Optimising public transport: A data-driven bike-sharing study in Marburg

RC3 preroll music

Herald: Hello, everyone, welcome back to
Chaos West TV. The next talk will start

momentarily. I will now switch back to
German for a few seconds to announce a

translation. Then I'll switch back and
then we'll go off to the races as they say

So nochmal schnell auf Deutsch,
willkommen zurück zu Chaos West TV, eure

beste Bühne auf dem rc3. Der nächste Talk
beginnt gleich er ist zwar auf Englisch

wird aber wie so vieles dank unserer
Übersetzungscrew auf Deutsch übersetzt.

Ihr solltet in der Lage sein das im Stream
einfach auszuwählen ohne größere Probleme

und dann könnt ihr den Vortrag auch direkt
simultanübersetzt auf Deutsch hören

und ich rede jetzt auf Englisch weiter.

Alright back to English.

Now in the comfort of your own homes
or wherever you're viewing the stream,

please do a warm round of applause
for our next speaker,

Martin, who will talk about
optimizing public transport.

Let's go.

Martin: Welcome to my contribution to this
year's rC3 2021 in the form of this talk,

Optimizing public transport:
a data-driven bike sharing study in Marburg

I would like to thank the organizers of the
rC3 2021 for organizing the whole event.

And in particular, I would like to thank
the channel that accepted me Chaos West TV

well for accepting the presentation of my
work. Today I would like to give you a

quick overview of one of my hobby projects
in which I scraped and therefore

downloaded over one million data points
regarding the bike sharing system in the

city of Marburg. This study came about
when I was traveling from Stuttgart to

Frankfurt and ultimately to Marburg some
time ago, and I was watching the amazing

SpiegelMining talk by David Kriesel. So
thank you very much for this implicit

inspiration of the work that you're about
to see now.

Who am I? My name is Martin Lellep,
and I studied physics in the past,

and actually, I continue to do so in the
form of a Ph.D. in theoretical physics at

the University of Edinburgh in Scotland
and in my spare time I like to do data

analysis of all kinds of data.
There are two more things...

There are two more things
that are important for here now.

It's first of all, I studied at the
University of Marburg, obviously in

Marburg previously, and then also I like
to ride my bike. Marburg, for those who

don't know it yet, it's a small,
university dominated town that is in the

north of Frankfurt am Main, roughly 80
kilometers. So an hour by car or an hour

by train, approximately. And again, it's
quite dominated by the university that is

located there, and that can be seen simply
in terms of, for instance, numbers. There

are roughly 25,000 students for an overall
population of 77,000 residents in total,

which is quite substantial, obviously. You
can see a quite popular picture here of a

picturesque scene in Marburg. We can see
the castle and then the river Lahn, as

well as a few houses and a bit of green.
And the bike rentals are currently

provided at the time of recording this by
the company called Nextbike. Before now

diving into a bit more technical details,
I would like to motivate my story or my

study by the story of Anna. Anna is a
university... is a university student at

the University of Marburg, and she lives a
bit outside the city, so she typically

does not walk to the place that she needs
to be or study at. But she takes the bus

from her... from her flat to the
university, to the city. And then does the

last mile by walking or cycling or
whatever. And she's also quite an eager

student, so she very often studies quite
late. As you can see here, that's a

picture of late Marburg, so to say, and
just as it happens now, she needs to catch

a bus now because she's a bit late. She
forgot to pack in her... her fancy MacBook

in time, so she needs to hurry up a
bit and, well, didn't really make it. So

therefore, she thought maybe

taking a Nextbike for the last mile
to the bus station is a good idea

so she can safely take then subsequently
the bus home. And normally the bus…

The Nextbike stations look like
that here. So there are plenty of bikes.

It's very easy to go there, grab a bike
and go to your destination. Now Anna must

be a very unlucky student today because
she arrives at the bike station, and it

turns out that the station is empty, so
ultimately she misses at least this bus

and therefore only arrives at home a bit
later. Her cooking plans and her Netflix

plans, all that stuff postponed a bit
because, well, she arrives a bit later.

And that's, of course, a very, very sad
story, and maybe it happens to multiple

people, not only Anna. And in fact, it
also happened to me a few times, and every

time it happened to me, I thought, well, I
must be the most unlucky person in whole

Marburg going to a normally completely
fully packed bike station and now it's

completely empty. Missing, for instance,
subsequent public transportation.

