2. IMPORTANCE OF INVESTING IN DATA
UNDERSTANDING MOBILITY IS AN
EMPIRICAL SCIENCE
MOBILITY IS
GOING TO
CHANGE IN MANY
FUNDAMENTAL
WAYS THE
COMING DECADES
UML – URBAN MOBILITY LAB
Knowledge
discovery starts and
ends with data
Our assumptions
and ideas are as
good as the
evidence for them
The same holds for
mobility policies &
measures!
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datasources
voertuigoperations
kosten operators
voertuigdata (v,x,a) CANBUS
kosten gebruikers
RegioLab TU-Delft
lussen, floatingcar, car2go
NDW
VRI data
taxi
GPS
ov operations
ov chip
niet georganiseerd
publiek vervoer
trein data
GPS
sporen
vertragingen
GOVI
incidenten
events
data lokatiebezoek,
evenementen
buienradar
knmi
wegwerkzaamheden,
incidenten
social
social media pers media
event data
Sociale netwerken
twitter
facebook
lokalisering
mob telefoons (netwerk, apps)
gps
wifi
netwerk belastingen
(gemiddelden, pieken, dalen)
logistiek
vervoerders
container/pakket
logistieke bedrijven en stadsdistributie
vaarwegen
verplaatsingen
verplaatsingsonderzoek
A'dam OVIA/OVIN
multimodaal
lustellingen
vaarweggebruik
Amsterdamse
thermometer
bereikbaarheid
drivers
mobliiteit
effecten
parkeerdata
activiteiten patronen
tijdsbesteding
leefbaarheid
luchtkwaliteit data
actueel (RIVM)
prognose (KNMI)
tel/meetpunten GGD
meteo data
actueel
historisch
effecten
energieverbruik
platforms
citysdk app
NDW
reistijden
bezetting P&R
real time ov
etc...
Nederland van Boven
niet real-time
historisch
volledige
beschikbaarheid
Prime Data IT
buienradar
PPA
ALS data
RWS waternet
Visualisatie cockpit (TNO)
data uit projecten
(energie, afval, mobiliteit)
internet (google)
zoekmachines
gezondheid
luchtvervuiling
geluid
GGD
gezondheidswaarneming
veiligheid
politie
ambulance
verzekeraars
reisplanning
ov planners
weer/meteo
navigatie
economische
gegevens
CBS onderzoek
toerismecijfers
KvK
arbeidsplaatsen
werkeloosheidsgevens
criminaliteit
sociaal
demografische
informatie
fysieke omgeving
infrastructuur
ondergrond
gebouwen
wegwerkzaamheden
kabels en leidingen
tijdelijke
verkeersmaatregelen
kaartmateriaal 3D
2D
papier vs digitaal
basisregistratie
kadaster
ruimtelijke ordening
UML – URBAN MOBILITY LAB
THE OVERALL IDEA
Loops FCD
GSM
Surveys Emissions
and energy
Chip card
dataTwitterRoad works
maintenance
PT schedules
updates
Events,
incidents,
accidentsDemographic
data
Status infrastructure weather News, information
Vecom data
Existing (open)
data platforms
1 hour
brainstorm with
AMS partners /
enthusiasts on
June 12 …
UML DATABASE
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UML – URBAN MOBILITY LAB
Loops FCD
GSM
Surveys Emissions
and energy
Chip card
dataTwitterRoad works
maintenance
PT schedules
updates
Events,
incidents,
accidentsDemographic
data
REAL-TIME
INFORMATION
OFF-LINE MOBILITY INFORMATION
MOBILITY SERVICES
SHORT-CYCLIC
ASSESSMENT
LONG-TERM
PATTERNS
UML DATABASE
Status infrastructure weather News, information
Vecom data
Existing (open)
data platforms
DATA FUSION, PROCESSING & DIAGNOSTICS TOOLBOX
THE OVERALL IDEA
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UML – URBAN MOBILITY LAB
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AMS Kennisvragen
Monitoren vs
voorspellen
Drukste plekken
in de stad?
Populaire
woonlocaties?
HB-data voetgangers/ fiets?
Beleving
Hoe beleven
weggebruikers het
verkeer?
(Keuze) gedrag
Hoe groot is de
keuzevrijheid?
Gevolgen/ effecten op
interventies in keuzegedrag?
Socio-demografische
gegevens
Hoe verandert
keuzegedrag o.i.v.
ICT?
Gedragingen/
behoeften van
mensen
Hoe wil je fietsers
beinvloeden en sturen?
Organisatie
Rolverdeling
Rijk/ DCO's?
Invloed bepaalde
interventie?
Leefbaarheid
Bereikbaarheid
Impact van N-Z lijn?
Bereikbaarheid i.r.m.
economische ontwikkeling
Effect van economische
ontwikkelingen op bereikbaarheid?
Lucht
Impact verkeersmaatregelen
op luchtkwaliteit voorspellen
en monitoren?
Ruimte
Herontwikkeling (leegloop winkelgebieden)
Benutting openbare
ruimte (asset mgt)
Waar zit de ruimte/ krapte in
verkeerssystemen?
