►
From YouTube: CI WG demo: MASS DOT Smart Transportation
Description
Date: 5/4/18
Presenter: Rachel Bain
Institution: Massachusetts Department of Transportation
Northeast Big Data Hub
A
B
B
B
B
My
office
is
a
shared
service
with
our
transit,
our
biggest
transit
agency,
the
MBTA,
which
is
led
by
a
general
manager,
who's
hired
by
the
secretary
and
you're
you're,
getting
a
crash
course
on
Massachusetts
politics
right
now,
so
it
gets
a
little
bit
dicey,
but
basically
we,
you
know
what
it
amounts
to.
As
we
serve
everybody.
We
have
a
highway
division,
a
motor
vehicles,
division,
a
rail
and
transit
division
and
in
there
are
not
exhibition
and
then
a
dotted
line
to
again
this,
the
the
Boston's
biggest
transit
agency.
B
We
were
created
when,
when
we
did
transportation
reform
in
the
Commonwealth
back
in
2009,
which
took
a
whole
bunch
of
disparate
transportation
agencies
and
squished
them
all
together,
the
technical
term
under
mascot,
and
so
as
part
of
that
legislation.
What
they
created
was
in
the
office
of
performance
management
and
innovation,
and
a
lot
of
our
charge
was,
you
know,
set
set
performance
goals
and
measures
set
targets,
and
you
know
track
performance
and
that's
really
evolved
over
over
the
years.
B
One
of
the
one
of
the
key
things
is,
we
really
pulled
the
MBTA
work
and
the
the
transit
stuff.
So
now
you
know
my
staff
is
50%
MBTA
50%
mascot,
and
that
gives
us
an
opportunity
to
kind
of
cross
pollinate
this
analysis
and
tools
and
lessons
learned
from
across
division.
So
things
that
we're
learning
in
transit,
I
can
apply
to
motor
vehicles
and
then
maybe
to
the
highway
division
and
and
you'll
see
some
examples
of
that
further.
B
We've
done
much
more
on
our
own
data
collection
in
the
form
of
survey,
work
and
we've
done
a
lot
more
on
the
data
visualization
in
the
form
of
dashboards
and
and
some
some
other
tools
for
operations.
And
that's
you
know,
I'll
show
you
some
of
that.
So
I
thought
we
would.
We
would
jump
in
to
how
we're
using
technology
to
report
performance
and
kind
of
delve
into
what
has
become
kind
of
a
bread
and
butter
business
for
us
which
are
at
the
dashboards.
B
B
It's
their
management
dashboard
and
what
it
shows
is
for
the
entire
Registry
of
Motor
Vehicles
Network,
which
are
29
retail
branches,
the
volume
of
customers
that
day
and
the
the
percent
of
our
customers
that
were
waiting
under
30
minutes
between
30
and
60
minutes
and
over
60
minutes
back
in
February.
They,
our
Motor
Vehicles
division,
had
been
running
a
system
that
dated
back
to
1986.
B
It's
basically
it
was
our
it's
been
kind
of
a
good
sandbox
for
us
to
develop
some
tools,
kind
of
quick
and
dirty
and
then
see
if
they
are
going
to
kind
of
sustain
themselves
in
the
organization.
So
it's
a
good
pilot
space.
For
us,
this
is
just
goes
more
in
depth,
as
you
can
look
at
it
by
service
center.
B
So
the
this
is
the
Boston
branch
on
February
27th
of
this
year,
and
they
were
just
knocking
it
out
of
the
park
they
were
doing
really
well
and-
and
we
can
look
at
it
by
service
type,
so
license
ID,
renewal,
registration,
renewal
things
like
that.
These
are
all
things
that
are
critical
for
the
Motor
Vehicles
division
to
look
at
on
the
daily
basis.
B
B
Here
in
Boston
it
snowed
just
a
tremendous
amount
and
it
kept
snowing
and
it
kept
snowing
and
it
kept
getting
colder
and
colder,
and
it
basically
crippled
the
MBTA
for
about
a
month,
and
so
lots
of
things
came
out
of
that
we
have.
