View the video presentation and technology demonstrations of the Responsive Passenger Information Systems, which aims to alleviate congestion within Sydney’s rail network by developing a relationship between customer actions and needs in real time, and provisioning services to respond to shifting customer needs.
This innovation is a partnership of Sydney Trains, Rail Manufacturing CRC and UTS.
00:00
good evening everybody before I begin
00:02
the proceeding is not behalf of all
00:03
those present I'd like to acknowledge
00:05
the gadigal people of the eora nation
00:07
I'd also like to pay respect to elders
00:09
both past and present acknowledging them
00:11
as traditional custodians of knowledge
00:13
for this land and that's something we
00:15
say particularly at the University of
00:16
Technology Sydney which is where I'm
00:18
from I'm delighted to see so many people
00:20
here tonight I'd like to give her very
00:21
special welcome to the minister
00:23
the Honorable Andrew constants Minister
00:24
for transport infrastructure howard
00:26
collins CEO of sydney trains tim Rearden
00:29
secretary for transport us at Wales as
00:31
many as well as many of our industry and
00:33
government partners in the UTS community
00:35
for those of you I haven't met I'm Glen
00:38
why I'm the deputy vice-chancellor of
00:39
innovation and enterprise at UTS it's a
00:42
glorious title for actually the last
00:45
three years I've actually been looking
00:46
after our research portfolio at UTS and
00:49
and actually as of last Tuesday I've
00:50
moved into this new portfolio and as
00:52
part of that role I look after our
00:54
innovation and entrepreneurship we do a
00:56
lot of work with students and industry
00:57
but the enterprise part of my work
00:59
effectively takes all of the work we do
01:01
in our research and helps connect it to
01:04
our community to our stakeholders to
01:06
industry and so on we are at UTS we are
01:14
a University of Technology I know that's
01:16
part of our name and we're very
01:18
committed to using technology to improve
01:20
lives whether it's from things like low
01:23
cost easy to operate systems to remove
01:26
arsenic from groundwater in Vietnam
01:28
which is an interesting project we're
01:29
involved in at the moment - the
01:31
technology that we'll be talking about
01:32
here tonight the responsive passenger
01:34
information systems this is all
01:36
characterized were characterised by our
01:38
commitment to collaborating with
01:40
partners to develop new technologies for
01:42
very practical and yet innovative
01:44
applications I won't say too much more
01:46
on there are responsive passenger
01:48
information systems you'll hear more
01:50
from our experts on that but this is a
01:52
really important project will help
01:53
improve customer comfort and overall
01:55
experience at busy stations like when
01:58
your d'Or Town Hall on the Sydney train
02:00
network which would be welcomed by many
02:01
including me I'm a person who loves
02:03
public transport I catch the ferry and
02:05
the train to work each day and I walk
02:07
through the lovely tunnel underneath
02:08
central so the idea of responsive
02:11
passenger information systems is
02:13
very appealing what's made this possible
02:16
that's been the commitment in close
02:17
collaboration between our researchers
02:19
and the industry partners involved
02:20
Sydney trains the rail manufacturing
02:22
Cooperative Research Centre and
02:24
transport for New South Wales I'd like
02:26
to congratulate everyone involved I
02:28
remember a meeting we had with the
02:30
minister
02:31
probably 12 or 18 months ago where we
02:33
kind of threw around some ideas and it's
02:36
been very very exciting to see since
02:38
that point a number of us coming
02:41
together and working on this on this
02:43
technology
02:44
I'd like to as they congratulate
02:46
everybody
02:47
clearly there's unprecedented passenger
02:50
growth I just learned that there are 1.3
02:53
million passenger journeys on Sydney
02:56
rail everyday I think 388 million year
02:58
that's an incredibly large number
02:59
clearly there are people like me who
03:01
have increasing expectations of the
03:04
responsiveness of our of our public
03:07
transport system and clearly there's
03:09
lots of infrastructure constraints as we
03:12
grow Sydney and and demand more
03:15
Transport and rail capacity I believe
03:18
that only by working together can we
03:20
deliver innovative solutions that are
03:22
going to be of benefit to to all of us
03:25
here in in Sydney and then to take those
03:27
solutions and translate them into
03:29
capability working with smaller medium
03:31
enterprises and other organisations
03:33
industry to potentially take them out to
03:35
the world so you've probably heard
03:37
enough from me
03:37
I'd like now to introduce one of the
03:39
people behind this great work at UTS and
03:42
she'll be our host for this evening dr.
03:44
Michels eyebots from the UTS Transport
03:46
Research Centre Michelle is a transport
03:48
planner specializing the analysis of
03:50
sustainable urban passenger transport
03:52
systems in her research at UTS she works
03:56
with our Institute for sustainable
03:57
futures as well as within our Faculty of
03:59
engineering and information technology
04:00
where she lectures and transport
04:02
engineering we're very fortunate to have
04:04
her at UTS she's been absolutely
04:06
integral to this research project and to
04:08
building collaborative research
04:10
partnerships with all of the parties
04:11
involved please join me in welcoming dr.
04:14
Michels eyeballs
04:16
[Applause]
04:20
Thank You Glenn so tonight's very
04:23
exciting for us it's the culmination of
04:25
several months of research that's taken
04:28
place over the last year it's been a
04:31
very big team of researchers at UTSA
04:34
from a number of different faculties and
04:36
many of whom are here tonight what we
04:40
want to do tonight is we want to talk
04:42
about the nature of the the problem that
04:45
we've been charged with trying to solve
04:48
and we also want to talk about the
04:50
technology so as you can see we're all
04:53
set up for that and done hopefully
04:55
that'll be quite exciting so what we're
04:58
going to try to do we had a dress
05:00
rehearsal earlier that went sort of okay
05:02
so hopefully now that we're here and
05:05
doing the real thing and we've got all
05:07
of you here to to watch and participate
05:09
in this in this demonstration hopefully
05:13
we'll it'll go really smoothly but
05:15
before we tell you that story what I'd
05:18
like to do is just give you a little bit
05:21
of background about about the project so
05:25
basically we are looking at the
05:28
development of a new class of passenger
05:31
information system and it uses robotic
05:34
technology so it's only been made
05:36
possible with the new sensor
05:38
technologies and an actuator
05:41
technologies that are around at the
05:44
moment that weren't available 10 years
05:46
ago and responsive passenger information
05:50
systems they differ very much to the
05:51
earlier forms of passenger information
05:53
system technology which we call static
05:56
and dynamic so statics are basically
05:58
just the signs that we see on railway
06:00
stations and throughout the railway
06:02
environment and dynamic signs are those
06:05
signs that change so we often see the
06:08
the the dynamic passenger information
06:11
systems on platforms where you can see
06:14
things changing and the times are being
06:15
updated and what-have-you so what we're
06:17
proposing here goes goes far beyond that
06:21
and I guess it's also a form of
06:24
technology that we hope will demonstrate
06:26
tonight that utilizes our very
06:28
new and different approach a very new
06:30
philosophy to technology and we're not
06:33
just seeing it happen in transport we're
06:35
seeing it in a number of different areas
06:37
you'll often hear the term human in the
06:40
loop or human centered design and what
06:43
that basically means is that designers
06:46
engineers technologists are now looking
06:48
at ways in which to look at the the
06:51
natural instincts the the predilections
06:53
the needs of users of a system and
06:56
actually integrating that into the
06:59
structure and function of the system in
07:02
order to make the system work even
07:04
better than what it than what it
07:06
otherwise would and this is a very big
07:08
step away from old approaches to
07:10
technology that involve what what are
07:12
often called command and control
07:14
approaches where you've got actors or
07:17
operators directing everybody but not
07:20
necessarily or really considering what
07:23
it is that users users are after and
07:25
what what they might want so this is all
07:28
very compatible with the customer
07:30
service philosophy that we've seen
07:32
blossom here in Sydney recently networks
07:35
like the Sydney trains Network are very
07:37
different now to what they they were and
07:39
how I remember them only just a few
07:42
years ago so it's been wonderful to see
07:45
that change and we're hoping as UTS
07:49
researchers that we'll be able to
07:50
contribute to that ongoing change and
07:53
improvement so okay back to our story
07:57
we're drunk going to try and tell it as
07:59
a story in order to make it more
08:01
interesting so I remember an engineer
08:03
once saying to me you know Michelle
08:05
people respond to stories they don't
08:07
respond to bullet points so here we go
08:10
let's let's hold on tight and see what
08:13
happens all right so every new
08:15
technology needs a problem so what I'd
08:18
like to do in order to get us on our way
08:20
with this story is to ask Tony ade to
08:23
come up and join us
08:24
so Tony is the executive director of the
08:28
future network delivery Directorate and
08:30
he's responsible for planning our future
08:33
network the stuff exactly so I didn't
08:39
know where the clicker
08:41
okay all right so to get us going so
08:45
Glenn's mentioned it and it's been
08:48
through all the papers lots of people
08:50
are talking about these incredible
08:51
growth rates that we're seeing on the
08:53
Sydney trains Network can you give us
08:55
some background on that what's been
08:57
happening just right it's run through it
08:58
well certainly there's no doubt about it
09:00
there's been unprecedented growth on our
09:03
network in fact it's so busy out there
