WEBVTT

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<i>hacc preroll music</i>

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Herald: And a lovely welcome back to the
haccs stage on the third day this

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Congress, we are here with a talk on "A
few quantitive thoughts on parking in

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Marburg" by Martin L. He's interested in
data analytics now and infrastructure and

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traffic in general. And because of that,
he started scraping publicly available

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parking data in Marburg and just went on
and analyzed it and found a lot of

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interesting things which he is going to
present in this talk to you right now. In

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case you didn't know, there is IRC client
on the live.hacc.media where you can ask

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questions later or with the #rC3hacc tag
on Twitter.

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Martin Lellep: Welcome to my talk "A few
quantitative thoughts on parking in

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Marburg". I am delighted to speak here on
this Congress because I love the yearly

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conferences. Also, thank you to the
organizing team for making all this

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possible. You do an absolutely fabulous
job. Now, the first question that you

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should ask is: why? The following is a
purely hobby project question, I came up

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with a question because transportation is
important, but unfortunately, it's also

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difficult. The most popular vehicles these
days are cars and hence the question, how

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do people park in Marburg? Who am I? My
name is Martin, and I analyze publicly

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available data. I live close to Marburg,
therefore the parking in Marburg. Now, a

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little bit of background regarding
Marburg, it's a small picturesque, vibrant

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university town. There are a few highlights,
such as the castle, the old town and the

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river, just to name a few. It has around
80,000 residents and a somewhat dense core

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around the old town. You can see a few
pictures here of the castle, the old town

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and the river, respectively. Now, at this
point, I would like to give my props to

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David Kriesel because all this work was
inspired by his amazing data science

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talks. You can find them on YouTube. And I
absolutely encourage you to look for the

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Bahnmining, Spiegelmining and the Xerox
story talks. OK, so if you have questions,

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then please ask, I will be there live
during the Q&amp;A of this conference and also

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you can send me an email with whatever you
like, essentially. OK, so first of all, I

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would like to give a quick introduction to
the data source. Now, the data, the

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parking data from Marburg is publicly,
well it's published live on a system that

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is implemented by the city, by the city
council, I believe . It's called

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Parkleitsystem Marburg or PLS for now, and
it publishes the data such as the parking

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decks, the number of free parking spots
and the location. The address here is

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pls.marburg.de. And let's see how it
looks. Yeah, so obviously it's still

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online and you can see here the parking
deck names listed, the number of free

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parking spots. Color coded is if it is
rather full or if it's rather empty, you

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can see here all of them are in the green.
The green color coding here, it's

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because it's probably close to Christmas.
Nobody wants to really park in the city.

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And the only one that's this one here, the
Marktdreieck Parkdeck that it has some

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load to it. Then also there's a button
called route. So whenever you click on the

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on this button, say we we pick the
Erlenring-Center button, we are redirected

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to Google Maps and we can see here the
location of this parking deck, for

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example. Let's go back. Last but not
least, there's also the maximum vehicle

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allowance and of course, the time stamp of
the data. OK, back to the presentation

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now. This is a very simple website, so of
course it's easy to scrape and that's what

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I did. Regarding the scraper, I used a
Linux computer and a docker container. And

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this scraper, you can see a small sketch
here to the left, it simply visits the

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website every 3 minutes inside the docker
container and writes the data into I

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believe it was csv files, which are
subsequently used for the data analysis.

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All of it, the scraper and the analysis
scripts are written in Python. OK, the

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data format is pretty simple, it's
processed internally with data frames,

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with the package panda. Everybody who
knows Python probably knows panda, anyway.

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It's the data format is as follows. The
row corresponds to the time. The column

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corresponds to the specific parking deck,
and the cell corresponds to the number of

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free parking spots at that time of that
parking deck. Now, in order to make the

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numbers a bit more usable, I transformed
the number of free parking spots to the

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number of used parking spots by
subtracting it from the maximum along the

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time. OK, now the intro is just to get
used to the data, we'd like to take a look

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at the locations of the of the park houses
or the park decks. This is a screenshot.

