December 12, 2024 • 23 mins
Tired of gridlock and traffic jams? Smart cities are the future, but their roads often feel stuck in the past. Traditional traffic management solutions are no match for the complexities of modern urban life.
In this episode, we explore the future of transportation with AI and visual data taking the front seat. We discuss the importance of real-time analytics combined with historical data, and gain some insights into the critical role of visual capabilities in traffic management, how AI-driven insights aid city planners, and the ways these technologies promote sustainability.
Join us as we explore these ideas with:
Joseph Harvey, ITS Market Sector Leader, ISS
Christina Cardoza, Editorial Director, insight.tech
Podcast Topics
Joe answers our questions about:
- State of traffic management
- AI’s power in city planning
- Empowering traffic flow
- Opportunities for improvement
- Traffic technology implementations
- Taking security into consideration
- Partnerships making it possible
Related Content
To learn more about intelligent transportation systems, read Video Intelligence Illuminates Path to Pedestrian Safety. For the latest innovations from ISS, follow them on X at @isscctv and on LinkedIn.
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
(upbeat music)
(upbeat music)
- Hello and welcome to"insight.tech Talk,"
where we explore the latest IoT,
AI, edge, and network technology trends
and innovations.
As always, I'm yourhost, Christina Cardoza,
(00:21):
Editorial Director of insight.tech
and today we're taking it to the road,
talking about intelligenttraffic management
with Joe Harvey from ISS,
which stands for IntelligentSecurity Systems.
Hey, Joe, how's it going?
- It's going well.
How about yourself?
- Not too bad.
I'm excited to diveinto this conversation,
but first I want to know a little bit more
about yourself and what you do at ISS.
(00:43):
- So here at ISS, I'm theITS Market Sector Lead.
What I'm doing is taking our leaders,
our founders, what theirvisions are for the analytics
and the neural networking efforts
that we do here at our company,
and bringing those intothe ITS market space.
ISS has a background of 25-plus years
(01:07):
in being a security and safety company,
and we've developed thisvast network of analytics
and just really safety-solving solutions.
In the last three to four years,
that has now been my responsibility
to bring that into the ITS market space
and allow those solutionsto really take fruit
(01:29):
and take hold in the marketplace.
- Yeah, absolutely.
It's amazing to see the advancements
and what these solutions
can do to improve industries.
A lot of times on the podcast,
we're talking about
how they can help manufacturing plants
or retail areas, and today we're talking
about traffic management,
which I think is somethingthat a lot of people,
(01:49):
whether you're the driver,commuter, passenger,
they struggle with.
So, it's amazing to seea part of the technology
and advancements be able to be applied
to everyday life.
So, I wanted to start theconversation just looking
at, you know, the state oftraffic management today,
and where are some of the improvements
where a company like ISScan come in and help?
(02:11):
- Yeah, that's a really great question.
I think you hit on somethingin the lead-up there,
too, that these products
and what ISS has done have been
in the marketplace for 25-plus years as AI
and neural networking
becomes a little more buzzwordy
or just even a littlemore in the conscious
of everyday space.
(02:32):
We are seeing them applied
in a lot of differentareas, specifically ITS.
Being able to take thatfundamental understanding
and growing from a really grassroots side
where we control the buildof all of the products,
(02:56):
all of the solutions,
and kind of taking a a la carte approach
when you're looking at ITS
to apply those specificallyinto the solution space.
ITS, for a very long time,
has had a lot of traditional measures,
has been grounded on alot of just, I'd say,
(03:18):
pushback to technology attimes, right and wrong,
because we do have great solutions
that work in this space.
But as more and more technology feeds
into that area,
and they can see just froma safety-saving device,
a data-rich standpoint,
what a solution like ISScan offer, or several
(03:42):
of the companies in thespace that are pushing
into it now with neural networking,
video intelligence, andpushing that forefront,
we're seeing things rapidly change.
That's where the excitementreally is in ITS now,
how the interconnected realm
is going to really workwith everyday motorists
and how companies like ISScan be at the forefront
(04:04):
of that conversation.
- Yeah, so let's dig deeperinto that a little bit,
you know, AI's role in all of this.
How can AI start to be integrated
into something like trafficmanagement that drivers,
city planners, government officials,
that we start seeingsome improvements there?
- Yeah, absolutely.
(04:25):
We have a vast portfolioof products that AI,
neural network, video intelligence are
at the core of every single one of those.