09:05
it's it's really becoming something we
09:08
need to address very quickly if I draw
09:10
your attention to that graph up there
09:11
and you can see from 1990 there's been a
09:13
steady growth right through till about
09:15
today but when we get further on into
09:18
the future and we predict the future
09:19
it's absolutely growing in a very rapid
09:22
pace and it means we have to do
09:24
something differently to address that in
09:26
the immediate future right now here and
09:28
now with more trains more services and
09:30
obviously we have to think carefully
09:32
but the innovative in smart ways of
09:34
tackling the future is it just the
09:36
morning and afternoon hope that we're
09:38
seeing this growth in or is there
09:39
something no not at all like you know if
09:42
he if you guys are on the trains all the
09:43
time you can now see that it's not
09:45
typically just a name and PM peak our
09:48
trains are fall all the time not just
09:50
any time and if you take traditionally
09:53
an aim and P and peak we don't we're not
09:55
a name picked railway anymore we've in
09:57
the off-peak between Peaks and on
09:58
weekends it's quite extraordinary and if
10:00
you have a look at that slide but it's
10:02
got 2021 on weekdays by 2021 we're gonna
10:06
have a 20% growth on weekdays but take a
10:09
look at the weekend a hundred and twenty
10:12
percent and for those who don't know we
10:14
carry about a half a million people on a
10:16
weekend just imagine 1.3 million people
10:20
travelling on our network on a weekend
10:22
and that's a really good story yeah so
10:24
what what what is causing this cuz it's
10:26
it does seem to have happened quite
10:28
suddenly well it hasn't really been
10:31
suddenly don't take this station here
10:33
Town Hall it's been happening for a
10:36
while okay but take Town Hall and have a
10:38
look at that station in the a.m. P it's
10:40
full of people and we'll talk about that
10:42
a bit later on but when you look at that
10:45
and you look at things that we've been
10:47
doing as Sydney trains over the years
10:49
you know we've introduced Opel we're
10:51
much cleaner we're lots
10:53
customers in our staff of law interact
10:55
in staff very visible look at their
10:58
stations and there are corridors and you
11:00
see development everywhere so people are
11:02
attractive to the set to the network and
11:04
I think the biggest success for us more
11:07
so than just our brand is the fact that
11:08
we've built something here that's quite
11:10
unique we have been able to demonstrate
11:13
to the community and our customers that
11:15
we care and we move them they trust us
11:18
to get about their day and I think
11:19
that's a very big deal for us yeah ok so
11:22
to the project now so in the very early
11:25
days when we started you gave us the
11:28
direction you said look I want you to
11:30
focus on Town Hall station what could be
11:32
done there with the technology and also
11:34
at Parramatta station so just give us a
11:37
bit more background on why you why you
11:38
asked us to focus on that well there's
11:40
three key reasons why we did that let's
11:42
start with Town Hall Town Hall is a busy
11:44
station very unique 250 people interact
11:49
with that station every day that's a
11:51
large number in any shape or form the
11:53
staff at Town Hall have to move that
11:56
type of number on and off trains and
11:58
they do a fantastic job every day doing
12:00
it but just imagine that train that's
12:02
just on that picture they're arriving on
12:04
that train is about 1,200 to 1,500
12:06
people about a thousand of those people
12:09
are going to get off onto that platform
12:10
we're on that platform there's seven
12:13
hundred plus people to get on and how do
12:15
you do that and manage that with the
12:17
train every three minutes coming in and
12:19
out with the same scenario and hold that
12:21
train for no more than 60 seconds so you
12:23
keep the railway running it's quite
12:25
remarkable
12:26
so Town Hall is one of the reasons
12:28
because if there's anything we can do to
12:29
make the innovation and the operation a
12:32
lot more seamlessly over there and
12:34
predict something like that wasn't
12:35
really much so help us then we talk
12:38
about Parramatta in Parramatta is the
12:39
other location risks and phenomenally
12:42
starting to grow to the point where it's
12:44
a city in its own right very soon
12:46
there'll be 20 trains per hour running
12:47
through that place curved platforms same
12:50
scenarios and what we found ourselves in
12:52
a position is that we may have a new
12:54
Town Hall on the hands so we have to
12:56
bring that into play so I guess for - is
12:59
that by bringing incorporating - very
13:01
busy places on our network the west part
13:03
of Sydney and the
13:04
every day anything that we come up with
13:06
here we can get immediate benefit and
13:08
that's the Russian help me I live in
13:09
those stations so I'm hearing you on
13:12
that but I have to say having worked on
13:14
the project now Town Hall is I I mean I
13:17
was quite overwhelmed by what it is that
13:19
Sydney trains does every day in order to
13:21
keep that operation going
13:23
I've got all the respect for the staff
13:24
it's taken a long time for those guys to
13:26
get it to a position where they move in
13:29
seventy plus thousand people in two
13:30
peaks getting them through there
13:31
seamlessly very safely and in the third
13:34
and final thing is that map that you see
13:36
up there we've got quite a few trains in
13:39
the morning peak making their way into
13:40
the city carrying thousands of people
13:43
and when you think about it for one hour
13:45
there's a hundred and twenty trains
13:46
merging from 15 lines curving their way
13:49
to six lines to make their way through
13:51
and every one of those trains will make
13:54
its way to Town Hall some shape or form
13:58
250,000 people touch that place every
14:00
day and we really need to respect that
14:01
as they work through this okay so with
14:04
them I mean I'm also very mindful that
14:06
we've got a new timetable
14:08
just about to be introduced so with that
14:11
new timetable how does that impact on
14:13
Town Hall ah be facts beautifully let's
14:17
start let's start let's start very
14:19
simply it's trains are gonna go through
14:21
there very frequently and you could
14:23
argue that if you've got a train
14:24
traveling through there on time very
14:26
frequently and more rather than coming
14:29
through with more choice of where to go
14:30
you will move the people a lot easier
14:32
our customers a lot easier but but to do
14:34
that takes extraordinary effort by the
14:36
station staff to train crew and all the
14:38
operators to make that work so you still
14:40
need to do more but when we talk about
14:42
the timetable we talk about the biggest
14:44
permanent uplift of a timetable ever
14:47
brought on this network and and look
14:49
I've been here a long time and this is
14:50
the biggest thing that we've ever done
14:51
in terms of preparing for a timetable
14:54
1,500 extra trains per week that is
14:57
quite extraordinary have a look at the
14:59
weekend to address that growth 750 extra
15:03
weekend trains will now appear that
15:05
doubles all the services everywhere
15:08
including the airport line where it's
15:09
much needed and between the AM and PM
15:11
peak we're pushing that to 250 extra
15:14
trains in that period
15:15
and that's a challenge for everybody but
15:17
it really starts to address the the
15:19
crowding issues that we have on the
15:20
network and it starts to produce us as a
15:23
very effective rolling so so you are
15:25
going to try and push more train paths
15:27
through Town Hall during the peak
15:29
periods with this new time travel not
15:32
quite right okay
15:33
I probably have to take a step back
15:35
which have put more trains through there
15:37
we need more singling systems and we
15:39
need new and innovative technologies to
15:41
help do that we are at track capacity
15:44
and we need to do more if we're looking
15:45
at that projected number into the future
15:47
and we are investing in that space and
15:50
in fact when we move to new signalling
15:52
and the technologies that we're talking
15:53
about here today and the demonstration
15:55
will will help I think people understand
15:57
the effort going into managing Town Hall
15:59
but we also don't see relief for quite
16:01
some time
16:02
the new set that the minister and the
16:06
government of the day has invested
16:07
heavily in Sydney Metro by 2019 we have
16:11
the first cut but it won't be until 2024
16:14
when that train that Metro makes its way
16:16
under the harbour and through Central
16:18
until we get some form of relief for
16:20
that area when that happens and we
16:22
introduce new signalling system we will
16:24
be able to run more but at until that
16:26
time until 2024 we have to think of a
16:28
lot smarter ways of doing it so we've
16:30
still got this problem at the at the
16:32
core at the center at the network centre
16:34
that we have to deal with okay so more
16:40
growth going back to what you were
16:41
originally saying so that the the
16:43
further growth it does need more train
16:45
pads so we've still got we've got some
16:48
time to wait for that there is new
16:50
technologies on the horizon but we've
16:52
still got to just we've just got to do
16:54
something in between and that's what
16:56
we're here today I mean this is a good
16:58
example of what we can do to support the
17:00
staff give them the tools let them know
17:02
some behind the scenes activity that
17:04
will help nudge the customers to the
17:07
areas they need to go so that we can
17:08
actually load those trying to get them
17:10
out much quicker and you never know if
17:12
we get that right in sequence a try it
17:14
might actually stimulate another train
17:16
path I guess we won't know until we test
17:18
it okay so it's so it's sort of about
17:19
stabilizing the system as much as we
17:22
care as
17:22
trying to expand it that's great okay so
17:24
train paths are really valuable here
17:26
they are they are all right so hold that
17:28
thought
17:29
and if you'd like to just wait here or
17:32
there what well the growing there could
17:34
be a growing crowd here ok no I'll wait
17:36
over here outside there's the Train the
17:38
body right here all right
17:40