You
know
we
ended
up
with
a
management
Control
Board
and
we
ended
up
with
all
sorts
of
other
kind
of.
B
Organizational
changes
that
came
out
of
that
winter,
but
what
also
came
out
of
it
is,
is
people
all
kind
of
looked
around
and
said.
We
really
don't
know
how
the
system
is
operating
and
our
customers
in
particular,
don't
have
any
kind
of
faith
that
we
are
reporting
our
data
on
a
regular
basis
and
accurately.
So
we
built
the
the
MBTA
back
on
track
dashboard.
It
has
a
couple
of
key
features.
The
the
most
interesting
is
the
reliability
feature,
which
is
this
upper-left
square.
B
It's
the
thing
that
changes
every
single
day
and
I'll
I'll
kind
of
delve
into
it.
Basically,
you
can
look
at
the
entire
system,
which
was
the
slide
before
then.
You
can
look
at
say,
Subway's,
specifically,
and
look
at
how
the
subway
performed
on
February
27th
of
2018
and
basically
that
is
showing
how
long
did
a
customer
have
to
wait
on
the
platform.
You
know
across
the
whole
subway
network
and
then
you
can
go
a
little
further
in
and
you
can
look
at
it
by
the
red
line.
B
Specifically,
that's
the
next
one
and
then
you
can.
You
know
you
can
look
at
it
by
peak
and
you
can
look
at
it
by
off-peak,
and
so
these
are
kind
of
the
different
ways
that
you,
as
a
customer
could
play
with
the
data
yourself
and
look
into
it
and
determine
you
know:
did
you
really
wait
a
long
time
that
day
was
that
an
outlier
day?
Is
it
consistently
poor-performing
or
is
it
you
know
we
just
had
a
bad
day
and
I?
B
Okay,
so
this
kept
slightly
more
interesting,
so
this
is
where
we
really
lean
on
mg,
HPCC
and
and
John's
team.
We
have
a
relationship
with
MIT,
we
have
research
contract
with
them,
and
one
of
the
things
that
they've
been
developing
over
the
past
few
years
is
an
origin
destination
model
for
the
MBTA.
We
don't
have
a
fare
collection
system
that
is
tap
in
tap
out,
like
some
other
transit
properties.
Do
it.
B
We
have
just
tap
in
so
we've
had
to
infer
where
you
get
out
of
the
system,
and
so
it's
taken
a
while
to
do
that,
and
but
it's
really
important
to
know.
You
know
where,
where
are
people
starting
their
trip
where
they
ending
their
trip,
especially
when
we're
making
service
changes
so
to
help
service
planners
test
route?
Changes
example,
for
example,
is
cutting
back
a
route
or
extending
a
route
or
removing
a
stop.
B
We
can.
We
can
see
for
any
given
stop
or
set
of
stops
or
routes
how
many
people
are
using
it
as
an
origin,
a
transfer
point
or
a
destination,
and
if
a
lot
of
people
are
using
that
stop
as
a
destination,
this
tool
lets
them
see
where
they're
coming
from
or
if
people
are
using
the
stop
as
their
origin.
Where
are
they
going?
B
It
also
provides
a
route
summary
information
for
service
planners
to
see
how
the
route
performs
in
terms
of
our
service
delivery,
metrics
and
our
data
sources,
for
this
are
basically
GTFS
which
are
stops
and
routes
or
our
origin,
destination
and
transfer
model,
and
then
the
service
delivery
policy
performance
measures.
You
can
kind
of
see
how
it
how
it
works
in
a
little
video
air.
Hopefully
it
just
shows
the
different
ways
that
we
can
make
a
selection.
B
A
B
B
This
is
we
are.
We
are
also
in
partnership
with
Waze,
and
this
is
maybe
the
best
example
of
why
my
office
I,
like
the
fact
that
my
office
is
positioned
between
kind
of
the
public
transit
part
of
our
work
and
the
highway
and
Motor
Vehicles
part
of
our
work.