For an end-user agency,
when you're taking a look
at either data gathering from a standpoint
of vulnerable road users,
from your traveling everyday motorist,
urban, arterial, out on freeway,
(04:47):
being able to use devices
that are gathering this data in a manner
that is at a 95%, 96%,
97% accuracy,
and being able to do itreal-time, automated,
where an operator is then only responding
to a specific need.
One of the specific products
(05:09):
that we have here at ISS
is a pedestrian safety device.
It has a dynamicillumination for pedestrians
within crosswalks.
When you think about themore traditional measures,
you're asking a driver toreact to a notification that,
hey, a pedestrian may be in the crosswalk.
When you think aboutdriving in a school zone,
(05:31):
you come up,
you have that yellowstatic sign that says,
hey, this is a school crossing.
What we have done is leveraged AI
and the camera technology in order
to dynamically illuminate at dusk
or nighttime hours a pedestrian,
a child, a mobility device
that is within the crosswalk
(05:52):
and actually show where they are.
This was a revolutionarything for our company
in the industry just
because no one has doneanything like that before.
If you also take a look atsignalized intersection,
more of the traditional road measures are
if you ever come up andyou see just a cutout
in the road and youwonder what construction
or what may have happened,
(06:13):
there's magnetic loops in the ground.
If you look up at the intersection,
you may see a number of devices up there,
but with the developmentof cameras along with AI,
we're really seeing theability for the controller,
the smarts, the brains behind
those intersections being able
to greater understand its environment,
(06:34):
be able to react real timeif there is an incident.
So, if you have collisions,
near miss is a really bigtopic for us right now.
Having an operator take a look at that,
or traditionally someonehas picked up the phone
and called and said,
wow, you just had someone go through
that intersection at 50 milesper hour on a red light.
You have an issue here.
(06:56):
An engineer would haveto go out to the field,
take a look at what'sgoing on with cameras,
with AI, and with video intelligence.
All of that is at the fingertipsconstantly of operators,
and they're able to react much quicker
and or look at these data sets long term
to affect change on the roadways
when they're looking at design
or reshaping of the roadway itself.
(07:18):
- Yeah, I can imaginethose visual analytics just
become even that more important.
I'm thinking about sometimes I see workers
on the side of the road,
they have their radar guns out.
They're making sure testing
the speed of everybody going,
making sure the speed is correct there,
but you can havedifferent things happening
at different times,
(07:38):
and one person going like really fast
at some area could messup the entire sample data.
So, if you're able to visually see
what happened at that time,
it can help provide deeper insights beyond
that real-time analytics
that you were talking about.
You know, I'm thinking being able
to improve overall city planning
(07:58):
and being able to improvethese traffic areas
like the lights and howoften things happen there.
- The original adoption of cameras
was a little hit or miss
because of just their inability at times,
especially during weather events.
But with the advancements there
and then along with youhave this neural network
that is able to understandthose environments
(08:20):
and make adjustments on the fly,
exactly what you said.
Instead of getting partial data outliers
and making assessments on ones,
and zeros that a engineermight be looking at,
they're able to go back
and actually pull those video feeds
and really drive true meaning
and understanding to the data
that they're looking at.
(08:41):
- I'm curious, since we're using AI,
are you guys able to implement any models,
or automatic triggers thatsay if one event happens,
this event will happen?
I'm thinking, just frommy personal experience,
my parents live a mile down the road,
and there's just onelight between us and them.
And that light can be five minutes long
(09:02):
to get to their house,
and there will be no carscoming on either way.
So, I'm just wondering
if there could be likean AI trigger that says,
okay, a car has pulled up,
there's no other cars coming,
we're going to make it green,
and I can get to my parents' house faster.
- Yeah, absolutely.
Something as simple as,
yes, at the given intersection
(09:23):
where you're able to visually take a look,
basically place a call
into the signalized controller in order
to affect the signal phase and timing,
or SPAT, absolutely.
That is something thatwe are presently doing
and we're seeing in the marketplace.
There's a statistic that anywhere from,
I think there's 300,000to 400,000 intersections
(09:46):
within the United States,
signalized intersections,
somewhere around half
of those still eitherare without detection
or some sort of outdatedor historical methods.
So like the loops thatI had made mention of
and being able to addthese visual aids that,
(10:06):
again, yes, the algorithms
and the video intelligenceis able to advise
that a vehicle is thereand limit the congestion,
limit the environmental impact
of a car sitting and waiting two,
three minutes at someof these intersections,
but also then gather all of that data.
