Okay . And we thought theywould get better. We thought
the speeds would improve. Thetravel times would shorten, and
(18:04):
it was the total opposite. Wesaw that the connected vehicles
messaging would send a messageback to another connected
vehicle behind it that in turnwould broadcast a message to a
vehicle behind that one. Andthen the message just
propagates back further andfurther. And the more vehicles
that are connected sending thismessages, the more vehicles
that are slowing down. So itended up slowing down traffic
(18:25):
more, and that was the totalopposite of what we thought
would happen.
Bernie Fette (18:28):
Right. It's a pretty sharp contrast. So
you've got this mix, this moshpit of different kinds of cars,
latest technology in manycases, but you know, 10, 15
year old cars at the same time.
Jeff Shelton (18:40):
Yeah. And so I heard one person say that,
well, we'll just make allvehicles automated connected in
the future. Well , I don'tthink that's ever gonna happen.
You know, they've been tryingto ban smoking for how long and
people still smoke, you know?
Yeah, yeah . If you drive aFerrari or a 1968 Shelby
Mustang, do you want it todrive itself or do you want to
drive it?
Bernie Fette (19:00):
Yeah.
Jeff Shelton (19:01):
Right.
like huge cities like the cityof Houston, the one limiting
factor right now that I thinkwe're encountering is model
runtime. So to run Houston andyou know, you have to run it
over multiple iterations. Andwhat I mean by that, you
(19:22):
simulate it for a whole day andit looks at the travel patterns
of the vehicles, and then you ,you simulate it again and it's
like another day of learning.
You know, like if you move to anew city and you don't know
your route to work, you trydifferent routes until you find
the one that's best and getsyou the quickest. That's how
the model works. Mm-hmm.
Houston about 15 iterationsuntil we get what we call
(19:45):
convergences. But thatbasically means the model's
stabilized. Okay. And thattakes three days to run that.
Bernie Fette (19:52):
And that's where you get to start noticing some
of those patterns that youtalked about earlier.
Jeff Shelton (19:57):
RIght. And when I say three days burning , I'm
talking about three days on avery good computer. If you just
use an average computer, I usea a different computer. Three
days, took 12 days on thatcomputer.
Bernie Fette (20:08):
Okay. Well, I'm glad for what you shared there
because my next question had todo with what you hope to see in
terms of future advancements.
The technology has moved alongquite a bit to help you do your
work better, faster, et cetera. So then is it pure computer
muscle, computer capacity thatwhere you see the biggest needs
(20:31):
for improvement or, or thereother things that you think
would improve the modeling thatyou and your colleagues do?
Jeff Shelton (20:37):
Well, in addition to the improvement of the model
performance, but I think justthe algorithms that are built
into the models. You know, thealgorithm that predicts
shortest path from where you'recoming from to where you're
going. Okay. That's origin anddestination. That's what we
call the model. Okay. Andthere's algorithms to predict
that and they can always bebetter.
Bernie Fette (20:59):
So basically doing the work faster and doing
it with more precision is whatyou hope to move toward in the
future.
Jeff Shelton (21:06):
Correct.
Bernie Fette (21:06):
You know, for as long as I've known you, you've
always appeared to be reallyexcited about the work that you
do. So what exactly is it thatmotivates you to show up to
work every day ?
Jeff Shelton (21:19):
Well, I, I love modeling. Yeah. Like I said
before, my wife says I playvideo games for a living,
Bernie Fette (21:25):
Jeff Shelton (21:26):
Sometimes I do look at it that, I mean , um,
it's a lot more challengingthan playing a video game
because you're actuallypredicting real-life
conditions. But it is kind ofneat to see your predictions
come out to reality. And I'llgive you an example. So the
Texas Department ofTransportation asked us to do a
(21:48):
little study out on the eastside of El Paso where traffic
was spilling back from theintersection onto the freeway
in the afternoon. So I went outthere, I looked at it, I built
a model for it, and I gave themseveral alternative design
changes they could do to helpimprove traffic. And they
(22:08):
implemented those things. Soit's really cool to go out
there and see your predictionand your recommendations get
put to life in real-worldconditions.
Bernie Fette (22:17):
Yeah. Really great example of public
service, honestly. JeffShelton, research scientist at
TTI. Jeff, thanks very much forspending some time with us, and
thanks for your hard work andyour enthusiasm.
Jeff Shelton (22:31):
Thanks, Bernie. I really appreciate it.
Bernie Fette (22:35):
Nearly all of us encounter roadway traffic every
day, either as a driver or apassenger. Those experiences
might involve congestion orroadway maintenance. At some
point, it's a safe bet thatthose things are going to
happen, and we have at leastsome idea of when. But some
extreme events like earthquakesand bridge failures occur
(22:57):
without warning, and there's noway to forecast those in either
case. However, we can predictmore accurately than ever
before the nature and severityof their transportation
impacts, along with how wemight best respond to them. In
the absence of a crystal ball,that might just be the next
(23:19):
best thing. Thanks forlistening. Please take just a
minute to give us a review,subscribe and share this
episode, and please join usagain next time for a visit
with Ben Edelman. And a closelook at the latest trends in
crashes involving pedestrians.
Thinking Transportation is aproduction of the Texas A& M
(23:42):
Transportation Institute, amember of the Texas A&M
University System. The show isedited and produced by Chris
Pourteau. I'm your writer andhost, Bernie Fette. Thanks
again for listening. We'll seeyou next time .
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