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There's an interactive version. Let me
open it here. It's a interactive map. You

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can see two types of markers, the first
one red, the second one green, and that's

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because the red ones are the ones that are
given, well they are encoded in the links

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of the PLS system, and they
are actually wrong. So when you click on

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the for instance. Erlenring-Center parking
deck that I've done before, the location,

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longitude and latitude are actually
incorrect and, um, Google Maps corrected

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on the fly. And therefore, I have shown
here the ones given on the website that

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are incorrect in red and the ones shown
that are correct. So you can safely focus

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only on the green ones. Um, a quick
overview here is the train station region,

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there are two. And then they are scattered
around the city. Um, sometimes there are

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two parking decks very close by, for
instance, these two and these two. And

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that's because it's essentially one
parking deck with two parking sections

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typically inside the building and on top
of the building. OK, let's go back to the

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presentation. With that in place, we or we
take a look at the joined data, meaning I

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accumulate the number of used parking
spots across all the parking decks. You

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can see that here now, so it's a quite
comprehensive picture, I started data

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scraping in August 2019 and stopped it at
the end of February 2020.

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This data here is a different resample
frequency of the original and raw data. I

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started with a resample of one hour. So
just a reminder, it's the true frequency

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is three minutes. Again, I resampled here
into one hour. It's not very easy to

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understand on that scale here. Then to one
day it's the orange now and lastly on one

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week and we can learn different things
from it. So in particular, the orange

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curve of one day shows that there might be
some periodicity in the signal. And the

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green one shows that there are times or
weeks that are particularly... where

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there's particularly little parking
demand, for instance, here around

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Christmas 2019. OK, so again, from the
orange signal, you can see that there's

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probably some periodicity, and in order to
quantify that, I plotted the or computed

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the auto correlation function. The auto
correlation function essentially takes a

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time signal and computes the overlap
between the time signal and the same

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signal shifted by some time and whenever
there's a large overlap. That points

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towards the periodicity, and here we can
see that the periodicity maximum or the

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auto correlation maximum, the first one
corresponds to one week and therefore the

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periodicity can be safely assumed to be at
seven days. Of course, when there's

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periodicity and a signal at seven days,
for instance, there's also periodicity. In

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14 days and in 21 days, but the
correlation coefficients, they decay

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typically. OK, now we have the periodicity
with respect to days in place. Now let's

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take a look at the day and hour demand.
And for that, I computed a two dimensional

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histogram with the day Monday to Sunday on
the one axis and the other axis

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corresponds to the hour. And here we can
clearly see that the majority of the

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parking demand is around the noon hour. So
starting from 11 to to approximately,

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let's say, 5 p.m. or so. Interestingly.
That was a point where I was surprised is

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that Sunday's is a day where there's
little parking demand in Marburg, I

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wouldv'e guesstimated that Sunday when
everybody has spare time, they typically

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rush into the city. But that's obviously
not the case. Another interesting fact is

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that Monday morning seemed to be very
difficult to get up because you can see

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the parking demand is smaller than on on
other mornings. OK, now, after that, I

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come to the separated... separate and
analysis where I take a look at the

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individual parking decks. So first of all,
again, the times series, it's it's a bit

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dense and it's very hard to see. So there
are a few things to learn from the

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picture. So first of all, the green
signal that corresponds to the Erlenring-

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Center. Reminder, I just opened it. In the
very beginning of this talk seems to be

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the dominant one, then there are quite a
few data gaps. So take for instance. Well,

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it's very apparent here for the violet
one, the Furthstraße-Parkdeck, this one

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here. And that's an extreme case. It had
obviously some kind of problem. It was

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open for some time and then closed for
some other times. Typically, park houses

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or parking decks are either open 24/7, but
there are also quite a few that are that

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close overnight. OK, next I was interested
in the statistics of parking demand for

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individual parking decks, so I
concentrated only on, say, one parking

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deck and computed the histograms of the
used parking spots also, depending on the

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time. Let's focus here on the Oberstadt,
it's the old town and you can see that the

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overall parking demand peaks at around,
let's say, maybe 20 used parking spots, so

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that's the average, but that's not for all
times when we make that statement,

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depending on the time, for instance, the
morning we can see that's approximately

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the same. But when we go towards noon, we
can see that the number of parking spots

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or used parking spots increases. There are
even a few times when it's at the maximum

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around noon. Now, when we go towards later
hours, the maximum shifts towards smaller