(10:27):
What time of day?
What type of vehicles?
Are there other pedestrians?
Do you have a heavy bike
or scooter population
that might be an alternative method
that you were unaware
of during certain times of day?
All of these data points
are really helping engineerscontinue that conversation.
And honestly, long-term,
(10:48):
as more and more devices get connected
and more and more ofthese end user agencies
are able to take inputsof an alerted event
and interconnect everything
that they have at their fingertips,
they're making the road safer
and real impact at time ofevent to affect that change.
(11:09):
And it is honestly the reason a lot of us
are in this industry becausewe can see that change,
but we are seeing it rightnow happen very quickly.
- Yeah, all of these benefits,
it would seem to me a no brainer
to start implementing thisintelligent technology
at these intersections on the roads.
But like you mentioned,
a lot of these intersectionsstill have outdated
(11:32):
or traditional technology.
So how can they startmaking these improvements?
What are the challengesto implementing it?
Can they leverage anyexisting infrastructure?
How does that work?
- Yeah, that's at least fromour standpoint here at ISS,
one of the benefits to the end user agency
that we are taking a look at
(11:52):
with the just pure amount of capital
that is spent on infrastructure,
what we are looking to do is leverage
what customers might have in field already
and being able to build on top of that.
When you hear scalability
or flexibility from a manufacturer,
what that means from ISS is the cameras
(12:13):
that are already out withinthe traveling motorist,
within the infrastructureof a end user agency.
We are able to take that input
and just leverage our video intelligence
and our neural network in order
to provide whatever outcome
they may be looking for.
If that is something likethe intersection technology,
(12:35):
if that is for pedestrian safety,
is it just incident detection?
Use what you've already had in place
and leverage that,
and then allow ISS as amanufacturer to continue
to build on top of that
and give the scalability to agencies.
Funding is our tax dollars,
and we understand that much
(12:58):
of the ITS market space is exactly that.
We need to make sure thatthose dollars being spent,
even if new technology
and advancements are being made,
we can make best use ofthose dollars pre-spent
by an agency and ultimatelythe traveling public
that is funding what is goingout on their road space.
(13:18):
- Yeah, absolutely.
So, I want to be able
to give our listeners a clearer picture
of how this works.
So I'm curious if you haveany real world examples
or customer case studies,
anything that you can share with us
of how ISS came in,
whether you're workingwith a city official,
(13:38):
government official, how you guys came in
and implemented the technology
and what the result of that was.
- Yeah, there's a numberoff the top of my head.
A few are specificallyin tolling agencies,
where if you've ever driven
in heavy populated areas
and been on a toll road,
(14:01):
there are a number of cameras
and just devices that are up
on the gantries alreadyin use by that agency.
Being able to go in andprovide, in this case,
our LPR solution
where we're able to bea part of the totality
of what that governing body may be doing.
So, in that case,
(14:21):
if we are doing license plate capturing,
in some places we're justdoing flow estimation.
So, your speed, volume, gap, occupancy,
different things like that.
Again, the data engineersjust hold dearly,
need constantly in orderto make these decisions
about where they're going
to bring different roads into,
how they're going to reshape
where we might be driving.
(14:43):
So that is one aspect.
I would say the biggest scale
we've done was actually in Mexico City
as a global companyfounded here in the U.S.
But as a global company,
we actually haveimplemented in Mexico City
(15:04):
our SecurOS, which is our operating system
within their entire agency.
And we are that operating system,
the end point for their operators to use
and have somewhere north of 65,000 cameras
along with alarming devices, horns,
(15:25):
and really have taken their smart city,
allowed the interconnection
between all of these physical devices
to live on our network.
And we are able to then leverage,
again, that neural network
to really just open up the possibilities.
If you think about that number of cameras
(15:45):
and the number of personnel
you would have to have in order
to even review or take a look at live,
allowing our system toreally be that point
of the spear for them
and everything else
to just kind of livebehind was transformational
for Mexico City.
So that's our feather in the hat.
That was a very large project for us,
(16:08):
but allows you to kind of understand
the scale to which some
of these very large cities in the world
or here in the U.S. have
and kind of what their need
is when reviewing just inbound video
into their system.
- Those are awesome use cases to hear.
And I imagine with SecurOS, obviously,
(16:31):
intelligent security systems,
security is in your name.
