1

Bevorzugter Zitierstil für diesen Vortrag

Axhausen, K.W. (2015) Smart cards and social networks: Simulation and familiar strangers, presentation at the Excellence in data science, Politecnico di Torino, October 2015.

Smart cards and social networks: Results and familiar strangers

KW Axhausen IVT ETH Zürich October 2014

3

Social networks: •  Timo Ohnmacht •  Andreas Frei •  Matthias Kowald •  Lijun Sun •  Andreas Diekmann, ETH Zürich •  Jonas Larsen, Roskilde/John Urry, Lancaster

Agent-based models •  Thibaut Dubernet •  Pieter Fourie

Social network generation

•  Theo Arentze, TU EIndhoven

Acknowledgements

4

Most of the materials and more will be in:

Kowald, M. and K.W. Axhausen (eds.) (2015) Social networks and travel behaviour, Ashgate

Further acknowledgements

5 5

Why the interest ?

6 6

An agent-based model of travel demand: e.g. Singapore

Road based – Switzerland 1950 and 2000 Sc

here

r, 20

04

8 8

Long distance journeys (100km+) in Germany, 2010‘s

Type Number/year km/journeyVacation (5 days plus) 1.0 1600Short vacation (2-4 days) 1.2 410Other journeys with overnight stays 0.3 410Day excursions 6.0 200Business trip with overnight stay 1.2 500Business trip without overnight stay 1.2 150Long-distance commuting and other trips 5.0 150

Fric

k un

d G

rimm

, 201

4

Why social networks in transport/spatial planning ?

9

10

Example: Number of accompanying travellers

Short vacationExcursion: nature

OtherExcursion: culture

Meeting friendsFurther education (leisure)

Garden/ cottageVoluntary work

Disco, pub, restaurant, cinemaMeeting relatives/family

Window shoppingPick up/drop off/attendance

Group/club meetingFamily duty

CemeteryActive sports

EducationLong-term shopping

Walk or strollDaily shopping

Private businessPrivate business (doctor,...

Work

3.02.52.01.51.00.50.0

Mean

Dog travelling alongOther persons travelling alongHousehold members travelling along

Axh

ause

n et

al.,

200

7

11

Example: Heterogenity in choice

Location choice

•  WTP •  Taste •  Joint choice with family, friends, persons to meet •  Schedule constraints •  Social constraints

For mode choice in addition

•  Luggage •  Company •  Weather •  Temperature

12

Example: Residential location choice in Kt. Zürich

Variable Beta t-Test Rent/Income -5.51 *** log(m2/head) 0.98 *** Frequency weighted mean distance to friends -8.16 * Exponent (friends) 0.22 ** Mean distance to work/school -1.59 ** Exponent (distance to work) 0.37 ** Travel time to Bürkliplatz 0.02 ** log(transit accessibility) * "No car" 0.41 ** log(car accessibility) * “Car" -0.30 ** Share of equally sized HH within 1 km 0.02 * Population density within 1 km 0.01 ** Share of empty flats in municipality -0.11 N= 683, rho² = 0.2128; * > 0.1; ** > 0.05; *** > 0.01

Bel

art,

2011

Travel and social networks

13

14

Benchmarking the current state

•  Numbers of contacts •  Distance distributions •  Geographies •  Frequency and mode of contact

•  “Productivity” •  Levels of local anomie •  Levels of local trust •  Level of place attachment

Empirical strategy

•  Surveys of social geographies & mobility biographies •  Egocentric •  Snowball

•  Travel diaries •  One-Day •  Multiple days

•  With/without information about the presence of others •  With/without named co-travellers, co-present persons

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Social network surveys @ IVT

•  Ohnmacht: 50 egos qualitative/quantitative in Zürich

•  Larsen/Urry: 24 egos qualitative/quantitative in NE England

•  Frei: 300 egos quantitative in Zürich

•  Kowald: snowball; 750 egos quantitative worldwide (starting with 40 egos in Kanton Zürich)(12000 alters in total) (8 day diary included)

•  Kowald/Diekmann: 2000 respondents of the Swiss Environment Survey – 5 core alters

•  Sun: Smart card use on busses in Singapore 16

17

Biography of an architect, about thirty La

rsen

, Urr

y an

d A

xhau

sen,

200

6

18

Number of contacts reported

Bars show percents

0 10 20 30 40 50

Number of contacts named

2%

4%

6%

8%

10%

Perc

ent

Frei

and

Axh

ause

n, 2

007

19

Great circle distances between “leisure” contacts: Zürich Fr

ei a

nd A

xhau

sen,

200

7

20

Great circle distances between “leisure” contacts: Snowball

log−transformed distances [km]