do you want to give me the clicker oh
17:44
they took the clicker off me earlier
17:46
already so what I'd like to do now is
17:49
I'd like to invite professor John Rose
17:51
to join us so professor professor John
17:54
Rose is the director of the the Center
17:58
for business intelligence and data
17:59
analytics at UTSA and John is an
18:02
economist so quick question how much is
18:06
a train path worth so the simple answer
18:09
to that is about 50 to 60 million
18:10
dollars annually mm-hmm okay and how did
18:15
you get that figure so a bunch of
18:18
calculations of course being an
18:20
economist a bunch of assumptions so if
18:23
you assume that each traditional train
18:25
can carry say 1200 people and that
18:30
people will also be you'll have
18:31
additional trains on the shoulders of
18:33
the peak you can generate about 2,000
18:36
jobs from an additional additional train
18:39
path then based on that most of the jobs
18:43
that were likely to be created in the
18:45
CBD from that additional train path
18:47
they're going to be higher value type
18:49
jobs so you could occur you can say that
18:52
that's basically worth 37 million
18:53
dollars add on additional tourism and
18:57
movement of lower skilled jobs and lower
18:59
value jobs to suburban centers and you
19:02
get about 50 million dollars okay
19:04
so that's quite a lot I'm impressed by
19:06
that so even if we were conservative and
19:09
we took it down to say 40 million that's
19:11
still quite a lot so just a trained par
19:13
from our existing net worth trying to
19:15
squeeze that out of it it's worth it's
19:17
worth quite a bit so and a lot of that
19:19
is because of the the particular role of
19:23
the CBD isn't it so could you tell us a
19:25
little bit about that yes so stephenie's
19:27
are really important to two cities like
19:29
Sydney because that's where most of the
19:32
high value jobs are actually created and
19:34
actually
19:35
exist so the jobs that are basically
19:37
designed to service international trade
19:40
global trade that's not to take away
19:44
from suburban centers but the more
19:46
suburban centers are basically serviced
19:49
the local residential areas and the
19:53
workforce that is needed for the CBD
19:55
centers so the CBD is the really big
19:57
income generator for the the city as a
19:59
whole that's why we see these bigger
20:01
numbers here and it shows how
20:03
structurally it's important so the UM
20:06
Tony mentioned Parramatta CBD as well
20:09
and there's also been a lot of
20:11
discussion not just recently but for a
20:13
long time about paramater is the second
20:15
CBD so it does that somehow conflict
20:19
with the idea of trying to get more
20:20
trains through the Sydney CBD or is
20:23
what's going on there do you think so I
20:25
don't think it actually conflicts with
20:27
what I'm saying I think that we will get
20:30
to the point where somewhere like
20:31
Parramatta could be a second CBD I think
20:34
at the moment though the problem is
20:36
capacity so basically the public network
20:41
public transport network is designed to
20:43
get people into the city from all across
20:45
the Sydney region so once we can
20:48
actually build a network and get people
20:50
to be able to travel to Parramatta from
20:53
all over Sydney I think you'll see some
20:55
shifts from the city towards that but at
20:58
the moment we just have these
20:59
constraints that I think preventing that
21:01
from actually happening so it's really
21:03
the the important thing for firms and
21:05
these global businesses is that they're
21:06
able to access the entire metropolitan
21:08
workforce and not just a local area
21:10
which is why maybe Parramatta has
21:13
struggled to take on that same that same
21:15
role and I think we'll get there in the
21:16
end but I just don't think we're there
21:18
right this moment okay all right so from
21:22
a people perspective that's really
21:26
interesting
21:28
so what I think we might do now is ask
21:33
Susanna LeBron to join us thank you so
21:36
Susanna is the executive director of the
21:39
customer service director at Sydney
21:41
trains and and Suzanne is responsible
21:43
for representing the customer inside
21:46
Sydney trains and lifting the value
21:48
proposition so Susanna Taney was talking
21:52
a lot about the growth and what's been
21:54
responsible for that and he also
21:55
mentioned very briefly that the the
21:57
quality of the services or what people
21:59
are experiencing is just different now
22:01
and it's made it more attractive so what
22:03
sorts of things have been happening and
22:05
I've just remembered to give you the
22:06
clicker that's right so what is really
22:10
important to remember is that in 2013 we
22:14
took on a really ambitious program of
22:17
uplifting our customer service across
22:19
Sydney trains but transport at large and
22:22
what I'm really pleased to say is that
22:25
we have sat at 90% now for a strong 18
22:29
months but we did start that journey at
22:31
78% what I want to show you next is are
22:35
the nine key drivers that we focus on
22:38
which our customers naturally when you
22:40
look at them they're quite simple but
22:42
these are the areas that our customers
22:44
consistently remind us that we need to
22:46
focus on I'm just going to talk about a
22:48
couple straight-up machine so first of
22:52
all ticketing we have had one of the
22:55
biggest changes in the ticketing product
22:57
that you would have seen you know across
22:59
the whole of I suppose Australia and it
23:01
has been a resounding success
23:03
so the Opel option the Opel product 13
23:07
million people per week use that to
23:09
travel so 30 million travel on on Opel
23:13
half of those are on rail so naturally
23:17
we need to pay attention to looking
23:19
after those customers that are
23:20
travelling on us with the Opel product
23:22
the other thing that's really important
23:24
for our customers is timeliness so what
23:26
does that mean on-time performance our
23:28
customers really need to know and need
23:31
to know that we've got a reliable
23:32
service again pleased to say that in the
23:35
last financial year ninety three point
23:37
four percent on-time performance which I
23:39
think any of you that travel regularly
23:41
know that you can pretty
23:42
much turn up and nearly go in most of
23:44
our stations and great with the more
23:46
trains more services that's going to be
23:48
even more enhanced one of the other
23:51
things that I think it's important to
23:52
call out because I think if you go to a
23:53
barbecue now people often say oh the
23:56
trains are so clean the station's are so
23:58
clean that's the reality huge investment
24:01
has occurred not only in our station
24:03
environment but with our trains as well
24:06
and the actual benefit of cleanliness
24:08
also means that our customers feel safe
24:11
and secure so through that investment we
24:14
also have customers that are now really
24:16
pleased to be at stations at all times
24:19
of the day even through the night
24:20
because of the way it's been maintained
24:23
and cleaned okay so can I just interrupt
24:25
you there a little bit so on all of
24:29
these areas not just the the ones that
24:31
you've covered there so the customer
24:34
satisfaction survey result is what's
24:36
been driving and guiding a lot of what
24:38
you're doing
24:39
so you're scoring on ninety ninety
24:41
percent are you are you looking to just
24:43
stay there or what tell us what well I'm
24:46
not going to do I'm not gonna do crazy
24:47
straight and say yet we're at ninety
24:49
percent and we'll just leave it there
24:51
the reality is to maintain such a high
24:53
score and any major organization that
24:56
has customer services its key
24:57
deliverable needs to consistently look
25:00
at how they need to improve so to
25:02
maintain we need to keep improving one
25:05
of the other two is that's really
25:06
important and actually leads into why
25:08
this UTS partnership has really started
25:11
to bring around success for us is around
25:14
customer service so if you get customer
25:17
service right which we clearly are doing
25:19
really well with that the model of out
25:21
and about has only been really supported
25:24
through technology it's enabled our
25:26
people at stations to be responsive and
25:29
dynamic and have real-time information
25:31
through the technology that we now have
25:33
the other thing that leads into that is
25:35
information information is critical and
25:39
if we have information that is proactive
25:41
and available not just for us but for
25:44
our customers is actually the game
25:46
changer to match what Tony is absolutely
25:49
articulated as a growing patronage for
25:52
us so
25:53
you need to you need to find new ways of
25:56
doing things in order to just to stay
25:58
let's click on it's just the clicker all
26:01
right right we didn't really cause that
26:03
but but this is this is the clincher
26:06
page okay this is the one so we've had
26:09
some great workshops with UTSA and the
26:13
reality is that not only have we had a
26:15
fantastic partnership and we are
26:18
benefiting and you know transport at
26:20
large is benefiting but our customers
26:23
are now going to benefit from the
26:24
partnership that we've now got through
26:26
UTSA the key on this diagram is to
26:29
actually look at the fact that we've got
26:30
arrows going in both ways we know that
26:33
with the growth and with the success of
26:35
the service that we now offer we
26:38
actually need our customers to help us
26:40
out because as you can see with Town
26:42
Hall if we just rely on us as operators
26:46
to gather information and work with that
26:49
information we may limit our success on
26:52
how we look after these customers in
26:53
their journey but if we actually get our
26:56
customers and we've had some fantastic
26:58
our apps already today but if we
27:01
actually had our customers to be part of
27:03
this and this is where you heard the
27:04
words bye-bye Tony's saying nudging and
27:07
behind the scenes technology it means
27:09
that we really will fulfill all the
27:12
information that we need to actually
27:13
look after our customers and have them
27:15
part of the operational journey so if
27:17
you've got new ways to think about all
27:20
of this information and engage with your
27:22
customers then you're able to to get
27:25
that relationship going in more of a