Veiligheid
Energiegebruik
Experimenten met actieve
datacollectie bij burgers
Technologische
ontwikkeling
Gevolgen
electrische
auto?
Invloed van nieuwe
modaliteiten (e-bike)
Stromen
Welke routes gebruiken
fietsers en voetgangers?
Factoren van invoed?
Hoe beinvloed verstedelijking
de vervoersstromen?
Hoe inicht in multimodale
verkeersstromen?
Reistijden
(real-time +
historisch)
Hoe krijgen we grip/ data op
voetgangers/ fietsers?
Hoeveel fietsers rijden er in
de staten van A'dam?
Demografische
kenmerken
Hoe kunnen verschillende
stromen beter op elkaar
afgestemd worden?
Real-time
meetgegevens
verkeer
Schalen
Regionaal
NL steden zijn relatief klein;
invloed en effecten?
Landelijk
Hoe groot is de
latente vraag?
Economische data
(woningwaarde, grondprijzen
etc)
Netwerk
Hoe kan een hubstructuur
bijdragen aan interne
logistiek in een stad?
Waar zitten de
knelpunten in het
netwerk?
Modaliteiten
Factoren van invloed bij
keuze modaliteit?
Samenhang sociale
groepen per modaliteit?
Hoe flexibel zijn mensen in
hun vervoerskeuze
Hoe kunnen verschillende
vervoerswijze elkaar versterken?
Data & privacy
Toekomst
Wensen DATA
In hoeverre is het delen van
vervoersmiddelen een trend?
Nieuwe vervoersconcepten
i.r.t. 'sharefactoren'?
Effect verstedelijking voor
ontwikkeling vh transportsysteem?
Nu
Invloed snorfietsen
indien naar rijbaan?
Behoeften
consumenten &
bedrijven
Verkeersonveilige
kruispunten opsporen
voordat ongevallen
plaatsvinden?
Evaluatie
Doorrekenen effectiviteit van
maatregelen en evaluatie
achteraf; inzicht krijgen!
UML – URBAN MOBILITY LAB
Same
brainstorm:
many, many
questions
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GSM data; tracking travellers
- Potential public transport services
Crowd data
- Evaluating and optimizing pedestrian flows
Passenger data (APC); tracking passengers
- Evaluating and optimizing ridership and passengers flows
GOVI (AVL)
- Vehicle performance
UML – URBAN MOBILITY LAB
FROM DATA TO DISCOVERIES IN PUBLIC TRANSPORT
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PEDESTRIAN FLOWS
FROM DATA TO DISCOVERIES IN PUBLIC TRANSPORT
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ORIGIN-DESTINATION PATTERNS
FROM DATA TO DISCOVERIES IN PUBLIC TRANSPORT
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fictitious data
11. The image cannot be displayed. Your computer may not have enough
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fictitious data
12. The image cannot be displayed. Your computer may not have enough
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fictitious data
13. The image cannot be displayed. Your computer may not have enough
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fictitious data
14. The image cannot be displayed. Your computer may not have enough
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fictitious data
15. The image cannot be displayed. Your computer may not have enough
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fictitious data
16. The image cannot be displayed. Your computer may not have enough
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fictitious data
17. The image cannot be displayed. Your computer may not have enough
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fictitious data
18. The image cannot be displayed. Your computer may not have enough
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fictitious data
19. The image cannot be displayed. Your computer may not have enough
memory to open the image, or the image may have been corrupted.
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appears, you may have to delete the image and then insert it again.
fictitious data
20. The image cannot be displayed. Your computer may not have enough
memory to open the image, or the image may have been corrupted.
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fictitious data
21. The image cannot be displayed. Your computer may not have enough
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fictitious data
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Optimizing public transport network, timetable
design and services
Potential yearly cost savings: > €50 million
Recent data driven studies:
The Hague: 5-15% increased ridership
Tram Utrecht: : €200 Million social benefits
UML – URBAN MOBILITY LAB
FROM DATA TO DISCOVERIES IN PUBLIC TRANSPORT
23. - Real-time estimation &
prediction of delays after
major incidents in road
networks
- Estimation and prediction
queue lengths on urban
arterials
UML – URBAN MOBILITY LAB
Notoriously difficult mobility
problems, for which we need
fusion of data & model
predictions
24. - Estimating OD flow
patterns over all possible
travel modes
- Linking activity patterns to
travel choices and predict
the consequences
- Combined optimization
innercity goods and person
transport
UML – URBAN MOBILITY LAB
Notoriously difficult mobility
problems, for which we need
fusion of data & model
predictions
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UML: WORK IN PROGRESS
ü Constructing the lab:
open platform
ü Collecting the data
ü Setting up partnerships
UML – URBAN MOBILITY LAB
WHAT’S NEXT ?
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Join us!
• Serge Hoogendoorn
S.Hoogendoorn@TUDelft.nl
• Hans van Lint
J.W.C.vanLint@TUDelft.nl
• Niels van Oort
N.vanOort@TUDelft.nl