Freq

uenc

y

0.01 0.1 11 10 100 1000 10000

010

020

030

040

0

Dat

en: S

chne

ebal

lbef

ragu

ng IV

T, Si

ehe

Kow

ald

et a

l. 201

2

21

Example of a social network geography

22

Size of network geometries

95%-confidence ellipse of the social network geography

1.E10

1.E91.E81.E71.E61.E51.E41.E31.E21.E11.E0

Perc

ent

40

30

20

10

0

Frei

and

Axh

ause

n, 2

007

23

Interactions by mode and distance between homes

10000.001000.00100.0010.001.000.001

100.00

80.00

60.00

40.00

20.00

0.00

SMS messages/year Great circle distance [km]

Email messages/year Great circle distance [km]

Phone calls/year Great circle distance [km]

Face-to-face visits/year Great circle distance [km]

Great circle distance (km)

10000.001000.00100.0010.001.000.001

100.00

80.00

60.00

40.00

20.00

0.00

SMS messages/year Great circle distance [km]

Email messages/year Great circle distance [km]

Phone calls/year Great circle distance [km]

Face-to-face visits/year Great circle distance [km]

Great circle distance (km)

Frei

and

Axh

ause

n, 2

007

24

2010/11 Snowball survey

25

Behind egos’ horizons: The connected ‘snowball’-graph

Vertices Edges Density Components Triangles

Without sociogram 6‘584 7‘349 0.000 19 0.017

With sociogram 6‘584 32‘671 0.002 19 0.518

Seed

Ego

Bridging alter

Kow

ald

and

Axh

ause

n, 2

011

26

Comparisons

Transport motivated social network surveys

•  East York, Ontario (Wellman, Carrasco et al.)

•  Eindhoven, Netherlands (Arentze, Van der Berg)

•  Concepcion, Chile (Carrasco)

•  City of Zürich (Frei)

•  Kanton Zürich snowball (Kowald)

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Contact “density” – shares by distance class

28

Great circle distance [km]

Dens

ity

0 10 100 1000 10000

0.00

0.10

0.20

0.30

Great circle distance [km]

Dens

ity

0 20 40 60 80 100

00.

20.

6 ZurichEindhovenSwitzerlandConcepcionToronto

Kow

ald

et a

l., fo

rthc

omin

g

Shares of contact by mode

29

●

●● ●

●

●

●

● ●

Great circle distance [km]0 1 10 100 1000 10000

0.0

0.2

0.4

0.6

0.8

●

● ●●

●

●

●

●

● ● ●● ●

●

●●

●

Great circle distance [km]0 1 10 100 1000 10000

0.0

0.2

0.4

0.6

0.8

●

● ● ●●

●

●●

●● ●

●●

●

●

●●

Great circle distance [km]0 1 10 100 1000 10000

0.0

0.2

0.4

0.6

0.8

●● ● ●

●● ●

●

●

●

Zurich

Eindhoven

Switzerland

Concepcion

Internet

Face-to-face Telephone

Kow

ald

et a

l., fo

rthc

omin

g

30

Low level networks as a building block

31

Smart card records as a source

Card  ID,Passenger  Type,Travel  Mode,

BusService  Numbe r,Ride  Start  T ime  (Date),Bus  Registration  No.Direction

Bo arding  S top  ID

Alighting  Stop  ID,Ride  Duration,Ride  Distance,Fare  Paid ,

MetroTransfer  Numbe r,Boarding  Station  ID,Ride  Start  T ime  (Date),Ö

Sun,

201

3

Arrival distribution along a line

Institutsseminar 2014

Sun,

201

3

33

Number of contacts versus usage frequency Su

n et

al.,

201

3

34

Monday Su

n, 2

013

35

Tuesday Su

n, 2

013

36

... Friday Su

n, 2

013

37

... the weekly summary Su

n, 2

013

38

A small world network in Singapore‘s busses Su

n, 2

013

•  One component by Wednesday

•  Diameter: 6

•  Characteristic path length: 2.95 •  (random: 2.63)

•  Average clustering coefficient: 0.19 •  (random: 4.5x10-4)

•  Small-world •  Watts DJ & Strogatz SH (1998) Collective dynamics of ‘small-world’networks. Nature 393:440-442.