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values again. Now, this this behavior of
the maximum shifting, so clearly,

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depending on the hour, is not not apparent
for all the parking decks. For instance,

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the Parkdreieck here ... Marktdreieck,
sorry, that doesn't show the signal as

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clear as the Oberstadt one. OK, from this
all now we can quantify also the, I call

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it integral parking demand, simply it's
the the number of parking spots that have

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been provided per parking deck. Now the
picture here, it's normalized to the

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maximum and one can see from this picture
here very easily that the Erlenring-

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Center, as we've estimated or guessed
previously already is the one that's

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dominating the whole city. It's providing
the most parking spots by a large margin,

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actually. The next one is the Lahn-Center
and then maybe the Oberstadt and the other

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ones follow after these. Another
interesting point here is that the

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proportion of parking spots provided on
weekends differs for the different parking

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decks. For instance, here you can see this
one here is quite a big portion, the

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Erlenring-Center, also on weekends.
Contrary, the Marktdreieck-Parkdeck has

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only a very small portion of, um, of
parking spots provided on weekends. It

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might be interesting to know that this
particular parking station is ... it's the

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one that is used if you want to go to a
doctor, because it's very close. So many

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doctors are not open on Sundays, on
Saturdays, and therefore probably the

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parking demand is quite low. Now, there's
a temporal version also where I rendered a

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small video that I'm opening now, and you
can see essentially the same as in the

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previous graph, but against time. Again,
it's very apparent that there's a

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periodicity and here my scraper crashed
and it's back in business again, and I

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found it interesting to see that there are
parking decks that have cars... well that

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host cars, even at night, for instance,
here the Erlenring-Center again in the

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Lahn-Center, the ones that are the largest
one, they offer parking also overnight.

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And there are some cars in there,
probably. OK, let's close that again. Now,

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I come lastly to the prediction part now.
The goal here is to measure the parking

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demand through the parking decks, but then
to interpolate between the parking decks,

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so I would like... so I have ...say the
Oberstadt the old town and the, I don't

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know, the Erlenring, which was the largest
one. I would like to know what's the

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parking demand in between, for instance.
For doing so, I use a spatial fit and I

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use a machine learning model for that, in
order to do that spatial fit. It is now,

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in this particular case, a non parametric
model called Gaussian Process Regression.

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And the nice thing about that is that it
also returns the uncertainty. Because say,

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for instance, you would like to use these
model, machine learning predictions to

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say, build some kind of parking deck or to
get rid of one. All these operations, all

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these derived actions would be very
expensive. So you would like to know if

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the uncertainty is large or small for
whatever the machine learning model

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predicts. Just for the math oriented
people. If you're interested in that

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model, definitely take a look at the, I
would call it, Gaussian process bible by

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Rasmussen. It's amazing to read. Yeah,
there are two, um, evaluations now, I did.

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The first one is based on the whole data
set, so there's no spatial or..sorry...

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there's no temporal resolution. And what I
do, I did well, I rrendered a video and I

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would like to explain you the outcome of
that while it is running. The top picture

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here shows you the prediction by the
machine learning model. And the the bottom

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picture shows you the uncertainty. The
training data, meaning the parking decks,

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is denoted by the black points. Now, first
of all, the uncertainty, you can see that

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wherever there is training data, the
uncertainty goes down. So the model is,

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um, certain about its prediction that
because, well, there's training data and

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in between the uncertainty rises again.
Now the prediction, you can see some small

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hill. It's exactly the Erlenring-Center,
which was the largest one. Now, what is

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shown in the video is it's rotating. You
can see the coordinates of Marburg on the

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on the plane, on the bottom plane. And at
some point, the view rotates upwards and

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gives you a top down perspective with a
corresponding color bars or corresponding

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color map. So, again, here's the the
maximum, the Erlenring-Center. And I did

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that because next we would like to finally
measure the parking demand between

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stations. OK, there's another small video
again, and now we start right from the top

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down, color coded view and again, the
black points are the... is the training

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data, but now the red points are, is kind
of test data, meaning positions in

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between. I concentrated now on the Mensa
because I have a special relation with the

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Mensa, the physics department, the
university library, the train station and

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the cinema. And just to demonstrate from
this spatial fit, we can derive the

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parking demand at these positions also.
Here, this yellow pike, it's the

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Erlenring-Center again. Now, that's only a
qualitative result, of course, I don't

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want to derive any quantitative at this
point, it's just a proof of concept that

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it is possible to derive something like
that from the publicly available data.