So, you know, when we think of that,
sometimes it's thought about like safety
and surveillance and protecting
what the cameras are capturing,
but also on the back end,
you know, the securitywe're talking about,
collecting license plate data,
collecting videos of drivers.
So, you know, I assume that SecurOS
(16:54):
any other technology
and solutions that you guys have,
privacy and security ofkeeping that data safe
and making sure that, you know,
personal data is protected is something
that you guys are also on top of.
- Absolutely, it is atthe forefront of kind
of the digital age
has been both a privacy aspect,
(17:16):
but then a security aspect.
Yes, as our name implies,
ISS, Intelligent Security Systems,
as you may have mentioned, too,
we had our grounding
in what physical security meant
for real world applications
and have continued to build on that.
(17:36):
From the privacy standpoint,
the industry has taken a branched approach
in what they are looking
at from the standpoint
of what we are actually capturing out
on the roadway where we can blur faces,
blur license plates,
actually have the intelligence
within the cameras understand
(17:57):
and help us remove anyof that personal data.
But from the standpoint
of then what is captured,
working with each agency individually
on what their standards are,
and then from a securitystandpoint here at ISS,
being able to followall the major outlines
for your security, for your privacy
(18:18):
and making sure that any advancement
that we might make,
that is the parallel path
that we are making sure we are following,
because trust
and kind of understanding
from our users isparamount to our success.
(18:40):
It has to be a part of what we do.
And for us here at ISS has been kind
of a driving parallel path
to what we bring to market.
- Absolutely.
And that's great tohear because technology
and all these things,
no matter how big the benefits
that they do bring,
there's always going to be those privacy
or data concerns.
(19:01):
So, it's great to beable to have a solution
that gives you both.
You can take advantageof all these benefits
and ensure that data
and sensitive information is protected.
And I also, since we're talking about AI,
I wanted to ask,
and I should mention insight.tech
and the "insight.tech Talk,"
we are sponsored by Intel,
but I can imagine being able to apply AI
(19:25):
to these different areas.
You need it to be high performance.
You're collecting real time analytics.
So that needs to happen at the edge.
So, I can imagine that you are using
and partnering with Intel in all of this.
So, I'm just curious what the value
of that partnershipand that technology use
from Intel has been for ISS.
- Yes, I would say almost invaluable
(19:46):
when you're going to try to really put
into a box what companies like ISS needs
from a performance standpoint
and the partnership,
we need with a company like Intel.
As advancements continually get made,
(20:06):
more and more
of our end user agencies are asking us
to include different data points
and just push the capabilities
of what the physical hardware
and software are
without a company like Intel understanding
and the forethought they have
of what the marketplace is going to need
and how they can be abenefit to manufacturers
(20:28):
to instantly react, to have solutions,
and truly partner with us
to solve problems
that we are seeing in the real world.
You can't understand
how great of an impact
that has from our standpoint
and ultimately the rest of the industry
that is relying on anIntel to really continue
(20:50):
to push that forward for us.
- Awesome.
Well, I can't wait to see some
of these technologies
and advancements come to my area
and be just more widely adopted
and more spread out, you know,
get to my parents' house a little faster.
(Joe laughing)
But I appreciate this conversation.
It's been very interesting to hear.
Before we go, Joe,
(21:11):
I just wanted to turn it back to you
one last time.
Any final thoughts
or key takeaways you want toleave our listeners with today?
- Really just understanding
what is possible from a Intel,
from a video intelligence company,
and what the traveling motorists
are going to see.
We are trying to solve problems
(21:32):
that are either real time today,
actual events that you are seeing out
on the roadway,
but also looking to partner
and solve problems
that we don't even understand yet.
As that connected realm continues
to kind of build itself,
more and more challenges
will be brought to us
and asked of us to solve.
(21:54):
And we believe here at ISS
that we are up for that challenge.
But we look forward tocontinuing conversations
with all parties to see
how we can leverage the strength
of what we've builthere over 25 plus years
and everybody else that is interconnected
within solving these problems
(22:14):
to transform the traveling public.
So, we look forward to it.
We appreciate being a part of it.
ITS is in our lifeblood here at ISS,
so we're just happy to be a part
and appreciate the time
to just share a littlebit of our knowledge
and the excitement behind these products,
the solutions, and theentire space itself.
- Great.