We
got
into
the
partnership
with
Waze
mostly
to
support
the
highway
division
and
their
highway
operations
needs,
but
my
team
is
kind
of
heavy
on
the
transit
planner
background,
so
they
were
looking.
B
B
Don't
remember
when
waves
caused
them
I
think
they
they
call
it.
Basically,
like
jams,
you
know,
traffic
jams,
so
what
we
did
is
we
took
the
way
stuff
that
we
were
working
with
on
the
highway
side
and
we
put
it
on
a
map
with
all
our
bus
stuff
on
the
MBTA
side,
and
we
created
this,
this
merger
of
the
ways
and
the
real-time
bus
data,
so
that
we
could
give
our
dispatchers
of
more
information
when
they
were
making
decisions.
B
B
B
To
all
of
this,
this
is
a
tool
we
built
to
visualize
the
Green
Line
vehicle
locations
in
real
time
previously,
what
was
happening
is
dispatchers
with
radio
the
operators
to
get
their
locations
and
most
of
our
green
line,
which
is
the
oldest
line
in
transit
in
the
United
States
I
believe
and
acts
like
it
in
Boston.
It
certainly
still
acts
like
it.
B
B
Our
it's
actually
gone
to
production,
so
we,
you
know
it
was
great
to
build
this
as
a
prototype
tool,
actually
get
it
into
the
field
for
testing,
see
if
people
liked
it
and
then-
and
you
know,
take
all
of
that
information
that
that
we
learned
from
that
that
pilot
and
then
actually
build
all
of
that
information
into
a
production
level
tool
which
is
probably
that's
pretty
exciting
other
stuff.
We
do
home,
we
do
surveys,
so
this
is
our
MBTA
customer
customer
opinion
panel
survey.
We
put
out
a
customer
opinion
survey
every
single
month
and
again.
B
B
So
these
big
paper
reports
I'd
like
to
get
rid
of
as
much
as
possible,
while
still
giving
both
the
internal,
our
internal
customers,
what
they
need
to
manage
their
business
and
our
external
customers
as
much
information
about
our
system
as
they
need
to
feel
well-informed.
That's.
This
is
just
another
example.
This
is
our
annual
report
tracker
and
then
I
usually
end
all
my
presentations
with
this,
because
this
is
what
my
staff
comes
in
and
and
says
to
me.
They
knock
on
my
door,
and
so
we
did.
B
We
did
a
thing
and
okay,
what
was
it
and
my
favorite
thing
that
they
do?
Is
they
maintain
a
data
blog
and
you
can
find
that,
through
our
back
on
track,
our
MBTA
back
on
track,
dashboard
and,
basically
to
show
us
all
of
the
research
and
the
data
analysis
and
what
we
are
thinking
and
what
we
are
doing?
It's
definitely
not
one
of
those
like
good
news.
B
You
know
organizational
blogs.
This
is
real
geeky
stuff,
so
they
they
love,
maintaining
it
and
it
gives
kind
of
an
insight
into
our
process
and
when
we're
trying
to
figure
out
like
how
to
look
at
a
you
know
how
to
look
at
data
and
how
we're
going
to
work
with
it
and
when
we
get
something
wrong.
That's
that's
been
another
another
interesting
thing.
When
we've
gotten
something
wrong,
we
try
to
explain
it
there.
A
That's
very
cool,
interesting
I,
like
the
idea
of
sort
of
the
feedback.
Blog
being
you
know
the
honest
one,
not
the
not
the
PR
one
that
many
places
have
so
I
like
that
idea
anyway,
go
ahead.
Just
go
around
see
if
there
any
questions
Rachel
today
and
if
not
then
I've
got
a
couple
of
questions,
maybe
to
join
the
two
together.
I.
A
Think
we're
running
late
in
the
day,
so
yeah
I
have
my
earlier
question.
Is
it
John
was
a
little
bit
of
a
setup
for
a
Rachel,
because
I
think
she
that
in
the
four
years
that
I've
known
her,
the
the
state
government
is
transitioned
from
proof
why
I
should
release
my
data
to
prove
why
I
shouldn't
release
my
data
as
the
the
mantra
starting
with
the
governor
and,
if
there's
time
kind
of
talking
about
that
experience
would
I
think
be
of
interest.