After it happened to me a few times, I
thought, well, maybe I'm not that unlucky.

So is there may be a system to empty bike
stations in Marburg. And given all my

my spare time interest of analyzing and
capturing data, I thought, well, data to

the rescue, of course. And therefore, the
idea for this talk now was to build a web

scraper in order to acquire Nextbike data.
Collect the data, store the data, analyze

the data and then hopefully finally help
Anna, me, and other students to figure out

which stations maybe to avoid and which
stations are safe to go to if you're in

desperate need for a bike.

The tech stack that I'm using here,
it's based on a Docker container

in which a python scraper runs
every 30 seconds that queries the

Nextbike API. It downloads the data, it
parses the data, and then saves the data

outside the Docker container in order to
be evaluated later on. And it turns out

that the whole concept of what I just
described also has a name. It's called

Extract, Transform, Load Pipeline or ETL
in short. And what I again wrote here is

an ETL pipeline in Python, and then I
wrote an analysis code also written in

Python and all that was running on a small
home server in my flat. The data that I

captured consists of the bikes identified
through IDs and then also the locations of

those bikes, typically at stations, but
some of them were also freestanding and

last but not least, the station locations,
and of course, obviously also a list of of

stations. And then with it, I went ahead
and did a few pictures that I'm about to

show now and a few analyses. And if you're
interested in that and there are slides

available on this website here, the
website can be read through the QR code or

through that link and this website
contains the slides that you'll see in

here, high resolution figures, a few
interactive figures and all the

information on the previous blog articles
that I wrote about this topic.

So the results of Anna, first of all, to
start slowly. It turns out that there are

37 bike stations in Marburg,
with roughly 230 bikes spread across

the whole Nextbike Marburg ecosystem.

And it's now, well, knowing that
there are roughly 40 stations,

it's quite interesting to see
where these stations are,

because then Anna could,

for instance, already go to another
station if one station is empty.

And what you can see here is now a map
of Marburg, where the stations are

annotated by these dots.
And the area of the dot,

as well as the color code,
corresponds to the average number of

parked bikes at that station. So let's see
an interactive version because it's a bit

nicer to see it in that way. So I click on
here. Alright. OK, now we can pan around

and zoom as you can often do with these
interactive graphics and also by clicking

on these buttons or on these these points,
you can see the station name, as well as

the average number of bikes placed there.
And becomes quite obvious that, well, most

of the stations are in the central part of
the city, a few in the outskirts here. And

it turns out that the largest station in
terms of the number of parked bikes on

average is the main train station
Hauptbahnhof. There are again a few more

spread around the
central part of the station,

such as the Elisabeth-Blochmann-Platz,
which is the second largest station.

And then if you continue the train
line here, you can see that there's

actually another set of stations, where
the secondary train station is.

So that's another train station,
smaller train station.

OK, so the first results for Anna
would then be a day-hour usage histogram,

because it's the kind of the first order
approach, I would say, in order to see how

the ecosystem of Nextbikes is in use
against day as well as hour. And therefore

Anna will based on this figure here, she
will understand when to maybe plan for a

bit more time when looking for a bike in a
desperate fashion. And since this figure

is a bit more difficult to understand, I
would like to take a moment to explain it

and we are going to start with the top
figure here. What you can see on the x

axis is the hour of the day and on the y
axis, and that's shown in the whole

figure. So each of the the numbers that
you see is the following: it's the

average. And well it's the number of
parked bikes and then you subtract the

average of the number of parked bikes in
the whole ecosystem of Marburg. So that

means if a number of zero is encountered
like roughly here, it means that the

average number of parked bikes simply in
the system at that point in time. When the

number is larger, it's above the average,
if it's smaller, it's below the average.