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Now I forgot to mention the beginning that
there's a bonus and I would like to come

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to the bonus now. It is about the Corona
crisis or pandemic, of course. What I did

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is, the initial data acquisition phase,
here in black, that's the whole talk was

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about that black portion here. I stopped
it at around the end of February and I

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restarted the whole data acquisition
process now again at in approximately

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April. Just to capture something from the
Corona crisis as well. And you can see

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here again, the time series. I think the
most interesting bit about it and the most

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comprehensive bit is the the mean . You
can see the the mean across the whole time

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denoted by this dashed line. And you can
see that the mean is smaller. So during

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the Corona pandemic fewer people parked in
Marburg, which is reasonable, I would say.

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But there are also times where the number
of parking spots decreased significantly.

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So for instance, right when the Corona
crisis started in April and now the second

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wave in October, November, December, it is
visible that the parking demand decreased

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a lot. And I went one step further and
wanted to know the the differences between

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pre Corona and during Corona also for each
of the parking decks, that's what I did

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here. It's now not the normalized parking
demand, but the absolute parking demand.

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So now we can see also the absolute
numbers, the black black bars you've seen

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previously already. Now the red bars is
for the during the Corona crisis. And then

00:21:16.440 --> 00:21:22.340
I defined these, the first wave and the
second wave as serious corona times. So I

00:21:22.340 --> 00:21:25.831
also plotted a third bar... set of bars
here. And it's interesting to see that

00:21:25.831 --> 00:21:32.730
while most of the parking decks, of
course, suffered in terms of providing

00:21:32.730 --> 00:21:39.630
parking demands or most of them provided
fewer parking decks, parking spots. But

00:21:39.630 --> 00:21:44.119
there are a few, like, for instance, the
Marktdreieck-Parkdeck here that, well,

00:21:44.119 --> 00:21:49.710
almost increased. We can see during the
corona in general it increased a bit. And

00:21:49.710 --> 00:21:54.190
then during the heavy corona, it increased
even more. And as I mentioned before, this

00:21:54.190 --> 00:22:00.329
is the parking deck that corresponds to,
yeah, a whole collection of doctors. So. I

00:22:00.329 --> 00:22:07.220
derive that well during Corona times the
parking demand in front of doctors even

00:22:07.220 --> 00:22:13.369
increased a tiny bit. Yeah, with that, I
would like to come to my conclusions.

00:22:13.369 --> 00:22:18.100
Thank you for sticking with me until now.
So I scraped publicly available data here

00:22:18.100 --> 00:22:26.840
with a small scraper set up. I analyzed
it, for instance, for day and hour

00:22:26.840 --> 00:22:30.889
patterns. And last but not least, did some
machine learning in order to quantify the

00:22:30.889 --> 00:22:38.779
demand in between the stations, there is
an accompanying blog article also. You can

00:22:38.779 --> 00:22:42.169
find it down here, there all the figures
in higher resolution and you can play

00:22:42.169 --> 00:22:47.519
around with an interactive map also, if
you like. Um, and to finally now conclude

00:22:47.519 --> 00:22:51.419
the presentation. I would like to hear
from you what you think about this

00:22:51.419 --> 00:22:57.460
analysis. I'd like to improve with these
kind of mini studies. And therefore, I

00:22:57.460 --> 00:23:03.059
would be very interested in your critique
regarding the content, the presentation

00:23:03.059 --> 00:23:08.019
and general content... comments. Again,
you can email me to this email address

00:23:08.019 --> 00:23:15.919
here, or alternatively, I set up a Google,
um, Google form. So the Google forms

00:23:15.919 --> 00:23:20.509
document that exactly comprised of these
questions, and you can simply type them in

00:23:20.509 --> 00:23:24.919
if you're interested. Thank you very much.

00:23:24.919 --> 00:23:29.559
Herald: All right, first of all, thank you
for this amazing talk, I have a few

00:23:29.559 --> 00:23:33.940
questions what have been relayed to me and
I'm just going to ask them one after the

00:23:33.940 --> 00:23:39.070
other. And let's not waste any time and
start with the first one. Have you found

00:23:39.070 --> 00:23:46.889
parking decks that are usually heavily
overloaded or never completely used?

