www.its.leeds.ac.uk/people/c.calastri Social networks, i.e. the circles of people we are socially connected to, have been recognised to play a role in shaping our travel and activity behaviour. This not only has to do with socialisation being the purpose of travel, but also with enabling mobility and other activities through the so-called social capital. Another theme in the literature connecting social environment and travel behaviour is social influence, i.e. the investigation of how travel behaviour can be affected by observation or comparison with other people. Research about the impact of social influence on travel choices is still at its infancy. In this talk, I will give an overview of how choice modelling can be used to investigate the relationships between social networks, travel and activities. I will touch upon work that I have done so far, in particular I will describe my applications of the Multiple Discrete-Continuous Extreme Value (MDCEV) model to frequency of social interactions as well as to allocation of time to different activities, taking the social dimension into account. In these studies, I make use of social network and travel data collected in places as diverse as Switzerland and Chile. I will also discuss ongoing work making use of longitudinal life-course data to model the impact of family of origin and the “mobility environment” people grew up in on travel decision of adults. Finally, I will outline future plans about modelling behavioural changes due to social influence using the smartphone app travel data that are being collected in Leeds within the “Choices and consumption: modelling long and short term decisions in a changing world” (“DECISIONS”) project.

1. 
Social networks, activities and
travel: building links to understand
behaviour
Chiara Calastri, Institute for Transport Studies & Choice Modelling Centre, University of Leeds

2. Key themes for my PhD
 How do people interact with their social network
members, and what are the implications for travel
behaviour?
 What is the role of personal social networks in
activity patterns?
 Are people going to change their travel behaviour
following information about themselves and other
people?
 How are social networks formed and how do they
evolve (not covered today)?

3. Methodological consideration
 Basic data analysis could give insights to answer these
questions
 But such an approach would not be scientifically sound
or satisfying to us as modellers
 Many factors could explain the same effect and we need
to disentangle them
 Recent advances in choice modelling provide
methodologically robust tools to deal with this
 A key aim of my research is to operationalise and
improve these models

4. 
I – Patterns of interactions

5. Modelling social interactions
 Focus on mode and frequency of interaction
 Previous research mainly used multi-level models but
 Lack of integrated framework
 Did not deal with no communication
 No consideration of satiation

6. Research question
 How do people communicate with each of their social network
members?
 Mode of communication (discrete choice)
 Frequency of communication (continuous choice)

7. Data: snowball sample
 638 egos naming 13,500 alters, fairly representative of the
Swiss population
Kowald, M. (2013). Focussing on leisure travel: The link between spatial mobility, leisure acquaintances and social interactions. PhD thesis, Diss.,
Eidgenössische Technische Hochschule ETH Zürich, Nr. 21276, 2013.

8. Data:The Name Generator

9. Data:The Name Interpreter
“multiple discrete-continuous choice”

10.  Many real-life situations involve making (multiple) discrete and
continuous choices at the same time
 In general, people will not be able to choose an unlimited amount of
these goods because they have limited resources (e.g. money, time)
Modelling multiple discrete-continuous
choices

11. The Multiple Discrete-Continuous
ExtremeValue (MDCEV) Model
 State-of-the-art approach
 Utility maximization-based Khun-Tucker approach demand
system
 Desirable properties
 Closed form probability
 Based on Random Utility Maximisation
 Goods are imperfect substitutes and not mutually exclusive

12. The Multiple Discrete-Continuous
ExtremeValue (MDCEV) Model
 Direct utility specification (Bhat, 2008):
where:
U(x) is the utility with respect to the consumption quantity (Kx1)-vector x (xk≥0 for all
k)
ψk is the baseline marginal utility, i.e. the marginal utility at zero consumption of
good k
0≤αk≤1 reduces the marginal utility with increasing consumption of good k, i.e.
controls satiation. If αk=1, we are in the case of constant marginal utility (MNL)
γk>0 shifts the position of the indifference curves, allowing for corner solutions.