And you can clearly see from this small
figure here already that in the morning,

more bikes are typically parked. And then
in the evenings or around noon, you can

see two dips, a bimodal distribution so to
say. Where people, well, obviously use

bikes around noon and six p.m. roughly
where these used bikes, of course, are not

parked, and therefore these numbers are
smaller. And the same thing can be done

for the day of the week. Here and here you
can see that the Monday, well, the

beginning of the week and the end of the
week, meaning Monday, Tuesday and Saturday

Sunday are a bit more popular, so more
people ride a bike and therefore fewer

bikes are parked and therefore this is
negative. And then in the middle of the

week, fewer people seem to ride the bike,
the bikes in general. And if you combine

these figures now, you can see the the
joint histogram here, where you can not

only look for time or day separately, but
also in a combined fashion. So you would,

for instance, see that Monday morning is
the time where many people use bikes

because they are not as many bikes parked.
And then also on a Saturday, you can see

the same, so around afternoon many people
seem to use the bikes. Last but not least

on Friday mornings, it's quite easy to get
a bike because many bikes appear to be

parked, maybe because people envision
already the weekend. So that's the first

outcome for Anna. Well try to avoid times
around six and around noon when

desperately looking for bike. And although
even more interesting part for Anna is the

probability to find a specific station to
be empty. For that, I took the time series

of the number of parked bikes and counted
the occasions where there was no bike for

each of the stations here. And that has
been done again for each station

separately, so for each station, at the
end of the day, you get a number that

denotes the probability of finding that
station empty. And clearly, for instance,

the Hauptbahnhof, the main train station,
which was the largest station. It's

quite unlikely to find it empty,
and contrary, if you go to these

stations down here, for instance
the Am Plan / Wirtschaftswissenschaften

it turns out that these are empty at about
70 percent of the time, which is quite

substantial, I would say. And
interestingly, if you now look for the the

secondary train station in Marburg, the
Südbahnhof, you can see that this has

quite a substantial probability of
running empty at about 30 to 40 percent.

In particular, in comparison to the main
train station, which is essentially almost

never empty. Also interestingly, you can
then plot these probabilities against the

average number of parked bikes at the
station and you find an antiproportional

relation between those two. It means that
the larger the stations, the more unlikely

it is that it's empty, which is quite a
reasonable outcome, I would say.

So finally, to conclude for Anna,

she should try to avoid small stations

and in particular, she should try
to avoid the stations that are

well, annotated here with
the sad smiley, because these

tend to run empty quite often.

OK, so I have all this ETL pipeline
stuff already set up,

I have collected
over a million data points

and then I thought, well, maybe there's
more in the data then only helping Anna.

So everything that I've shown you so far,
it's from the perspective of a user.

And now I would like to turn to
what's the perspective of a city.

And there I would like to
ask a few questions, like…

How is Nextbike used in Marburg?
first of all,

and then, in general,
Is cycling a good thing for a city?

How can, or,
Can cycling contribute to a better city?

And now–better is of course first a quite
vague term–and then last, but not least,

is it worth improving
bike infrastructure for a city?

And all this again, is now from the
perspective of a city instead of a user.

The first thing that I would like to start
with is something that I call the distance

matrix in which I concentrated on the
positions of the bike stations and

computed the pairwise distances for all of
them. And since the distance is, of

course, symmetric, also the stored matrix
is now in the end also symmetric. And,

It turns out that there are roughly 600
combinations, and these combinations can

be shown in a symmetric matrix, as shown
here, where on the x axis this one here

and the y axis you can see the stations
and then each combination denotes

the distance between that one station and
the other station. It turns out that the

range of these distances is between zero
and roughly nine kilometers. And of

course, those that have a zero distance to
other stations are essentially the…

the stations themselves. So if you pick a
station, obviously the distance to itself

is zero and therefore the diagonal is
exactly zero. And then again, all the

remaining part is a symmetric copy of the
other diagonal part. The other thing and

that is now the main treasure, I would say
of this study, so the main base for

everything that follows is what I call the
transition matrix, where I counted the

number of transition of bikes from one
station to the other station. That is now,

of course, not symmetric anymore because
just because, say, five bikes go from one to

the other station, it does not mean that
these five bikes really come back again.

And therefore, the number of entries
is roughly 1400. Again, it can be shown

or visualized in the same fashion.
So you again have the stations on the one

axis and the same stations on the other
axis, and now each entry here in the

matrix corresponds to the number of
transitions of bikes from one to the

other. And the range is from zero to over
3000. And it turns out that actually the

self transitions, meaning somebody takes a
bike from a station, does something with a

bike, maybe grocery shop, grocery shopping
or so, and then the person comes back to

the same station. These events occur the
most frequent and therefore the largest

entry are on the diagonal, typically.
Sometimes it is not so interesting what

happens regarding the self transitions and
therefore another matrix can be derived

from the first one, namely a transition
matrix without diagonal elements where

those elements have been set to zero as
you can see here, if you look closely.