(22:35):
And I would urge all of our listeners,
like Joe said, see howyou can partner with ISS,
visit the website, haveany real-world problems
you're looking to solve.
We've talked
about intelligenttraffic management today,
but ISS offers many different solutions
across many different industries.
So, you know, have a conversation
with Joe and the team
and see how they can help you out,
solve your problems.
(22:56):
Also, keep up with us on insight.tech
as we continue to cover partners
like ISS in this space.
So, thank you, Joe, again for joining us.
Thank you to our listeners.
Until next time, this hasbeen "insight.tech Talk."
(upbeat music)
(upbeat music)
(upbeat music)
- Hello and welcome to"insight.tech Talk,"
where we explore the latest IoT,
AI, edge, and network technology trends
and innovations.
As always, I'm yourhost, Christina Cardoza,
(00:21):
Editorial Director of insight.tech
and today we're taking it to the road,
talking about intelligenttraffic management
with Joe Harvey from ISS,
which stands for IntelligentSecurity Systems.
Hey, Joe, how's it going?
- It's going well.
How about yourself?
- Not too bad.
I'm excited to diveinto this conversation,
but first I want to know a little bit more
about yourself and what you do at ISS.
(00:43):
- So here at ISS, I'm theITS Market Sector Lead.
What I'm doing is taking our leaders,
our founders, what theirvisions are for the analytics
and the neural networking efforts
that we do here at our company,
and bringing those intothe ITS market space.
ISS has a background of 25-plus years
(01:07):
in being a security and safety company,
and we've developed thisvast network of analytics
and just really safety-solving solutions.
In the last three to four years,
that has now been my responsibility
to bring that into the ITS market space
and allow those solutionsto really take fruit
(01:29):
and take hold in the marketplace.
- Yeah, absolutely.
It's amazing to see the advancements
and what these solutions
can do to improve industries.
A lot of times on the podcast,
we're talking about
how they can help manufacturing plants
or retail areas, and today we're talking
about traffic management,
which I think is somethingthat a lot of people,
(01:49):
whether you're the driver,commuter, passenger,
they struggle with.
So, it's amazing to seea part of the technology
and advancements be able to be applied
to everyday life.
So, I wanted to start theconversation just looking
at, you know, the state oftraffic management today,
and where are some of the improvements
where a company like ISScan come in and help?
(02:11):
- Yeah, that's a really great question.
I think you hit on somethingin the lead-up there,
too, that these products
and what ISS has done have been
in the marketplace for 25-plus years as AI
and neural networking
becomes a little more buzzwordy
or just even a littlemore in the conscious
of everyday space.
(02:32):
We are seeing them applied
in a lot of differentareas, specifically ITS.
Being able to take thatfundamental understanding
and growing from a really grassroots side
where we control the buildof all of the products,
(02:56):
all of the solutions,
and kind of taking a a la carte approach
when you're looking at ITS
to apply those specificallyinto the solution space.
ITS, for a very long time,
has had a lot of traditional measures,
has been grounded on alot of just, I'd say,
(03:18):
pushback to technology attimes, right and wrong,
because we do have great solutions
that work in this space.
But as more and more technology feeds
into that area,
and they can see just froma safety-saving device,
a data-rich standpoint,
what a solution like ISScan offer, or several
(03:42):
of the companies in thespace that are pushing
into it now with neural networking,
video intelligence, andpushing that forefront,
we're seeing things rapidly change.
That's where the excitementreally is in ITS now,
how the interconnected realm
is going to really workwith everyday motorists
and how companies like ISScan be at the forefront
(04:04):
of that conversation.
- Yeah, so let's dig deeperinto that a little bit,
you know, AI's role in all of this.
How can AI start to be integrated
into something like trafficmanagement that drivers,
city planners, government officials,
that we start seeingsome improvements there?
- Yeah, absolutely.
(04:25):
We have a vast portfolioof products that AI,
neural network, video intelligence are
at the core of every single one of those.
For an end-user agency,
when you're taking a look
at either data gathering from a standpoint
of vulnerable road users,
from your traveling everyday motorist,
urban, arterial, out on freeway,
(04:47):
being able to use devices
that are gathering this data in a manner
that is at a 95%, 96%,
97% accuracy,
and being able to do itreal-time, automated,
where an operator is then only responding
to a specific need.
One of the specific products
(05:09):
that we have here at ISS
is a pedestrian safety device.
It has a dynamicillumination for pedestrians
within crosswalks.