27:27
two-way thing cuz that this is this is
27:29
what's exciting was exciting for us as
27:31
researchers is this human-in-the-loop I
27:33
think so people have got these wonderful
27:36
instincts and desires like they want to
27:39
be more comfortable they want to have a
27:40
better experience so it's it's just
27:43
trying to find the ways to harness that
27:45
um you're probably are thinking okay
27:48
seriously what is it that we're gonna be
27:50
shown tonight but this is laying the
27:52
foundation for this somehow we need to
27:55
get information that is proactive and
27:58
real beyond what we have currently which
28:01
is our people on stations position yes
28:04
they've got the bright orange on but
28:06
visually they
28:07
can only see so much to be able to get
28:10
technology and the ability to understand
28:12
what our customers are doing on a
28:13
regular basis means that we can be
28:15
proactive in addressing what we know is
28:18
about to come around the corner which is
28:20
consistently more requirements of our
28:23
public transport system so if we've got
28:25
those that information going to people
28:27
and they're getting more stuff on their
28:29
mobile phones and their staff not the
28:33
staff people the Sydney trains people
28:35
they're also getting more information
28:36
about what's going on then we're able to
28:40
get that more symbiotic relationship
28:42
going so a lot of you that travel on our
28:45
on our transport network at large would
28:48
be familiar with a multitude of apps and
28:51
information that you can access we are
28:54
consistently growing that technology to
28:56
meet the demands working very closely
28:59
with the department headed up by Toni
29:01
Braxton Smith in transport around all
29:04
the apps and the ability to understand
29:06
intermode connections of our customers
29:08
so for us we can't just stop here we
29:11
need to understand behind the scenes
29:13
what else can we feed into these apps
29:14
into this technology to help not only
29:16
our people in the operator environment
29:18
but our customers understand what is it
29:21
that they need to do to actually get
29:23
their experience better ok so just to
29:26
finish off what's your vision how do you
29:28
want to use all this tech what do you
29:30
want people to fasten aspirationally and
29:32
I'd like to think that I can drop that
29:34
word and we'll eventually make this
29:35
happen I would love for someone to get
29:38
to their destination whether it's
29:40
sitting at an office or sitting at the
29:42
movie cinema and actually just think for
29:45
a minute how on earth did I get here I
29:48
caught a bus and then I caught a train
29:50
but I glided through those experiences
29:53
because I had information at hand that
29:56
enabled me to make choices and wise
29:58
choices but I also had people in the
30:01
operational environment nudging me and
30:04
getting me around certain obstacles that
30:07
when I got to my destination it was so
30:09
seamless they're really the recollection
30:11
is that they just glided through so
30:13
that's that's what I'd like to think
30:15
we're done yeah all right thanks for
30:17
that now there's some other people that
30:19
have had vision
30:20
in the past there have been very
30:22
critical to this story oh thank you so
30:27
I'd now like to ask the minister the
30:29
Honorable Andrew Constance to to join us
30:32
up here so Andrew of course is the
30:35
Minister for transport and
30:36
infrastructure and he's actually played
30:39
quite an important role in this project
30:41
you might not think so but you have
30:42
because I think I think if you hadn't
30:46
told us to go and do it it wouldn't have
30:48
happened so really that's that's what
30:50
did occurred but look there's a question
30:53
that I've been dying to ask you for a
30:54
long time I've never had the opportunity
30:56
to do it because I was really impressed
30:58
and taken as a transport planner with
31:00
the whole customer service thing that
31:02
glad is particular and introduced and I
31:04
thought frankly it was going to be very
31:06
difficult for you to top that but don't
31:08
television I know it's pretty good no
31:13
it's pretty good but the whole
31:16
technology thing in the technology focus
31:18
that you've really given the portfolio
31:20
has been I think incredibly significant
31:23
and I just want how did you know how did
31:27
you know that it was going to pan out to
31:29
be as significant as what it has been
31:32
well I think there's a couple things
31:34
first of all and transports a technology
31:38
business and it will grow so rapidly and
31:42
so quickly so as we progress through a
31:44
world where you know people basically as
31:47
Suzanne I said you know just to be it to
31:50
be completely seamless so the impacts
31:52
that artificial intelligence will have
31:54
the impacts in which certainly we're
31:57
already seeing in terms of utilization
31:59
of the data behind Opel ultimately
32:03
people want a personalized service
32:06
despite it being a public transport
32:08
system that we're talking about and and
32:11
ultimately they want to be able to use
32:13
technology in a way where it's pretty
32:15
much the speed of thought so
32:16
we we're going to see I think
32:18
increasingly and we've seen people
32:20
obviously take-up yes we we open the
32:22
data to allow those apps to be developed
32:23
this is much bigger than this and I
32:26
think we've got a tired old Network and
32:28
we're trying to utilize technology here
32:30
to make it work more effective
32:33
and and ultimately as we see the advent
32:36
of for instance autonomous vehicles
32:38
we've also got a little ways in which we
32:40
can apply some of those principles to
32:42
the way in which people move in and
32:43
around this tiedoll network so I think
32:46
that's shot of Town Hall where it's
32:48
crowded between the stairs and and that
32:52
magical yellow line again we can't widen
32:56
the platform so the question then
32:59
becomes well how do we position people
33:00
to get on to a train more effectively I
33:03
mean ultimately because of the way that
33:06
behaviors have been embedded into us you
33:10
know we want to speed up the way in
33:12
which people get on and off the trains
33:14
as Tony alluded to but we also of course
33:16
want to get the customer satisfaction
33:18
high I say you know it wouldn't matter
33:22
if if your your mind cities will I need
33:24
to jump on carriage number two because I
33:26
know that if I do that I can get up the
33:28
escalators at Martin place more quickly
33:30
to get to Parliament House why you'd
33:32
want to do that the first place I don't
33:33
know a bit but just as a principle
33:35
because I do it but but the point is in
33:40
many ways we're setting out ways and
33:42
this is our gonna take it to a different
33:43
sphere of thinking okay so when when
33:48
when we first came to see you because I
33:49
came to see you when we were launching
33:51
the UTS Transport Research Center and I
33:54
thought well we should actually tell you
33:56
what we're doing so you know what we're
33:58
launching and I have to say I was a bit
34:00
surprised at you know the the
34:02
supportiveness that you had for the
34:03
technology that that we were showing you
34:05
so okay can I ask just quickly what what
34:08
was it that that picture interest or or
34:10
what when we were showing you I think it
34:12
was some of the sensor technology work
34:15
that the guys at the Center for
34:16
autonomous systems have done with the 3d
34:18
sensing that we're going to see so what
34:21
was there anything that sort of struck
34:23
you about that I knew a technology sort
34:25
of person yourself like do you did some
34:28
consulting work but I was doing
34:31
consulting away from Microsoft Asia
34:33
alright so yeah I mean look I think that
34:36
the and it was in the sort of government
34:38
relations corporate space but I think
34:42
that the thing about all of these
34:43
is that we can do so much better and the
34:48
technologies are there but it's a
34:50
question now of how we take it to a
34:52
different level and some of the
34:53
technologies will advanced very quickly
34:55
you know people don't want timetables I
34:59
mean if you were running a service every
35:01
three minutes and the peak there's no
35:02
need for a timetable and so with as of
35:07
this weekend we'll be making some
35:08
announcements about those timetables but
35:10
the the key point is I mean around 70%
35:13
of the train network is going to have a
35:15
train arrive at a station anywhere from
35:17
an average 3 to 15 minutes max so where
35:22
we're moving away so it becomes a turn
35:24
up and go system the Metro certainly is
35:27
but you know it's a system which will we
35:29
could almost deliver a train every two
35:30
minutes similar to what's being
35:32
delivered in parts of Asia now so we we
35:35
want to change the behaviors but if
35:36
we're able to use the technologies to be
35:38
able to do it it does mean some of the
35:40
political sensitivities that you know
35:42
have dogged particularly politicians in
35:45
the past about crowded services and you
35:48
know trains it alight and buses it'll a
35:50
you know we we move beyond so that's
35:54
where the technology is taking us and
35:55
it's it's a good political outcome as
35:57
far as I'm concerned I remember I
36:01
remember talking to quite specifically
36:03
about interchanging and from a
36:05
professional perspective we we've often
36:07
thought that people on Sydney's train
36:09
network don't really interchange very
36:11
much and and I guess it's that's related
36:15
to the the unprecedented growth levels
36:17
that we're seeing as well so what what's
36:20
your take on the on the growth levels
36:22
like it what what do you think that that
36:24
means for the future yeah I mean
36:26
everyone's talking about over
36:27
development at around the city at the
36:28
moment people certainly don't want
36:32
growth if they can get around I think
36:34
that's fair to say but the the
36:37
interesting thing for us is we inserted
36:39
a a transfer discount into the Opel
36:42
system last year 12 months ago and I
36:46
think at that time it's about you know
36:47
30 percent of commuters would
36:50
interchange at least once a month so
36:52
they either get from a bus to a train or
36:54
vice versa
36:56
as of yesterday we're now at 50% on a
37:00