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A small world network in Singapore‘s busses, but uneven Diagramm[degree50by50] /Users/axhausen/Desktop/ID_Home_degree/degree50by50.sav

Median of y coordinate390000.00380000.00370000.00360000.00350000.00340000.00

Med

ian

of x

coo

rdin

ate

165000.00

160000.00

155000.00

150000.00

145000.00

140000.00

135000.00

477.01+415.01 - 477.00350.51 - 415.00267.01 - 350.50<= 267.00

median degrees in quintiles

Page 2

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Integration

41

Integration and future work

•  Generation of artificial social networks (Arentze et al., 2012) (degree,

clustering, distances)

•  Repeat of Switzerland Snowball

•  Measurement of network size (leisure, work, civic engagement)

•  Measurement of network dynamics (Timmerman’s ERC project, Carrasco’s Concepcion survey)

•  Measurement of anomie, trust and social network geography

•  Integration of network choice/decision making model (Dubernet)

42

Integration, again

43

Some hypotheses for travel behaviour and more

Wages Fleet comfort Housing consumption vtts et al.

Activities

Specialisation

k

vkm

pkm Tours + +

+ +

+

+ +

+ +

+ + +

+

-

- -

+

- Energy costs

+

+ -

Elasticity > 0 Elasticity < 0

+ -

Migration +

+

Professional and personal activity space

Network geography Number

of networks

Network overlap

Local anomie

-

+

+ -

- - - -

44

www.ivt.ethz.ch www.matsim.org

www.futurecities.ethz.ch

Questions ?

45

Literature and references

Arentze, T.A., M. Kowald and K.W. Axhausen (2012) A method to model population-wide social networks for large scale activity-travel micro-simulations, paper presented at the 91th Annual Meeting of the Transportation Research Board, Washington, D.C., January 2012

Axhausen, K.W. (2000) Geographies of somewhere: A review of urban literature, Urban Studies, 37 (10) 1849-1864.

Axhausen, K.W. (2008) Social networks, mobility biographies and travel: survey challenges, Environment and Planning B, 35 (6) 981-996.

Axhausen, K.W. (2007) Activity spaces, biographies, social networks and their welfare gains and externalities: Some hypotheses and empirical results, Mobilities, 2 (1) 15-36.

Axhausen, K.W. and A. Frei (2007) Contacts in a shrunken world, Arbeitsbericht Verkehrs- und Raumplanung, 440, IVT, ETH Zürich, Zürich.

Frei, A., K.W. Axhausen and T. Ohnmacht (2009) Mobilities and social network geography: Size and spatial dispersion – the Zürich case study results, in T. Ohnmacht, H. Maksim and M. Bergmann (eds.) Mobilities and Inequalities, 99-120, Ashgate, Farnham.

Frei, A. and K.W. Axhausen (2007) Size and structure of social netowork geographies, Arbeitsberichte Verkehrs- und Raumplanung, 439, IVT, ETH Zürich, Zürich.

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Literature and references

Dicken, P. (1998) Global Shift: Transforming the World Economy, Paul Chapman Publishing,

London. FCC (2001) Long distance telecommunication industry, FCC, Washington, D.C. Frick, R. and B. Grimm (2014) Long-distance Mobility Current Trends and Future Perspectives,

ifmo, München. Grannis, R. (1998) The importance of trivial streets: Residential streets and residential

segregation, American Journal of Sociology, 103 (6) 1530-1564.  Kowald M. and K.W. Axhausen (2011) Surveying data on connected personal networks,

Arbeitsberichte Verkehrs- und Raumplanung, 722, IVT, ETH Zürich, Zürich. Kowald, M. P. van den Berg, A. Frei, J.-A. Carrasco, T. Arentze, K.W. Axhausen, D. Mok, H.J.P.

Timmermans and B. Wellman (Forthcoming) The spatiality of personal networks in four countries: A comparative study, Journal of Transport Geography.

Larsen, J., J. Urry and K.W. Axhausen (2006) Mobilities, Networks, Geographies, Ashgate, Aldershot.

Putnam, R.D. (1999) Bowling Alone: The collapse and revival of American community, Schuster and Schuster, New York.

Schlich, R., B. Kluge, S. Lehmann und K.W. Axhausen (2002) Durchführung einer 12-wöchigen Langzeitbefragung, Stadt Region Land, 73, 141-154.

Sun, L., K.W. Axhausen, D.-H. Lee and X. Huang (2013) Understanding metropolitan patterns of daily encounters, Proceedings of the National Academy of Science (PNAS), 110 (34) 13774-9.