When you think about themore traditional measures,
you're asking a driver toreact to a notification that,
hey, a pedestrian may be in the crosswalk.
When you think aboutdriving in a school zone,
(05:31):
you come up,
you have that yellowstatic sign that says,
hey, this is a school crossing.
What we have done is leveraged AI
and the camera technology in order
to dynamically illuminate at dusk
or nighttime hours a pedestrian,
a child, a mobility device
that is within the crosswalk
(05:52):
and actually show where they are.
This was a revolutionarything for our company
in the industry just
because no one has doneanything like that before.
If you also take a look atsignalized intersection,
more of the traditional road measures are
if you ever come up andyou see just a cutout
in the road and youwonder what construction
or what may have happened,
(06:13):
there's magnetic loops in the ground.
If you look up at the intersection,
you may see a number of devices up there,
but with the developmentof cameras along with AI,
we're really seeing theability for the controller,
the smarts, the brains behind
those intersections being able
to greater understand its environment,
(06:34):
be able to react real timeif there is an incident.
So, if you have collisions,
near miss is a really bigtopic for us right now.
Having an operator take a look at that,
or traditionally someonehas picked up the phone
and called and said,
wow, you just had someone go through
that intersection at 50 milesper hour on a red light.
You have an issue here.
(06:56):
An engineer would haveto go out to the field,
take a look at what'sgoing on with cameras,
with AI, and with video intelligence.
All of that is at the fingertipsconstantly of operators,
and they're able to react much quicker
and or look at these data sets long term
to affect change on the roadways
when they're looking at design
or reshaping of the roadway itself.
(07:18):
- Yeah, I can imaginethose visual analytics just
become even that more important.
I'm thinking about sometimes I see workers
on the side of the road,
they have their radar guns out.
They're making sure testing
the speed of everybody going,
making sure the speed is correct there,
but you can havedifferent things happening
at different times,
(07:38):
and one person going like really fast
at some area could messup the entire sample data.
So, if you're able to visually see
what happened at that time,
it can help provide deeper insights beyond
that real-time analytics
that you were talking about.
You know, I'm thinking being able
to improve overall city planning
(07:58):
and being able to improvethese traffic areas
like the lights and howoften things happen there.
- The original adoption of cameras
was a little hit or miss
because of just their inability at times,
especially during weather events.
But with the advancements there
and then along with youhave this neural network
that is able to understandthose environments
(08:20):
and make adjustments on the fly,
exactly what you said.
Instead of getting partial data outliers
and making assessments on ones,
and zeros that a engineermight be looking at,
they're able to go back
and actually pull those video feeds
and really drive true meaning
and understanding to the data
that they're looking at.
(08:41):
- I'm curious, since we're using AI,
are you guys able to implement any models,
or automatic triggers thatsay if one event happens,
this event will happen?
I'm thinking, just frommy personal experience,
my parents live a mile down the road,
and there's just onelight between us and them.
And that light can be five minutes long
(09:02):
to get to their house,
and there will be no carscoming on either way.
So, I'm just wondering
if there could be likean AI trigger that says,
okay, a car has pulled up,
there's no other cars coming,
we're going to make it green,
and I can get to my parents' house faster.
- Yeah, absolutely.
Something as simple as,
yes, at the given intersection
(09:23):
where you're able to visually take a look,
basically place a call
into the signalized controller in order
to affect the signal phase and timing,
or SPAT, absolutely.
That is something thatwe are presently doing
and we're seeing in the marketplace.
There's a statistic that anywhere from,
I think there's 300,000to 400,000 intersections
(09:46):
within the United States,
signalized intersections,
somewhere around half
of those still eitherare without detection
or some sort of outdatedor historical methods.
So like the loops thatI had made mention of
and being able to addthese visual aids that,
(10:06):
again, yes, the algorithms
and the video intelligenceis able to advise
that a vehicle is thereand limit the congestion,
limit the environmental impact
of a car sitting and waiting two,
three minutes at someof these intersections,
but also then gather all of that data.
(10:27):
What time of day?
What type of vehicles?
Are there other pedestrians?
Do you have a heavy bike
or scooter population
that might be an alternative method
that you were unaware
of during certain times of day?
All of these data points
are really helping engineerscontinue that conversation.
And honestly, long-term,
(10:48):
as more and more devices get connected
and more and more ofthese end user agencies
are able to take inputsof an alerted event
and interconnect everything
that they have at their fingertips,
they're making the road safer
and real impact at time ofevent to affect that change.