regular basis cut communities are
37:02
starting to interchange so there I had
37:05
to I had this problem where our revenue
37:08
growth was going that was going down a
37:11
patronage growth going the other way
37:13
that's unsustainable and what we found
37:16
was that the eight trips and then the
37:18
rest of the week free was causing that
37:22
to occur I mean we need money to run the
37:24
system so we had to make a decision as
37:25
to well how do we deal with this
37:27
interestingly by putting the $2 transfer
37:29
discount in it has started to change the
37:33
transport planning and we had some
37:35
ridiculous anomalies in the network
37:37
where for instance of you know each
37:39
cliff to the 10 to the city we wouldn't
37:41
have people get to educate for one bus
37:43
and then jump on the train they wanted
37:45
to stay or interchange to another bus so
37:47
that they didn't have to pay twice so
37:49
just doing some of those sensible things
37:51
so we had a quite literally a bus
37:53
running the exact same route as a train
37:54
route on an underutilized line so just
37:58
changing some of those things is really
37:59
important the other thing is is that
38:01
what we are now experiencing is because
38:04
of Opel that the data is far more
38:06
accurate people aren't standing there
38:07
with clipboards from work out passenger
38:09
movements across the network so the
38:11
interchange points are also going to
38:13
start to change we've just separated the
38:15
T 1 and the T 2 line out so that again
38:18
we can deliver trains more effectively
38:21
in from Parramatta and to the people for
38:23
the Greater Western Sydney into into the
38:25
town come the year's end and we so
38:27
that's where the 1,500 additional
38:31
service uplift is coming from by just
38:32
reconfiguring some of those lines and
38:35
those interchange points so
38:36
traditionally there's been about 20 odd
38:38
interchange points across the network
38:40
that will grow the customer experience
38:42
knows interchanges very much as one
38:44
about how people can get through many
38:46
changes more quickly if we can improve
38:47
the customer experience better through
38:49
retail and other avenues so that they
38:52
become destination points central is a
38:55
classic example where there's enormous
38:56
opportunity there
38:59
and we are going to go to market
39:00
eventually in terms of what we can do to
39:02
redevelop an uplift central in the say
39:05
the same way we have seen some of the
39:08
the train stations around the world some
39:09
of those ground stations around the
39:11
world so it's going to be a very
39:12
interesting time as we we move through
39:15
through the technological change but
39:16
also the way in which we do run the
39:18
network okay so how about we have a look
39:21
at some of that technology now so I hope
39:24
you might still might stay here and help
39:26
us out and I'm also hoping that um Tony
39:31
and others come on come on up guys and
39:33
so we often talk about sensing in
39:35
perception and that's like the eyes of
39:37
the system that's where we're taking
39:38
data in the perception algorithms turn
39:41
it into useful information and that then
39:44
goes to another part of the system which
39:46
is what we call the cognition system
39:48
which is like the brain it thinks about
39:50
what's going on given the data it's got
39:52
and then it decides or make some
39:54
decisions about what can be done to
39:57
improve the system and that then is sent
40:00
out to what we call actuation pieces so
40:02
the actuation pieces are the bits that
40:04
actually do something they change
40:07
people's or they change people's
40:11
behavior they influence people's
40:12
behavior potentially by giving them more
40:15
options and in our in the case um in
40:17
this case what we're wanting to do is to
40:19
do that in a way that actually leverages
40:24
the the natural sort of desires and
40:27
instincts of people something else that
40:30
developed during the course of the
40:32
project was that we talked about micro
40:34
systems or the micro system environment
40:36
and the macro system environment and
40:37
you'll hear that a bit in the discussion
40:40
later on but basically what that means
40:41
is that the micro system is like the
40:44
platform in the concourse area or inside
40:46
the sydney trains network and the macro
40:50
system is outside and it's the the
40:52
broader urban environment in which
40:54
people are coming from and going to and
40:56
the reason why that significant is
40:58
because the sorts of information that
41:00
people need changes depending on whether
41:02
they're in the micro system or the macro
41:05
system so in the micro system they've
41:07
made the decision to travel what they
41:09
need is information about the the
41:11
details of what's going on in order to
41:13
make their trip more comfortable and the
41:15
macro system people can change there too
41:19
Asians quite quite considerably they
41:21
might change there at the time of
41:22
journey they might even decide not to
41:25
travel at all so there's lots of
41:27
opportunities for ways to influence the
41:30
system last one here a lot of the tech
41:34
that you're going to see tonight is a
41:37
bit rough around the edges and some of
41:40
it is a bit more sophisticated and it's
41:42
been a bit more polished so when when
41:45
people are developing new technologies
41:48
they go through a set of development
41:50
stages so there's the early discovery
41:51
period where you get planners like me
41:53
who are very broad in conceptual do a
41:55
lot of workshops think about the problem
41:57
and frame it then we hand over usually
42:00
to engineers who start working it up
42:02
into technologies and this is where
42:04
there is more cogent see around the
42:06
ideas and we then start seeing proof of
42:08
concept which is still a bit rough
42:09
around the edges
42:10
pilot prototyping in operations
42:12
evaluations so universities usually do a
42:15
lot of research in that area
42:17
and then we hand it over to a very
42:19
different kind of engineer that then
42:22
polishes it and turns it into something
42:24
that is going to be more like a finished
42:26
project product that can be implemented
42:28
in an actual station so UTS were
42:30
incredibly lucky that we have a unit
42:33
called rapido they're very new and what
42:36
rapido does is it takes the research
42:38
from unruly academics like myself and
42:41
and others and then polishes it and
42:43
turns it into something that is more
42:45
like a finished product so we're able to
42:47
work with industry right along the
42:49
entire development chain when we're
42:52
developing new technologies and and the
42:54
like so with those with that in mind
42:58
what I'd like to do now is invite Alan
43:01
Olimpia vich to to join us so Alan is
43:06
from the Centre for autonomous systems
43:08
and Alan's a specialist in I think it's
43:13
the other one we want to go to Alan's
43:15
our specialist in robotic sensing oh and
43:18
we're seeing some of it now ok so Alan
43:21
can you just talk us through what we're
43:23
seeing so we've got these this group of
43:25
people here now
43:28
we're crowding thank you all right all
43:34
right so we definitely gone beyond
43:36
clipboards we have a 3d sensor we have a
43:45
3d sensor that's actually mounted on
43:46
that pole there which is close to the
43:49
commercial ready type and it's capturing
43:53
a scene which is in front of these train
43:54
doors customers moving about and some of
43:58
the data obviously the sensor itself we
44:01
have developed the possession algorithms
44:02
that are tailored to extract data that
44:04
can be of use for rail service operators
44:08
some of the data is actually portrayed
44:10
here on the screen above we have the
44:14
people appearing in white there like
44:15
white silhouettes on the scene and below
44:18
them are tracks so if you to move if you
44:21
were to move about the there's a track
44:24
history that kind of goes and follows
44:25
you behind behind you so the type of
44:28
information that we can get from this is
44:32
is the amount of passengers that are
44:35
present we can get also the amount of
44:37
people boarding and alighting in time we
44:40
can look at crowding we know someone's
44:43
interfering with the door and we can
44:44
also extract when the doors have opened
44:45
then doors have closed and all the
44:47
associative times around that okay also
44:50
if passengers are just standing at the
44:51
door naturally boarding that can be also
44:53
identified so usually when a train pulls
44:56
into a station most passengers on a Town
45:00
Hall I've noticed are usually standing
45:02
to either side of the the doors aren't
45:04
they okay so sometimes we have Stuart
45:10
Warren from repeat I said he will turn
45:12
kind of the viewer round right okay so
45:14
so we can see who's quite John who's
45:17
there now closest and in in the middle
45:19
of the doors so as a as a rail operator
45:23
you you could get this information about
45:24
whether someone's obstructing the doors
45:26
where people be Jupiter around it and if
45:28
you want to implement a strategy to
45:30
alter how people move about you can have
45:32
a ground truth like you can actually
45:35
compare whether there's been any
45:36
difference in what you've done
45:38
because what I find really interesting
45:40
about this is that you are able to like
45:42
rotate the image and so therefore you're
45:44
able to get these incredibly accurate
45:46
counts on people but you can also see
45:48
the actual behavior like it's almost
45:50
like you can see people's personalities
45:53
but I can't identify who these people
45:57
are as individuals so the we leverage
45:59
the head and shoulder signature we call
46:03
it which is dimensions of each
46:04
individual there they're only to kind of
46:07
do that associational mm-hmm follow up
46:09
follow people around the train doors and
46:11
they allow us to separate people amongst
46:13
the hundred but they're not enough that
46:15
we can then find them on the street or
46:16
anywhere else yeah
46:18
and obviously this information you can't
46:19
then reuse anywhere and find these
46:22
people so it allows a level of privacy
46:25
so we can see people moving and we can
46:28
see where they are in relation to the
46:30
the Train door a bit of cartwheeling
46:34