Speaking of looking closely, it's quite
educational if you not only see the

figures, but also can explore them a bit,
and therefore I rendered an interactive

version of it. Let's... let's visit it. So
that's now again, the matrix without the

diagonal and one with the diagonal. And
now by hovering over these entries so you

can see that, for instance, from Am
Schülerpark to Ockershäuser Allee zero

transitions happened. And then a bit
larger one, for instance, Biegenstraße to

Hauptbahnhof over 800 transitions happened
in the time of capturing the data. So feel

free to explore a bit, maybe identify the
most, most interesting, most used popular

routes. Ok, such a transition matrix can
actually also be shown as a network graph

where here I concentrate only on the
largest entry because it turns out the

full transition matrix is a bit too dense.
And what is shown out here is as blue

circles, it corresponds to a station and
then these edges here are drawn whenever

there happens a transition. And you can
already see here that there are a few

stations that are quite isolated, like
those and then many stations have a self

transition and mostly feed to a more
central station.

And since that is also more
interesting in an interactive fashion,

I also rendered
an interactive version of that.

Now again, we can zoom, pan around
and drag the graph around a bit.

And interestingly, if you click on a
station, you can see from where

transitions happen to that station. So
like those interconnected central ones,

like the Hauptbahnhof, the main train
station, it's quite connected in the

graph. And then there are a few like
Friedrichplatz which are not connected at

all. Interestingly, that one here, for
instance, the Cafe Trauma/Aföllerwiesen it

doesn't even have a self connection. So it
turns out that, well, people apparently

mostly use it for taking a bike going into
the city.

And most dominantly,
the Elisabeth-Blochmann-Platz, actually.

OK, so if you now take
these transition matrices,

as well as the distance matrices
into account and mix them, first of all,

you can get a few interesting numbers. So
here I calculated the overall number of

trips, which turned out to be 210,000
trips in the time of capturing the data,

which is quite some essential number for
such a small city like Marburg. And this

is, of course, computed by taking the sum
of the transition matrix elements. And

then if you weigh these sums or these
entries with the distances between those

stations, it turns out that those
transitions or those trips essentially

correspond to a distance of 320,000
kilometers that have been traveled, which

is a few times around the Earth actually.

Now, when these two basic numbers and the

the matrices that I introduced earlier are
combined with a few statistical details –

like, for instance, the average
consumption of fuel of a car or how much

CO2 it produces while driving – a few
ecological, economic and social benefits

of a bike system or cycling in general can
be derived. First of all, I found it quite

entertaining that the overall number of
calories burned corresponds to 8.6 million

kilocalories. And to convert that to a bit
more, well, real life number, I would say

I calculated how many Nutella jars
those are, and it turns out that

it's roughly 4,000 Nutella jars that
have been burned in terms of calories

just by this system of cycling. And then
also, it can be found that this distance

here, if you would have driven it
by a car, you would have,

well, used almost 26,000 liters of fuel.
You would have produced 40 tons of CO2.

And that fuel that you would have bought
would have cost 34,000 €, actually.

Interestingly, that number here
of 40 tons of saved CO2

corresponds to an average
German who lives for 4 years

or 4 Germans that live for one year.
So a typical German produces

roughly 10 tons, and therefore
it's four times that, obviously.

Ok, so again, from the transition matrix,

you can derive a few more interesting
details like, for instance, details that

are interesting from the perspective
of traffic management.

Like, here I calculated the most popular
routes by finding the maximal elements

of the transition matrix. And it turns out
that the most popular route has been used

well over 2000 times a year from the
Hauptbahnhof to the Ginseldorfer Weg. And

if you look closely, you can see that the
main train station or the Hauptbahnhof,

as well as the Elisabeth-Blochmann-Platz
is involved in many of those top row routes.

And that's now again interesting. For
instance, if a city would like to improve

the bike system because we've now seen
it has quite a good impact for social,

ecological, and economical aspects.

But let's say the the city has maybe
limited financial resources.

It would be interesting to simply
calculate the most popular routes,

and then start fixing
or improving them first.

OK, now at that point,
you might ask yourself,

Well, what kind of data did he scrape?

And for that, I would like to
show you this graph. It shows

the number of parked bikes in the whole
ecosystem of Marburg against time.

And as you can see,
I did it in two batches.

The first one has been obtained from
March to December 2020. So last year.