(11:09):
And it is honestly the reason a lot of us
are in this industry becausewe can see that change,
but we are seeing it rightnow happen very quickly.
- Yeah, all of these benefits,
it would seem to me a no brainer
to start implementing thisintelligent technology
at these intersections on the roads.
But like you mentioned,
a lot of these intersectionsstill have outdated
(11:32):
or traditional technology.
So how can they startmaking these improvements?
What are the challengesto implementing it?
Can they leverage anyexisting infrastructure?
How does that work?
- Yeah, that's at least fromour standpoint here at ISS,
one of the benefits to the end user agency
that we are taking a look at
(11:52):
with the just pure amount of capital
that is spent on infrastructure,
what we are looking to do is leverage
what customers might have in field already
and being able to build on top of that.
When you hear scalability
or flexibility from a manufacturer,
what that means from ISS is the cameras
(12:13):
that are already out withinthe traveling motorist,
within the infrastructureof a end user agency.
We are able to take that input
and just leverage our video intelligence
and our neural network in order
to provide whatever outcome
they may be looking for.
If that is something likethe intersection technology,
(12:35):
if that is for pedestrian safety,
is it just incident detection?
Use what you've already had in place
and leverage that,
and then allow ISS as amanufacturer to continue
to build on top of that
and give the scalability to agencies.
Funding is our tax dollars,
and we understand that much
(12:58):
of the ITS market space is exactly that.
We need to make sure thatthose dollars being spent,
even if new technology
and advancements are being made,
we can make best use ofthose dollars pre-spent
by an agency and ultimatelythe traveling public
that is funding what is goingout on their road space.
(13:18):
- Yeah, absolutely.
So, I want to be able
to give our listeners a clearer picture
of how this works.
So I'm curious if you haveany real world examples
or customer case studies,
anything that you can share with us
of how ISS came in,
whether you're workingwith a city official,
(13:38):
government official, how you guys came in
and implemented the technology
and what the result of that was.
- Yeah, there's a numberoff the top of my head.
A few are specificallyin tolling agencies,
where if you've ever driven
in heavy populated areas
and been on a toll road,
(14:01):
there are a number of cameras
and just devices that are up
on the gantries alreadyin use by that agency.
Being able to go in andprovide, in this case,
our LPR solution
where we're able to bea part of the totality
of what that governing body may be doing.
So, in that case,
(14:21):
if we are doing license plate capturing,
in some places we're justdoing flow estimation.
So, your speed, volume, gap, occupancy,
different things like that.
Again, the data engineersjust hold dearly,
need constantly in orderto make these decisions
about where they're going
to bring different roads into,
how they're going to reshape
where we might be driving.
(14:43):
So that is one aspect.
I would say the biggest scale
we've done was actually in Mexico City
as a global companyfounded here in the U.S.
But as a global company,
we actually haveimplemented in Mexico City
(15:04):
our SecurOS, which is our operating system
within their entire agency.
And we are that operating system,
the end point for their operators to use
and have somewhere north of 65,000 cameras
along with alarming devices, horns,
(15:25):
and really have taken their smart city,
allowed the interconnection
between all of these physical devices
to live on our network.
And we are able to then leverage,
again, that neural network
to really just open up the possibilities.
If you think about that number of cameras
(15:45):
and the number of personnel
you would have to have in order
to even review or take a look at live,
allowing our system toreally be that point
of the spear for them
and everything else
to just kind of livebehind was transformational
for Mexico City.
So that's our feather in the hat.
That was a very large project for us,
(16:08):
but allows you to kind of understand
the scale to which some
of these very large cities in the world
or here in the U.S. have
and kind of what their need
is when reviewing just inbound video
into their system.
- Those are awesome use cases to hear.
And I imagine with SecurOS, obviously,
(16:31):
intelligent security systems,
security is in your name.
So, you know, when we think of that,
sometimes it's thought about like safety
and surveillance and protecting
what the cameras are capturing,
but also on the back end,
you know, the securitywe're talking about,
collecting license plate data,
collecting videos of drivers.
So, you know, I assume that SecurOS
(16:54):
any other technology
and solutions that you guys have,
privacy and security ofkeeping that data safe
and making sure that, you know,
personal data is protected is something
that you guys are also on top of.