there bit of air guitar all right and we
46:36
can also see people who are not moving
46:39
as well can't we so we can see people
46:41
who might be a bit obstructing so we can
46:43
have a side view and you'll be able to
46:44
see us hopefully three people here
46:48
courses ok so the trade you you look
46:50
like you're being a bit obstructive
46:52
there how are they my goodness all right
47:02
no no ok this is getting really bad I
47:05
think we need some help here in
47:08
particular I think we need some customer
47:10
service attendants so
47:15
[Music]
47:19
we definitely need to sort this unruly
47:22
mob out all right so what do things all
47:51
righty all right wonderful okay
48:18
you can come out now all righty
48:23
so Simon can I ask you like you can see
48:26
this stuff now I think this is the first
48:28
time that you've seen this and we were
48:30
speaking the other day about all this so
48:33
if you had more information about where
48:36
people were located and what was
48:38
happening outside train doors would that
48:41
be useful to you as a as an operator
48:43
absolutely so my job is to look after
48:47
one door I can't see all the doors along
48:51
the train all 16 doors so if we have a
48:53
technology here where we could spread
48:54
people across the platform much more
48:57
evenly to use all available doors and
48:59
encourage them from using some sort of
49:01
app on our phone and move them along
49:05
much better so it's sort of like giving
49:08
you eyes that can see further than what
49:10
you can with your own eyes so far and
49:15
there's always lots of customers in
49:16
between but if I could see something
49:18
that's gonna tell me to encourage others
49:20
to move on I would try and encourage
49:22
them hmm okay thanks a lot for that
49:25
thank Simon and thank you Andrew all
49:27
right
49:30
okay so I'd now like to ask Mike ailing
49:34
from downer to join us Mike's the
49:37
general manager of intimate of
49:39
innovation at downer and you're kind of
49:41
responsible for a lot of these this
49:43
stuff that's going on so we've had it's
49:48
been great working with Sydney trains
49:50
but we've also been lucky to have
49:51
another project with down a rail that's
49:53
also been supported by the round
49:55
manufacturing CRC and so you're much
50:00
further along that that development
50:02
paths so I was just wanting to check in
50:06
with you to ask but where where are you
50:07
up to because I'm actually out of the
50:09
project now cuz I'm only do the early if
50:12
I don't do the later stuff so what
50:13
what's happening I think you had a
50:15
earlier slide so we're now moving into
50:17
the commercialization phase of this
50:18
project so taking all the great research
50:21
that you guys have done and that one's
50:22
done and turning all the other research
50:25
and all the all the algorithms turning
50:28
that into some survival data so it would
50:31
we're in the commercialization phase and
50:33
again it's great that we're actually
50:35
using the rapido the UTS function to
50:38
help us commercialize that so can't
50:41
think things that we're doing at the
50:42
moment is to two aspects will be the
50:45
hardware so they they'll see the cameras
50:47
hopefully we're putting a cover around
50:49
it because it's gonna be we need to
50:51
protect it so they're doing a whole lot
50:52
of covers and trying to turn that into a
50:55
manufacturable product so obviously
50:58
we're gonna need quite a few of them for
50:59
the platforms so we're doing all that
51:01
and also looking at the the sensing
51:05
cameras are making sure they work in you
51:07
know or normal environments obviously
51:09
underground and also outside as well
51:11
because sometimes you get your issues
51:13
with sun glare and other things so we're
51:15
working through that then there's a
51:17
software aspect to it which is turning
51:20
all that data and all those blobs are
51:22
one of the bigger blobs moving around
51:25
and turning that into verbal information
51:28
and we look we've got two use cases one
51:29
you know people like Simon we're turning
51:32
that into a product that you can have on
51:34
a device on the platform that can then
51:36
tell you where the density of that those
51:39
passengers are
51:40
and also linking that with some of the
51:43
data we taking off the trains to tell
51:44
you when the next trains coming in which
51:46
switch carriages are full which ones are
51:49
half full and which ones are empty so
51:50
you can try and distribute passengers
51:52
along they along along the platforms so
51:54
strongly about that minute and then the
51:57
second part is really then from an
51:59
operation center perspective turning
52:01
that into you know metrics and some some
52:04
dashboards about you know trends dwell
52:06
time trends that type things so pretty
52:09
exciting we're turning air into a
52:11
minimal Viable Product and that's what
52:13
we're working with at the moment I still
52:18
don't know I mean I haven't caught up
52:20
with this for a while it's it's it's
52:22
been it's been an interesting journey
52:24
because it this is now getting here for
52:25
master perspective the exciting bit
52:27
where we can turn that into a viable
52:28
product and lovely seeing that research
52:31
being turned into something that can be
52:32
utilized and you're working with them
52:35
steward and the guys we're hoping to get
52:38
something out sort of February next year
52:40
the first minimal Viable Product and
52:42
then to start launching it probably in
52:44
March April next year so that's the plan
52:47
that is actually quicker than I thought
52:48
there you go I'm genuinely quite someone
52:51
that wasn't in the script okay okay so
52:55
do you want to add anything else no
52:56
that's good all righty okay so thanks
53:00
Mike so what I'm going to do now is
53:02
we're now going to click forward to
53:05
another part of the robotic system we're
53:08
now going to start looking at a bit of
53:11
the actuation and I'm going to ask dr.
53:14
Nathan Koechner to join us Nathan's an
53:17
adjunct professor at the Institute for
53:19
sustainable futures where I'm also
53:21
located and I think what we might do
53:24
guys is if we can click back to the the
53:28
sensor that would be that would be
53:30
really good so Nathan what is this walk
53:38
us through this um so like many of us
53:40
said and I can't Anthony said it's all
53:42
about trying to change customer behavior
53:44
that can take many shapes and forms this
53:47
is just a really nice visual way to
53:48
communicate that idea so this is
53:51
actually a digital sign
53:54
built into the sensing system we've seen
53:57
before and built into our actual Waratah
53:59
door so you can imagine a system like
54:02
this in this particular case where maybe
54:04
I'm somewhere I shouldn't be in front of
54:06
the doors and I start to get the message
54:08
don't be there or maybe when the the
54:11
trains ready for me to go on it turns to
54:14
a green arrow or something that
54:16
encouraged me in the idea is just to
54:18
plug this in to normal operations but
54:21
like I started with it's just one of
54:23
many different possibilities this is
54:26
sort of like a little like a subtle cue
54:28
and we've used the term nudge quite a
54:30
bit so this is a sort of is very rough
54:32
and ready but this is the the
54:33
conceptually this is what we're looking
54:35
at isn't it so the best examples of us
54:37
using this are the hardest to shine room
54:39
like this so we've run our life in
54:41
Sidney trains in Town Hall I recently so
54:44
in the interface between Kiev in town
54:46
hall and Town Hall our kite and hole
54:49
obviously it's just a really soft gentle
54:53
note on the shoulder or you think about
54:55
it if you're walking along and you get
54:56
to a decision point and an hour goes
54:59
this way just as you appear just as you
55:01
get into the field of influence then you
55:04
know the one in ten person fears a
55:06
little bit to the left the follow that
55:08
by the time they go 20 meters down the
55:10
track maybe there are despite of stairs
55:11
and sort of that flight of stairs if
55:13
we've got a real example like at all
55:15
does where are stairs 10 if we have more
55:18
people using this entrance there would
55:20
be less problem on the concourse then a
55:22
bit of technology to do that Woodhull
55:24
the really interesting thing is when we
55:27
do it in places like Town Hall no one
55:30
notices no one realizes nor complains
55:32
about it no it says what happens there
55:34
were you conscious the numbers they did
55:35
it so they're going there I didn't said
55:38
I don't know what you're talking about
55:39
coach the numbers and they all went the
55:42
way we told them to go so we really have
55:44
hit that level of being of the influence
55:46
on the surreptitious level to get a
55:49
desired outcome without our overloading
55:52
so it does work
55:54
unfortunately it works unfortunate
55:55
because I'm research everyone things
55:57
work we have to do extra work it's
55:58
better when it's a good idea it doesn't
55:59
actually work
56:02
excellent good one okay so I mean
56:06
another couple of quick question so this
56:09
is I mean I think this is a kind of a
56:11
crude thing but it's sort of your green
56:13
and red we all understand that what I
56:15
mean there's this form but what other
56:18
sorts of forms could actuate in like
56:20
this come from because we talked about
56:21
nudging and it's supposedly subtle and
56:23
cue and it's I've stopped the bursting
56:26
it's crude I'm research of the very
56:28
fuzzy blue end of the spectrum just to
56:29
get an idea out there and they doing
56:31
someone like Mike jumped on boys down to
56:33
make trains and I started and built
56:35
whole train doors it wouldn't look like
56:36
that they would still work hard to
56:39
answer your actual real question it
56:41
doesn't have to be a lot it could be
56:43
ticket gates it could be barriers it
56:45
could be things on the floor it could be
56:47
the waste doors work I think the best
56:50
example for this is if anyone seen a
56:52
Pixar animation or a Disney animation
56:54
they can make a broomstick have life so
56:57
anything that can move and tell a story
56:59
and where people will respond to stories
57:02
if we're going along and the thing
57:03
that's wiggling tells us the story of
57:04
don't be here then we don't be there so
57:08