And then I restarted the scraping at the
end of April and finished just a few days

ago in December 2021. And you can clearly
see that the number of parked bikes

decreases when the weather is good or when
there are summer months and therefore most

likely because the weather is good. And of
course, it suggests itself a bit given

that I captured this in 2020 and that one
year in 2021 and taking the corona

pandemic into account. Well, how does it
compare?

And therefore, I concentrated on the
overlapping month of the two data sets

and calculated, well,
the comparison, as you can see here.

Now in blue, it's 2021 this year
and 2021, sorry 2020 is shown in red.

And you can see that the number of
parked bikes increased actually.

There might be a multitude
of explanations for that. I don't know.

Maybe one explanation could be that people
took more advantage of working from home.

OK, so everything that I've shown you so far,

it's been mostly statistical statements,
averages, sums and stuff like that,

and now I was interested if it's possible
to do also more precise predictions.

And therefore I turn
towards a machine learning or

artificial intelligence task where I
predicted the num… where I tried to

predict the number of parked bikes,
meaning the quantity that I've shown over

and over again in the in the last few
minutes. So is it possible to predict that

number based on the hour of the day, the
weekday and the temperature that is shown

here for 2020? And when starting such a
task, it's always, first of all, very

useful to investigate the training data.
And therefore well I try to plot it. And

And because it's a three dimensional face
space, it's also very simple to plot it.

So you can essentially plot it as a
scatterplot. And the color coding here has

been chosen to denote the target variable,
meaning the number of parked bikes.

And just by inspecting the data, you can
already see that the smaller the

temperatures are, the fewer… sorry, the
more bikes are parked and therefore the

fewer bikes are used. I use a random
forest machine learning model, which

consists... which is an ensemble model of
decision trees, of randomized decision

trees. And this model is quite powerful
because it can work with little data. It

can work with a lot of data, and it's also
very flexible. If you would ever like to

extend the face space, like maybe it would
be interesting to see if one could predict

the number of parked bikes given a bank
holiday or given weekend. And all these

aspects could be added to the random
forest relatively easily. And that's now

the outcome: So I show the measured data,
well that's been data that hasn't been

seen by the model before, and I show that
data here and then the densely covered,

face-based prediction of the machine
learning model here. And you can see that

the color trends, they correspond quite
well to each other. Like you can, for

instance, see the smaller numbers or
larger parked numbers in the regime of

small temperature and also from a
quantitative perspective, the prediction

is quite decent as the square root of the
mean squared error corresponds to a

roughly a tenth of the average value of
the parked bikes.

Which, again in this context is quite a
decent prediction performance,

given how naive the
approach was in general.

OK, I did a bit more on machine learning,
but I'm not showing that here.

I calculated the Markov steady state
for the same data essentially.

And if you're interested in that, well,
feel free to check out this link here.

OK, last but not least, I would,
of course, like to come to

the summary for Anna, me,
and maybe other students.

So first of all, what I did was to scrape
Nextbike data in Marburg in order to find,

which stations to potentially avoid when
you're in desperate need for a Nextbike.

And for that, I calculated
the probabilities of empty stations

and found that the larger the station,
the less likely it is to run out of bikes.

So the general recommendation
from my side would be:

try to find larger stations if you're
in desperate need for an Nextbike.

And feel free to go back to
the interactive map to see the

the locations of these stations, which is
quite interesting in itself, I would say.

And then I turned towards
the perspective of a city, and

investigated a bit the usage patterns
of Nextbikes and therefore representative

most likely also cycling in Marburg, where
I calculated the day-hour usage.

So when is the system quite busy
and generally the most popular routes,

which might be of use for city planning
and also social, economical, and

ecological benefits of the whole system.

Last but not least, I showed that

more precise predictions are possible when
maybe a statistical statement is not

enough and you would like
to do per case predictions.

Last but not least, I was fortunate
enough to work with AstA Marburg.

In particular, Lucas and David,
thank you very much for your trust

in that project where we try to optimize
the placement of the bikes in the future.

The take home messages are now,
first of all:

Bikes are amazing! And not only are they
amazing for you and the environment,

but also for your wallet.
So you save essentially money on gas.

And also, I would like to,

well, highlight that those data-driven
optimizations of public transport

have the potential to, well,
increase the life, the quality of life of

many of us at moderate cost. So again, I
would like to come back to a case where

maybe a city would like to
improve bike infrastructure

that doesn't have enough
money to do it in one go.