- Absolutely, it is atthe forefront of kind
of the digital age
has been both a privacy aspect,
(17:16):
but then a security aspect.
Yes, as our name implies,
ISS, Intelligent Security Systems,
as you may have mentioned, too,
we had our grounding
in what physical security meant
for real world applications
and have continued to build on that.
(17:36):
From the privacy standpoint,
the industry has taken a branched approach
in what they are looking
at from the standpoint
of what we are actually capturing out
on the roadway where we can blur faces,
blur license plates,
actually have the intelligence
within the cameras understand
(17:57):
and help us remove anyof that personal data.
But from the standpoint
of then what is captured,
working with each agency individually
on what their standards are,
and then from a securitystandpoint here at ISS,
being able to followall the major outlines
for your security, for your privacy
(18:18):
and making sure that any advancement
that we might make,
that is the parallel path
that we are making sure we are following,
because trust
and kind of understanding
from our users isparamount to our success.
(18:40):
It has to be a part of what we do.
And for us here at ISS has been kind
of a driving parallel path
to what we bring to market.
- Absolutely.
And that's great tohear because technology
and all these things,
no matter how big the benefits
that they do bring,
there's always going to be those privacy
or data concerns.
(19:01):
So, it's great to beable to have a solution
that gives you both.
You can take advantageof all these benefits
and ensure that data
and sensitive information is protected.
And I also, since we're talking about AI,
I wanted to ask,
and I should mention insight.tech
and the "insight.tech Talk,"
we are sponsored by Intel,
but I can imagine being able to apply AI
(19:25):
to these different areas.
You need it to be high performance.
You're collecting real time analytics.
So that needs to happen at the edge.
So, I can imagine that you are using
and partnering with Intel in all of this.
So, I'm just curious what the value
of that partnershipand that technology use
from Intel has been for ISS.
- Yes, I would say almost invaluable
(19:46):
when you're going to try to really put
into a box what companies like ISS needs
from a performance standpoint
and the partnership,
we need with a company like Intel.
As advancements continually get made,
(20:06):
more and more
of our end user agencies are asking us
to include different data points
and just push the capabilities
of what the physical hardware
and software are
without a company like Intel understanding
and the forethought they have
of what the marketplace is going to need
and how they can be abenefit to manufacturers
(20:28):
to instantly react, to have solutions,
and truly partner with us
to solve problems
that we are seeing in the real world.
You can't understand
how great of an impact
that has from our standpoint
and ultimately the rest of the industry
that is relying on anIntel to really continue
(20:50):
to push that forward for us.
- Awesome.
Well, I can't wait to see some
of these technologies
and advancements come to my area
and be just more widely adopted
and more spread out, you know,
get to my parents' house a little faster.
(Joe laughing)
But I appreciate this conversation.
It's been very interesting to hear.
Before we go, Joe,
(21:11):
I just wanted to turn it back to you
one last time.
Any final thoughts
or key takeaways you want toleave our listeners with today?
- Really just understanding
what is possible from a Intel,
from a video intelligence company,
and what the traveling motorists
are going to see.
We are trying to solve problems
(21:32):
that are either real time today,
actual events that you are seeing out
on the roadway,
but also looking to partner
and solve problems
that we don't even understand yet.
As that connected realm continues
to kind of build itself,
more and more challenges
will be brought to us
and asked of us to solve.
(21:54):
And we believe here at ISS
that we are up for that challenge.
But we look forward tocontinuing conversations
with all parties to see
how we can leverage the strength
of what we've builthere over 25 plus years
and everybody else that is interconnected
within solving these problems
(22:14):
to transform the traveling public.
So, we look forward to it.
We appreciate being a part of it.
ITS is in our lifeblood here at ISS,
so we're just happy to be a part
and appreciate the time
to just share a littlebit of our knowledge
and the excitement behind these products,
the solutions, and theentire space itself.
- Great.
(22:35):
And I would urge all of our listeners,
like Joe said, see howyou can partner with ISS,
visit the website, haveany real-world problems
you're looking to solve.
We've talked
about intelligenttraffic management today,
but ISS offers many different solutions
across many different industries.
So, you know, have a conversation
with Joe and the team
and see how they can help you out,
solve your problems.
(22:56):
Also, keep up with us on insight.tech
as we continue to cover partners
like ISS in this space.
So, thank you, Joe, again for joining us.
Thank you to our listeners.
Until next time, this hasbeen "insight.tech Talk."
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