that that's that raises another point
57:10
because I mean it's good that we've got
57:11
the senses up so the the other thing
57:14
that you've often talked about how I'm
57:15
um
57:16
the other thing that you've talked about
57:17
a lot is that this type of actuation it
57:20
doesn't work if it's just going all the
57:22
time so you it's got to be subtle and
57:25
and then you run it through that and the
57:27
relationship in particular between what
57:29
why the senses are really important to
57:31
be connected to and the connections they
57:33
have with the actuation so it's the
57:36
classic we all know and we're all
57:38
fortunate because we are people and
57:39
we've gone through our lives being one
57:40
if you you enter an environment this is
57:43
constant noise you just ignore but if
57:46
you're standing somewhere and something
57:47
falls over you just instinctively go oh
57:49
it wasn't me so it's that little bit of
57:52
the information being contingent to you
57:55
having done something that means you pay
57:57
attention to information that really
57:58
meant something that's sign change
58:00
because I did something that fish moved
58:02
because I approached it it means you pay
58:04
attention to it
58:05
simplest way is more people looking at
58:07
the information the more people are
58:08
going to follow it the more people will
58:10
thinks that informations personalized or
58:12
individualized to them the more
58:13
we'll take it on board then it's a
58:15
matter of knowing what's happening in
58:18
the world in real time and they are the
58:20
connector to if we know where the people
58:22
are we know when they entered we know
58:23
when we want to tell them the
58:24
information a bit like Simon and you can
58:26
do if they can see what's going on they
58:28
know when to tell customers things then
58:30
we told them the information they need
58:31
to know then and only then which means
58:34
they instinctively listen to it take it
58:37
on board rather than just ignoring it as
58:38
yet another sign another exit sign we've
58:41
seen them everywhere so why do you here
58:42
there's one last thing I just want to
58:44
quickly talk about is the difference
58:46
between a robotic system and what you've
58:49
called a device mesh so I can say I get
58:54
what you're saying here so if I'm here
58:55
for example and I'm being naughty and
58:59
that that's then telling me that I'm
59:01
being naughty so there's no really
59:04
elaborate cognition system involved
59:06
there is there no so it's really a
59:08
continuum in a way if we had a widget
59:12
maybe it's fear of influence or
59:14
connectivity is only this big so maybe
59:16
it's a proximity kind of sensor but as
59:19
soon as that has a bit more of
59:20
intelligence like maybe it likes me so
59:22
let's mean here but doesn't like you so
59:23
it doesn't like you there that maybe
59:25
becomes a device the device mesh is the
59:28
concept of that's a useful device in and
59:31
of itself but it also connects to other
59:33
devices around so you imagine here if
59:36
this device were saying too crowded
59:38
everyone get away and its sister devices
59:40
at the top of the stairs saying don't go
59:42
down these stairs it's a bad entrance
59:44
it's busy down there go somewhere else
59:45
instead
59:46
then each sister device was saying at
59:48
entrance point the Concours don't go
59:50
this way go down the other way because
59:51
it's too crowded and there that's when
59:53
we start to stuck the devices into the
59:54
mesh start to get or blow the line
59:57
between the micro system micro
60:00
activation system and the macro a
60:02
Croatian system we can imagine we can
60:04
actually step over the precinct boundary
60:07
with the device mesh transition from
60:10
maybe something in a bowl ad or the
60:13
concourse to something on a mobile phone
60:15
or something on our news boards okay so
60:19
but but the big difference is that it
60:21
they're these individual things and
60:24
they're doing a job they helping the
60:25
situation make
60:26
it better by theirself they're
60:29
autonomous agents I can't be useful in
60:31
and of itself if it gets disconnected or
60:33
I fight it it still has viable valuable
60:35
function they're more powerful together
60:38
sure but they're very useful by themself
60:40
okay back in scale okay I hear what
60:43
you're saying
60:44
all right well that's good I think what
60:49
we need to do now is just look at a very
60:51
different type of sensing which could
60:53
lead to some other actuation devices but
60:57
thank you very much for that all right
60:59
so what I'd like to do now is ask
61:01
professor Sean hey to join us so Sean is
61:06
from the global Big Data technology
61:08
center and Sean's going to show us a
61:11
different form of sensing and I guess
61:14
the the main difference here short is
61:16
that what you and the folks have been
61:18
doing that have been working on these
61:20
these sensors and we might go over to
61:23
this other almost sensing so it's the
61:26
different camera now here we are yeah
61:29
okay so this is this is very different
61:31
and we're more at the you're more at the
61:32
proof of concept stage rather than the
61:34
product ization it don't you yeah agent
61:36
Lee Thank You Marshall
61:38
we want to actually to take the least
61:42
cost planning approach and see whether
61:45
there was any existing infrastructure to
61:48
use and a lot of problems or one of
61:51
their problems kept coming out from our
61:54
workshop and also from today is the
61:57
congestion so we have find ways and you
62:02
know being looking at ways to create
62:05
congestion Hitmen and they can till the
62:09
congestion levels and in your time at
62:12
the platform at concours at anyway hmm
62:16
okay so I've forgotten all right okay so
62:20
did you want us to go and maybe get some
62:23
more volunteers to show how these work
62:25
or did you want to run us through some
62:26
of the IDs yeah don't this work is many
62:31
done by warm by PhD student called
62:34
hungry who is here and yes so we just
62:37
want to tell the ideas
62:39
I'm going to show you our real system
62:41
actually implemented and in this system
62:45
you can see we have camera there
62:47
so pretending this one is the CCTV
62:49
camera so the idea is okay so a CCTV is
62:53
to send people okay within the green box
62:55
here and then when we have more and more
62:57
people coming coming up here so
62:59
hopefully we can build up the
63:01
conjunction map or the Hitmen and then
63:04
when the number of people reach the
63:07
threshold okay so we set a special to be
63:10
8 right so and when the number of
63:12
people's reach the stretcher
63:13
then we will create the warnings you
63:16
know or I saw which you can hear so
63:18
hopefully you know we can say see the
63:21
right hand side the blue one okay stick
63:23
and told him it so at this moment we
63:24
don't have many people we have one image
63:26
showing myself here so it won't seem
63:28
very heavy so yeah so being would be
63:32
nice you know if we can have more from
63:34
the audience okay guys we can greatly
63:36
build up the heat map yeah so as you can
63:39
see right so on yeah so we want people
63:41
to be in the green box alright so and
63:43
then now at this moment this technique
63:45
is more or less based on their face no
63:46
condition so when the number of people
63:49
reach age okay so we hear the voice okay
63:51
so say cloudy cloudy then we would like
63:53
to have people move out of the outside
63:56
black line and then the number of the
63:58
people will reduce to zero at this
64:00
moment right so we hate yes so what so
64:04
this is the whole idea so I'm you know
64:06
so we can be until when you know the
64:09
location okay so it's crowded and you
64:12
know we can make kind of advice okay to
64:16
tell people a way to move and you know
64:18
to let say for simple place or kind of
64:20
thing okay so I mean one of the things a
64:23
lot of us here who were transport people
64:25
we know about and think about is level
64:27
of service criteria and there's levels
64:29
and measures for that so this system
64:32
could basically give a live level of
64:36
service measure um by looking at the
64:39
densities over different areas you
64:41
exactly got this one X is very different
64:44
from the one just mentioned by Ellen
64:46
them might okay so we are not using 3d
64:49
census so all we are using here is 2d
64:53
with 2d sensor we can only get to the
64:56
threat visual kind of pictures or images
65:01
so will not be able to easily tell the
65:05
location of each individual person I saw
65:07
as you can see you know we can still
65:10
love detail where people saw so it
65:12
doesn't mean we cannot do it or I saw
65:14
just mean motive code yeah so for that
65:16
big group behavior yep this this is good
65:19
enough for some some aspects of it more
65:22
this one this is webcam yeah it's good
65:24
enough for now so I'm for the Lea system
65:26
we are using the existing infrastructure
65:28
CCTV cameras at the local station for
65:31
the for the testing so we are using
65:33
machine learning technique so which one
65:35
condition the audience I can so we wish
65:39
the CCTV cameras we have a wider view
65:41
and then we can take videos of many
65:45
people about 100-200 people's in the
65:48
scene and then we can still estimate
65:50
number of people's on the platform on
65:53
any areas okay so in the change station
65:56
so do you want to do you want to take us
65:58
through some of those yeah yeah before
66:04
showing him our CCTV base and density
66:07
estimation human density estimation and
66:10
also DeMuth I need to show one of the
66:12
work done by another team okay in our in
66:16
your buffer Cote so which is led by
66:18
Professor watch a mouse so this way is
66:21
based on Wi-Fi okay not it in CCTV so as
66:24
you can see okay based on Wi-Fi I'm the
66:26
thing has already also created a heat
66:28
map and this already taking into account
66:31
obviously CCTV data as well although
66:33
Hema credit using our CCTV data and you
66:39
know so se concedes and you know Wi-Fi
66:42
actually can not actually tell the
66:44
number of people because not everybody
66:46
would turn on the mobile phone so I can
66:49
I see her and so audience here probably
66:51
20 percent 30 percent of people thing on
66:53
the mobile so yeah we can only detect
66:56
people who already think of the cenotes