So then it might be interesting
to first find–in a data-driven way–which

combinations of, now in Nextbike terms,
maybe stations or in general streets

are popular, and then these might be worth
being fixed first with a limited budget.

OK, if you're interested in more, I was
very fortunate to be able to speak at the

last rC3 already about data in Marburg,
but last year I spoke about parking

in Marburg. If you like to, well, read the
blog articles corresponding to that

or just see the official CCC video,
just follow these links shown here.

Thank you very much for your attention.

If you have anything to get in contact
with me, reach out to my e-mail address.

Maybe some ideas on how to improve
a talk or what else to evaluate.

And then all the supplementary
materials that I mentioned,

and what I've shown here,
can be found again on this link here.

In particular, thank you very much
to all the people who reached out to me

based on my last year's talk. I haven't
come about to respond properly, but

I'm 100 percent certain that I will do so.

Thank you very much for your attention,
and have a good year.

Herald: Alright, welcome back. It's time
for the Q&A now. You probably know the

drill, but I repeat it anyway. If you're
on Twitter, on Mastodon or on the

Fediverse in general, the hashtag is
#rc3cwtv to ask any questions. And if

you're in the hackint IRC, the channel
name is the same except there's a dash in

between the rc3 and the cwtv. And we
apparently already have some questions, so

I'll just get started now.

First question:
Is the Nextbike API free to use?

Does Nextbike even know
that you did this scraping?

Martin: Yes, so as far as I know, the
Nextbike API has been reverse engineered

from the iOS app and there's a Github repo
by ubahnverleih and he documents lots of

APIs of public transport companies like
Nextbike or some companies that also

produce the scooters. And since it's the
public, since it's the official iOS API,

it's more or less public, so to say,
it's free and it's pretty much quota unlimited

because normally all the iPhones
access it. But again, I can only recommend

the ubahnverleih repository
on that on Github.

Herald: And you don't need
any credentials to access it?

Martin: No. Actually, you can, as far as
I checked, you can pretty much access the

whole world. So you can access stations
in Poland in, well, all of Germany now.

Herald: That's cool. It's probably
accidental, but it's quite cool anyway.

Martin: laughs Yeah.

Herald: Ok. What software did you use for
the machine learning stuff?

Martin: The machine learning stuff
has been done with Python,

and then specifically with sklearn,
which is a quite popular machine learning

framework for Python.

Herald: The working horse of the machine
learning community, I would say.

Martin: Yes, exactly yeah.

Herald: Do you know if the Nextbike adds
or removes bikes from the stations?

Or do they relocate the bikes?
Or do… I mean, do they do that?

Or does it just happen
as an emergent behavior?

Martin: I would say that…
So, I had the chance to speak

with a person of Nextbike while
I was working for the Marburg-ASTA

and he said that first of all, it's not
not very technical yet. Well, not very

digitalized yet, and they essentially
drive around. So I'm pretty sure that they

certainly collect bikes that need
maintenance, but then logically,

logically, probably also
relocate them where necessary.

Herald: All right. OK, someone wants to
know if the scripts that you use would be

public? I assume the main part with the
API is already answered if you gave the

Github repo. But are you planning to open
source anything else?

Martin: Potentially so I have no plans on
doing so just because it's additional

work, to be honest. If you're… well, I
can just do the same, well offer the same

same thing as last year: Just write me an
email and if there's enough people who are

interested, I probably strip down to my
internal repository. But since in the

internal repository there are a few
private notes, that one is not published

for sure right now.

Herald: All right. Anything else?

Dear listeners,
you have maybe 30 seconds to comply.

So there's one question, about
the time period of data that you have,

but I think you answered it in the talk.
Right?

Martin: Yes, it's more or less whole 2020
and 1/2 to 2/3 of 2021 that I collected.

Herald: OK, so you're probably mostly has
like a pandemic situation?

Martin: Yes, exclusively.
Pretty much, yeah

Herald: I wonder if that's more or less
usage than usual. I mean, it's less people

having to go places, but more people
wanting to not use public transport.