13. Interpretation of the ψ parameter
 Marginal utility of consumption w.r.t. good k
 ψk is the “baseline marginal utility”: utility at the point of zero
consumption

14. Interpretation of the α parameter
 The alpha parameter controls satiation by exponentiating the consumption
quantity. If αk=1, the person is “insatiable” with respect to good k (MNL
case), while when α decreases, the consumer accrues less and less utility
from additional units consumed.

15. Interpretation of the γ parameter
 The γ are translation parameters, i.e. they shift the position of indifference curves so that
they intersect the axes
 Bhat (2005) interprets the γ parameter also as a measure of satiation because of its
impact on the shape of the indifference curves
Source: Bhat, Chandra R. "The multiple discrete-continuous extreme value (MDCEV) model: role of utility function parameters, identification considerations,
and model extensions." TransportationResearch Part B: Methodological 42.3 (2008): 274-303.

16. Probabilities
 MDCEV allows modelling of either expenditure or consumption
 Model estimation maximises likelihood of observed
consumption patterns by changing parameters
where and
 Need to make assumptions about the budget

17. Model Specification
 Dependent variable: yearly frequency of communication by each mode with each
network member
 We estimate only 4 mode-specific effects for each independent variable & satiation
parameter
 Allocation model: individual budget given by the total annual number of interaction
across all alters and all modes
“Ego”
“Alter”“Alter”

18. Results:Core parameters
 α1 goes to zero in all model specifications->utility collapses to a
log formulation
 Baseline utility constants: strong baseline preference for face-
to-face (interesting for the debate on ICT substitution),
followed by phone
 Satiation (γ): Face-to-face provides more satiation, followed by
phone, e-mail and SMS.

19. Results: ego-level effects

20. Results: dyad-level effects
-1
-0.5
0
0.5
1
1.5
2
2.5
3
3.5
0 5 10 20 40 60 80 100 120 140 160 180 200 220 240
Distance
f2f phone e-mail sms

21. Results: dyad level effects

22. Contact maintenance and tie strength
(work in progress…)

23. 
II – Activities and social networks

24. Activity behaviour and social networks
 Activity scheduling (which, for how long, with whom,…) can
provide insights into travel behaviour
 Existing work incorporating social environment in models of
activity behaviour
 only focused on leisure and social activities
 mostly looked at one dimension of decision making
 mainly relied on data from the “Global North”

25. Activity type & duration
 People jointly choose to engage in different activities for certain
amounts of time, e.g. to satisfy variety seeking behaviour
 Choice of which/how many activities to perform (e.g. in a day)
not independent of duration
 …some of the activities might have some common unobserved
characteristics -> we use the nested version of MDCEV

26. A nested model
 The MDCEV (like an MNL) assumes that all the possible
correlations between alternatives is explained through the β.
 In some cases, some alternatives might share some unobserved
correlation (estimated), so that they are closer substitutes of
each other

27. The Multiple Discrete-Continuous Nested
ExtremeValue (MDCNEV) Model
 The utility specification is the same as in the MDCEV model
 Stochastic element not anymore i.i.d. -> nested extreme value
structure following the joint CDF:

28. The Multiple Discrete-Continuous Nested
ExtremeValue (MDCNEV) Model
 Utility maximised subject to a time budget T
 We again maximise the likelihood of observing the observed
time allocation across activities:

29. “Communities in Concepción”,
Concepción (Chile)
Extensive dataset collected in 2012 investigating several aspects of participants’
lives:
 Socio-demographic characteristics
o Age
o Gender
o Level of education
o Employment status & Job type
o Family composition and characteristics
o Personal and household income
o Mobility tools ownership
o Communication tools ownership
 Attitudinal questions
 Social network composition
 Activity diary for 2 full days