00:23:46.889 --> 00:23:56.029
Martin: Um so. Given that there are only
around what was it, 8 or 9 or 10 in the

00:23:56.029 --> 00:24:05.159
data set, honestly, I never looked for for
that question. So, um, short answers is:

00:24:05.159 --> 00:24:10.779
No. Long answer, yes, I could have or I
still could, I would say.

00:24:10.779 --> 00:24:16.980
H: OK. Have you tried prediction in time,
so guessing which parking decks will be

00:24:16.980 --> 00:24:21.700
exhausted soon?
M: No, no. So that's obviously it's

00:24:21.700 --> 00:24:26.750
like... it's... I would consider that
something like the predictive maintenance

00:24:26.750 --> 00:24:32.929
of traffic business kind of. It's
definitely a thing that people that have

00:24:32.929 --> 00:24:38.409
more time and more are willing to invest
more definitely should do and could do. I

00:24:38.409 --> 00:24:43.769
would say I mean, there's lots of lots of
additional data that might be of interest,

00:24:43.769 --> 00:24:49.369
like weather data. And, for instance, is
it a is it a public holiday, yes or no and

00:24:49.369 --> 00:24:55.580
all that kind of stuff. So, again, short
answer.: No. Long answer. Yes. Would be

00:24:55.580 --> 00:25:00.919
possible.
H: OK, so if anyone watching has the time

00:25:00.919 --> 00:25:05.710
or energy to do that, they could.
M: Absolutely. Yes.

00:25:05.710 --> 00:25:10.649
H: OK, and the last question I have right
now is, will the code or especially the

00:25:10.649 --> 00:25:16.509
scraping part be available publicly or
like in the GitHub or somewhere?

00:25:16.509 --> 00:25:24.580
H: Um, I could do that. So I was very I
was quite hesitant with it. So obviously

00:25:24.580 --> 00:25:29.840
publishing the data could be problematic.
I have no experience with it on the legal

00:25:29.840 --> 00:25:34.150
side. So I would probably not publish the
data, which is I mean, it's old data

00:25:34.150 --> 00:25:41.039
anyway. So and but then regarding the
code, I was just waiting if anybody's

00:25:41.039 --> 00:25:45.109
interested. So given that somebody stated
the interest, I would probably publish it.

00:25:45.109 --> 00:25:49.639
Yes.
H: OK, yeah I think that's it from the

00:25:49.639 --> 00:25:52.179
question side .
M: Hmhm.

00:25:52.179 --> 00:25:58.559
H: And they were all answered quite
nicely. And judging by that, I don't get

00:25:58.559 --> 00:26:04.809
any more questions right now. So, yeah, I
would conclude is talk. Maybe you can also

00:26:04.809 --> 00:26:08.369
like have a last word. From my side I'm
done here.

00:26:08.369 --> 00:26:14.779
M: Yes. So, um, well, thank you very much
for watching the talk. And I try to

00:26:14.779 --> 00:26:19.600
improve. I think I said it on the last
slide. If I'm right, let me know if you

00:26:19.600 --> 00:26:26.109
have any doubts or things to improve
essentially on. And then regarding maybe

00:26:26.109 --> 00:26:31.720
the last question of publishing it, I
believe that I put a link there to find my

00:26:31.720 --> 00:26:37.700
blog and I would probably just add another
blog post stating well there's an github

00:26:37.700 --> 00:26:42.240
repository. You can go there and just find
just find the code and stuff like that

00:26:42.240 --> 00:26:48.260
there. So if you're interested, just, you
know, find my website. My name is Martin

00:26:48.260 --> 00:26:55.690
Lellep. Um, and then you will in a few
days, I guess probably in 2021 only. So I

00:26:55.690 --> 00:27:00.080
won't be able to publish it in the next
two days. But then the code will be

00:27:00.080 --> 00:27:06.049
public. Yes.
H: OK, then. Have a great day. Great time

00:27:06.049 --> 00:27:08.740
at Congress and byebye.

00:27:08.740 --> 00:27:10.810
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