Martin: Yes, so based on my data,
I can see that it's

the number of parked bikes and
therefore the usage is going down, so

the number of parked bikes is going up.
Therefore, the usage is going down and

that was also confirmed internally by some
Nextbike people. Now, one more thing, so

regarding the people who are interested in
the code, regardless of if I am going to

publish it or not, they if you have
questions, just drop me an email. I mean,

the writing, the scraper in particular,
it's it's absolutely trivial. And if it's

not trivial for you, then the code
wouldn't be of of value to you anyway.

Herald: All right. How does your data
interpret broken / unavailable bikes at

the station? I mean, can you see that?
Or do you take it into account?

Martin: Yes, so I don't see directly.

I mean, I have a list of of all the bikes
and if I would dig a little bit deeper,

I could probably, you know, compile a list
where I see where the bike, where a

particular bike is standing at the moment.
And if that bike would be, for instance,

absent for a for a longer time, I could
conclude that it's maybe broken,

maintenance, maintained or something like
that. But there's no direct data on that.

Herald: All right. Do you do you think
that Nextbike moving the bikes has somehow

biased your data.
Like if basically relocate them?

Martin: That's a good question. I have
absolutely no idea. So I mean, what I what

I did calculate was that, so I defined a
term that I, a term of activity,

I defined it as the number of bikes coming
in, divided by the number of bikes going

out, plus the number of bikes going in. So
it's so to say the activity and when

that number - it's obviously between zero
and one - and if it's far from zero point

five, that would mean that the station
runs empty essentially or overfills at

some point and there are a few stations
where it's a bit above zero point five.

But of course, that's only this well, the
the data that I used has all only the

moved bikes incorporated already. So it's
not really something that could be used

for really trying to find it.

Herald: Do you, I mean, is this just kind
of data also available for,

for bike sharing services
that don't have docking?

If they even exist still in Germany?
I kind of lost track.

I think maybe they
all went bankrupt, but of course…

Martin: What do you mean by docking?

Herald: By, you know, they don't have
fixed stations, but they are floating.

Martin: So I mean, all that I did was to
look at the stations, but actually there

are a few free standing ones also in
Marburg, and these people are typically

penalized, penalized by money, so they
have to pay, pay a fee. I didn't analyze

it at all. Would be interesting for sure.
And as far as I know, there are cities

where it's completely, well, there are
no stations for Nextbike,

where people can drop it off
wherever they like.

Don't quote me on that, it's
just something that I've heard.

Most likely in the large cities.
So maybe in Berlin could be.

Herald: Yeah, I think here there are like
some locations where you have to drop the

bikes, but that's,
I'm not sure if that's Nextbike.

I can never remember which ones
laughs I actually end up using.

All right, everybody. Now is your last
chance to ask more questions.

I feel like at Teleshopping, like the rC3
Teleshopping, which I highly recommend if

you haven't checked it out. It's probably
the peak experience at the remote Congress

is the Teleshopping channel.
And you should all have a look.

And maybe buy some…
some extremely useful items that they sell

Herald: OK, so the chat confirms that
Nextbike does have cities without stations

Martin: Ah ja ja, very good.

Yet, I mean, I can only…

if you're remotely interested in all
these public transport data studies,

definitely check out the
ubahnverleih Github repository.

There's a large number
of systems documented there.

Herald: OK, and that's just ubahnverleih,
just as you would write it.

Martin: Yes, let me look it up
very quickly, Ubahn…

Well, the person is from Ulm,
and he also contributed to the

CCC infrastructure. His name is
Constantine and yes, it's ubahnverleih.

And I think it's like, I think the repo
name name is WoBike, as far as I know,

Herald: All right. Good. Thank you.

Alright. I think we've managed to exhaust
the internet. So, people, where can they

find you have to have any further
questions? Are you going to be wandering

the remote, the world or what it's called?
You know the…

Martin: Well, that's a good idea. I
haven't planned, but I can. So I've no

idea how it works, but I'm sure I can
figure it out. So I mean, in general, drop

me an email and you can find my email on
lellep dot xyz. It's my website.

Other than that, I could be online
in the 2D world adventure now,

if that's of of value to anybody.

Herald: People can maybe hunt you
down if they really need to, you need to.

Martin: definitely ja.

Herald: OK, wonderful. Well, thank you for
your talk and for answering the questions.

And thanks everyone for tuning in.
Have a good remainder of Congress.

I think you should be able to at some
point rate talks in the Fahrplan,

if that feature still exists, so if you
want to see more of this kind of stuff,

maybe leave some feedback.

Bye bye.

Martin: Bye.

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