Yeah
I
think
that
might
be
a
good
way
to
go.
A
I
was
also
going
to
put
there
because
I
know
Austin
and
Boston
share
more
than
just
you
know
the
similar
sound.
We
have
good
universities
that
do
interesting
things
and
a
lot
of
interesting
people
sort
of
in
the
community
and
I
know
here.
Part
of
you
know
some
of
the
data
that
have
been
released
taken
on,
for
example,
you
know
somebody
got
ahead
of
Capital
Metro
here,
building
our
bus
application.
That
just
did
it
themselves
one
day
because
they
were
bored
you
know,
and
so
opening
data
up
and
making
it
shareable.
A
C
This
is
Jen
I'm
interested
to
hear
what
Rachel
has
to
say
as
far
as
how
we
share
information
with
other
cities,
it's
at
least
in
the
transportation
world,
its
transportation
agency,
the
transportation
agency.
So
we
participate
in
the
National
Association
of
city
transportation.
Officials,
which
is,
is
a
pretty
progressive
organization.
That's
interested
in
topics
like
these,
although
is
more
or
probably
less
technical
folks
and
need
to
be
involved,
and
then
transportation
for
America
has
a
smart
cities
collaborative
that
I've
started
participating
in
there's
this
kind
of
things
definitely
of
interest.
C
I
know
there
was
some
get-together
at
Harvard
in
the
near
future
that
one
of
our
staff
was
going
to
that
was
more
general
beyond
transportation,
but
yeah
I'm
curious
what
what
I?
What
thoughts
you
have
Rachel
or
how
you
share
information,
I,
guess,
a
one
final
one
I'll
mention
is
Metro
lab
network
that
we've
been
involved
in,
which
is
more
general.
It's
just
City
University
partnerships
on
smart
city
topics.
That's
been
a
great
place
to
kind
of
share
across
domains,
but
always
looking
for
ideas.
B
Sure
we
I
think
we
take
a
lot
of
advantage
of
TRB
as
well
as
the
Transportation,
Research,
Board
and
so
work.
My
staff
in
particular,
is
pretty
active
both
on
panels
and
nchrp
kind
of
research
and
writing
their
own
papers
and
presenting
every
year
and
that's
been-
that's
been
a
really
good
Avenue
for
us,
just
just
generally
for
information
sharing
and
meeting
meeting
people
and
feel
like
we've
presented
on
the
back
on
track
dashboard
at
the
annual
meeting
before,
and
then
you
know
we
can't
we
got.
B
You
know
models
down
there
and
they're
saying
like
how
did
you
do
this?
Like?
Can
we
talk?
We
talk
about
the
design
of
it,
you
know,
and
so
that's
been
probably
our
biggest
go-to
for
for
that
kind
of
sharing
and
in
terms
of
more
broadly
just
sharing
data
and
it
may
be
hitting
on
John's
question.
You
know
the
legal
and
security
constraints
on
this
data.
Publishing
and
sharing
it's
I've
been
ill
I.
Probably
in
the
beginning
of
this
and
John
probably
knows
this
well,
I
was
probably
a
little
rogue
where
I
was
just
like.
B
We
want
you
to
just
be
able
to
download
it
into
like
a
CSV
file
and
and
go
to
town
like
tell
us
what
you're
finding
see.
What's
interesting
and
we've
we've
found
that
that
that's
been
great
and
so
we're
trying
to
offer
data
to
the
public
in
a
couple
of
different
formats.
That
I
think
we
did
in
the
beginning,
which
was
very
developer.
Heavy.
A
C
A
Think
that'd
be
interesting.
It
might
be
something
to
be
kind
of
cool
to
do
is
bridging
across
these,
because
stealing
each
other
software
is
the
best
way
to
make
up
these
interoperability.
Things
work
at
fine
I'm,
coming
from
astronomy,
where
there
is
no
value
to
the
data,
so
we
stole
a
lot
of
each
other
software
so
anyway,
I
want
there's
anything
else.
Then
I
will.