30. Time Use diary (filled in)
DAY Sunday
Start End
What were you doing (mode)
Where
N° Hour Min Hour Min Street 1 Street 2
1 10 0 11 20 Wake up, breakfast Michimalongo 15 NA
2 11 20 14 0 Tidy up at home Michimalongo 15 NA
3 14 0 14 5 Going to the shop (walk) NA NA
4 14 5 14 10 In the shop Michimalongo central NA
5 14 10 14 15 Going back home (walk) NA NA
6 14 15 15 30 Lunch Michimalongo 15 NA
7 15 30 15 40 Going to a friend's home (walk) NA NA
8 15 40 15 50 At a friend's home yerbas buenas alto NA
9 15 50 16 0 Going back home (walk) NA NA
10 16 0 19 0 Stay at home Michimalongo 15 NA
11 19 0 19 30 Going to the doctor (walk) NA NA
12 19 30 21 30 Staying at the doctor Salas O’Higgins
13 21 30 22 0 Going back home (walk) NA NA
14 22 0 0 0 Stay at home, sleep Michimalongo 15 NA

31. ActivityClassification
N Activity Description
1 Drop off- Pick Up
2
Family
time to support/attend family members in non-essential activities (Helping children with
homework, attending & playing w/ children)
3
Household Obligations
cleaning/tiding up, taking care of pets, performing ordinary maintenance at home
4 In-home Recreation TV, internet, reading
5 Out-of-home Recreation mostly exercise and sports, cinema
6 Services medical/professional services, banking and religious
7 Social visits to/from friends and relatives and other activities
8 Shopping
9
Study
school homework, University study, different type of classes, Other school and specific
training.
10
Travel
All the trips to/from activities.
11 Work
12 Basic needs
(OUTSIDE GOOD)
eat, sleep, stay home

32. Model specification
 Time allocation across 12 activities during 2 days
 48 hours budget
 Inclusion of both socio-demographics and social network
characteristics
 Satiation parameters are not parametrised:
withgk = exp(mk ) mk = ¢fkwk
where wk is a vector of individual characteristics for the kth alternative and
ϕ’k is the corresponding vector of parameters.

33. The nesting structure
 More than 30 different structures attempted. Final structure:
 “Family” does not belong to any nest
Activity
Out of home
-Drop off-Pick up
-Out of Home recreation
-Services
-Shopping
-Social
-Travel
-Work
In home
-Basic needs (Outside
Good)
-HH obligations
-In-Home recreation
-Study
ϑin home =0.4356ϑ out of home =0.7382

34. In-home activities: Utility parameters
Utility parameters (t-rat vs 0) Basic needs HH obligations In-home recreation Study Family
Baseline constants 0 (fixed) -3.271 (-16.06) -4.682 (-16.58) -5.054 (-9.83) -5.734 (-16.44)
Sex=male - -0.408 (-2.88) - - -
Age<26 - - - 0.421 (1.93) -
Age 26-40 - - - - -
Age 40-60 - - -0.747 (-2.91) - -
Lives w/partner - 0.369 (2.81) - - -
Partner works - - - - -
Low Income - 0.426 (3.35) - - 0.409 (1.34)
1 underage child - - - - -1.041 (-2.8)
2+ underage children - - - - 1.77 (4.56)
Agüita de la Perdiz - - 0.862 (3.05) - -
La Virgen - - 1.093 (3.85) - -
Driving License - - - - -
Internet use - - - 1.055 (2.19) -
Network size - - - - -
Share imm family - -1.04 (-2.96) - - -
Share friends - -0.808 (-2.85) - - -
Age homophily 40-60 - - - - -
Share students in network - - - - -
Share employed in network - - - - -
Contacts 1 km dist. - - 0.413 (1.47) - -
Share employed (student) - - - 4.26 (4.13) -
SC children,female - - - 0.313 (1.87) -
Socio-demographic effects
Social network effects