66:59
so but nevertheless okay so probably
67:01
this is not very clearly seen so using
67:04
our TTS ect
67:06
the camera okay based on one single CCTV
67:08
camera this is the concourse areas in a
67:10
different station so in the middle one
67:12
you can see ok the kind of Testament
67:15
which is not him
67:16
the Rohan sized him at the dimension is
67:18
more look more like the Unreal dimension
67:21
of the of the concourse but one camera
67:25
may not cover the whole whole area so in
67:27
a different station platforms 2 or 3 & 3
67:31
okay you can see one camera see if I can
67:34
only cover okay so about 1/6 of the
67:37
areas in a dear and open station so
67:39
that's why we need to combine the view
67:41
of different cameras together
67:43
so you see c9 and CA and c7 c9 see if I
67:47
see now basically you know back to back
67:50
ok next to each other one covering
67:52
platform to c9 and see if I come in and
67:55
perform part of platform 3 and then we
67:58
have c8 ok so we've been so covering
68:01
more part of the platform's and c7 okay
68:05
for viewing in different directions and
68:07
then you can see the combined result at
68:10
the at the bottom so this work Orchestra
68:12
sister fossa plot we have two different
68:14
approaches actually this approach is
68:16
many done by my pasty skin and Helia so
68:19
who is also here so I'm going to show
68:21
you actually another one you and the
68:24
audience so I'm done another wise more
68:26
intelligent approach so this way is
68:29
based on my tip Nani okay so everybody
68:31
now no did noni okay so it's very smart
68:34
if they're also very actually technique
68:36
so from this one so you also see the
68:38
combined result ok of the different
68:42
station for platforms on 2 and 3 and
68:45
then in the middle part you see the him
68:47
Ezra alright so in the bottom chart okay
68:50
you see the accuracy so the blue ones
68:52
telling the estimated number okay and
68:56
and the real one I think is the no the
68:58
blue one provides the clown shoes and
69:01
the red one STM is the estimate number
69:04
so you can see the number go from over a
69:07
hundred to to a few people
69:10
okay on the platform we can actually
69:12
model s quite actually estimate the
69:15
number so that
69:17
the difference is not very much on
69:19
average so you're getting counts and you
69:21
get reading relative positions as well
69:23
yeah we can also get a little position
69:26
but if you look at the heat map right so
69:28
also we can see okay total the areas you
69:32
can see the red color okay that tail is
69:34
quite quite kind of in a crowded area
69:37
over there okay on upper floor so if you
69:40
see the blue areas that mean nobody over
69:42
there okay like you know the view on c7
69:46
okay at this moment we see we see nobody
69:48
okay yep so there's still some more to
69:51
go with this do how to apply it
69:53
yes so we need updates more because we
69:56
record this data only in July sometimes
69:59
so using deep learning we need to have a
70:01
lot of data to you know to collect and
70:04
then name for the training so then we
70:06
can have more accurate result at small
70:08
metal result is quite present already so
70:11
we you know we still be able to to see
70:13
more a lot more accurate result and then
70:17
you know and then we go to the
70:18
application so for sure yeah okay thanks
70:21
for that
70:21
thank you much Elaine thank you yeah all
70:23
right okay so um oh yeah the clicker
70:27
I've got it back alright so what I'd
70:31
like to do now is ask dr. Chanyeol you
70:35
to come and join us so I wanted to ask
70:39
you a channel what are your thoughts
70:41
about connecting all of these things so
70:44
Nathan that I had a quick discussion
70:45
about the device mesh where it's all
70:47
relatively unconnected but you're
70:49
looking at cognition which is about
70:51
collecting or connecting those things so
70:53
what tell us a bit about that I guess
70:55
I'll start from the very top view so
70:57
with this all new technology now we know
71:01
more about the micro level passenger
71:03
flow and which means that that we now
71:06
know where people are and where they are
71:08
moving to in real-time and there's one
71:11
others also be information that we
71:12
actually have to talk about which is the
71:14
Opel cars
71:16
with this new Opel Adam system that we
71:20
there's recently implemented we now can
71:22
track the macro-level passenger flow
71:24
from stations to stations in this video
71:28
what we see is the
71:30
passion TM crowdedness of each station
71:33
and how many people are actually moving
71:35
from one station to the other so each
71:38
circle represents station and the size
71:40
in the color represents how crowded and
71:43
how many people there are and the lines
71:45
represents like each people traveling
71:47
between the stations so yes yes yes I
71:55
was hoping you wouldn't do this to us I
71:57
sort of met I'm just a planner I can't I
72:02
can't okay I can't cope okay okay so now
72:10
the question now the question is how do
72:12
we utilize how do we fully utilize all
72:15
this information to improve customer
72:17
experience and in order to do so we
72:19
should be able to digest all this
72:21
information and then understand in our
72:24
brain like how everything works
72:26
and my question is can we actually do
72:28
this can we actually under can we
72:30
actually digest the oldest information
72:31
as human and then come up with something
72:34
smart like smart plan that optimizes
72:36
like a network throughput or something
72:38
so in this scoping study we started off
72:41
by mathematically formulating the
72:44
passenger flow for each station so here
72:46
our station is decomposed into different
72:50
parts so here we have concourse
72:51
platforms and gig education stuff and
72:54
then we describe the relationship
72:57
between each part of this station so I
73:01
still think the maths is just too much
73:03
because I don't want to know this is
73:05
exactly what I expected so this shows
73:07
you that the system we are dealing with
73:09
is very sophisticated it is very complex
73:12
and most of us most of us including
73:14
myself I mean I wrote this but yeah I
73:16
don't really understand like exactly
73:18
what is going on so this is the point so
73:20
which means that this cognition problem
73:22
we are trying to solve is very
73:24
non-trivial and it's very hard to solve
73:26
so as human I don't know how to solve
73:29
this for now not yet not yet okay so
73:32
let's take two station views so let's
73:35
say this is actually put some numbers
73:40
but nothing so we have two stations here
73:43
and now we can like connects make some
73:46
like like to draw some lines to
73:47
represent the dependencies between two
73:50
stations like how each component in a
73:52
station is interconnected with the other
73:55
part in other station so this is simple
73:57
enough this is what happens if we have
74:00
ten stations so it is impossible for
74:02
human as a human to understand each bit
74:05
and what is going on as a like a whole
74:08
so but but the problem is we have more
74:11
than 200 stations in New South Wales and
74:13
then I actually try to put like 50
74:15
stations but it was impossible
74:16
everything was black so I kind of get
74:20
this one that I said to get the vibe
74:22
with this well I couldn't read the math
74:24
yes so if you just look at like one by
74:26
one that you might understand but if you
74:29
have to understand the ant dependence
74:30
between every single variable that is
74:33
the point this so if this one
74:34
illustrates along with this some model
74:37
that we have to actually understand
74:38
everything in order to make some smart
74:41
decision yeah so it is impossible for us
74:46
to understand everything so and what is
74:48
even harder is to actually come up with
74:50
some smart decision so instead of
74:54
instead of having human to do the two T
74:58
to do these kind of jobs we would like
75:00
to have a like a smart cognition system
75:02
that works like an Oracle so we would
75:05
like a system like this one - OH
75:10
to oversee the whole network the system
75:13
that gets the real data at the the real
75:18
time data from CCTV and Wi-Fi and all
75:20
these sensing things and then make a
75:22
smart decision based on this information
75:24
and we also want this system to forecast
75:28
and predict what's what is likely to
75:31
happen and act before that happens
75:33
rather than simply reacting to what is
75:35
locally happening so if I'm if I'm
75:40
hearing what you're saying it's sort of
75:41
like with the Apple car data that you
75:44
were talking about earlier it might be
75:46
the case that if you've got a really
75:48
smart cognition system in
75:51
there might be something that goes on
75:52
it's a Strathfield station two hours in
75:55
the past that is then going to affect
75:58
what goes on at townhall station and the
76:01
system has actually learned how to
76:03
recognize yes so based on this Opel data
76:06
which which we accumulate over time we
76:08
can learn how like how things act
76:13
differently based on what we have
76:15
currently and based on this real-time
76:17
data we can act we can predict what's
76:19
going to happen and act in advance or to
76:22
find like a better solution that
76:24
optimizes that resolves this situation
76:27
kind of thing so so in the scoping study
76:31
we have identified like three most
76:33
important problems that we think is like
76:35
critical as part of this cognition
76:37
system so first is the interchange
76:39
problem if a passenger is traveling from
76:42
one station to the other where how do we
76:45
estimate where this passenger is gonna
76:47
transfer like at which station a
76:49
forecasting problem if we see a large
76:52
crowd of people entering a station at a
76:54
certain time how would it influence the
76:56
rest of the network and the lastly I
76:59
think this is really important problem
77:00
planning problem if we alter the
77:03
timetable how would it affect the
77:05
passenger flow and ultimately can we
77:08
find an optimal timetable that maximizes
77:11
the network throughput as well as the
77:13
passenger satisfaction okay I think
77:19
that's about it yeah I hope it is
77:21
there's no more mess
77:31
wherever you are and wherever you go in
77:34
the world may you always travel well
77:35
thank you very much
77:37
[Applause]