35. In-home activities: Satiation parameters
Satiation parameters (t-rat vs 0) Basic needs HH obligations In-home recreation Study Family
Baseline γ - 11.77 (53.86) 7.937 (29.29) 5.774 (23.69) 2.173 (10.16)
Age<26 - - - - -
Age 26-40 - -0.168 (-2.04) - - -
1 underage child - - - - -
2+ underage children - - - -0.486 (-2.88) -
Agüita de la Perdiz - -0.248 (-4.06) - - -
Network size - - - - -
SC travel - - - - -
Contacts 1km dist. - - - - -
Nesting parameters (t-rat vs 1) Basic needs HH obligations In-home recreation Study Family
ϑ in home 0.436 (9.54) -
ϑ out of home - - - - -
ϑ family (un-nested) - - - - 1 (fixed)
Socio-demographic effects
Social network effects

36. Utility parameters (t-rat vs 0) Work Drop-off/Pick-up Out-of-Home Recreation Services Social Shopping Travel
Baseline constants -3.827 (-30.12) -6.133 (-14.5) -4.0542 (-25.71) -4.1466 (-34.41)-3.9735 (-8.38) -3.4295 (-15.22) -3.9229 (-4.41)
Sex=male - - - - - - -
Age$<$26 - - - - - - -
Age 26-40 0.449 (2.87) - - - - - -
Age 40-60 - - - - - - -
Lives w/partner - - - - - 0.472 (2.43) -
Partner works - 0.648 (2.04) - - - - -
Low Income - 1.029 (3.83) - - - - -
1 underage child 0.759 (3.32) - - - - - -
2+ underage children - 1.141 (3.17) - - - - -
Agüita de la Perdiz - - - - 0.259 (1.61) - -
La Virgen - - - - - - -
Driving License - 0.713 (2.43) - - - - -
Internet use - - - - 0.402 (1.92) - -
Network size - - - - 0.304 (1.85) - 1.4446 (4.45)
Share imm family - - - - - - -
Share friends - - - - - -0.858 (-2.48) -
Age homophily 40-60 - - - - -0.657 (-2.31) - -
Share students in network - - 1.244 (3.31) - - - -
Share employed in network 1.426 (5.57) - - - - - -
Contacts 1 km dist. - -1.838 (-2.99) -0.52 (-1.44) - - 0.861 (-2.39) -1.4028 (-2.28)
Share employed (student) - - - - - - -
SC children,female - 0.774 (2.83) - - - - -
Socio-demographic effects
Social network effects
Out-of-home activities: Utility parameters

37. Satiation parameters (t-rat vs 0) Work Drop-off/Pick-up Out-of-Home Recreation Services Social Shopping Travel
Baseline γ 5.841 (46.02) 0.344 (1.7) 2.637 (18.12) 1.659 (9.52) 2.643 (19.86) 0.8574 (6.33) 0.1695 (0.55)
Age$<$26 - - - - - 0.0541 (1.55) -
Age 26-40 - - - - - - -
1 underage child -0.189 (-2.92) - - - - - -
2+ underage children - - - - - - -
Agüita de la Perdiz - - - - - - -
Network size - - - - - - -1.3054 (-4.45)
SC travel - - - - - - -0.1691 (-1.94)
Contacts 1km dist. - - - - - 0.2167 (1.89) 0.2798 (1.58)
Nesting parameters (t-rat vs 1) Work Drop-off/Pick-up Out-of-Home Recreation Services Social Shopping Travel
ϑ in home - - - - - - -
ϑ out of home
ϑ family (un-nested) - - - - - - -
Socio-demographic effects
Social network effects
0.7382 (18.28)
Out-of-home activities : Satiation
parameters

38. Results: discrete and continuous choice
Utility parameters (t-rat vs 0) Work Drop-off/Pick-up Out-of-Home Recreation Services Social Sh
Baseline constants -3.827 (-30.12) -6.133 (-14.5) -4.0542 (-25.71) -4.1466 (-34.41)-3.9735 (-8.38) -3.42
Sex=male - - - - -
Age$<$26 - - - - -
Age 26-40 0.449 (2.87) - - - -
Age 40-60 - - - - -
Lives w/partner - - - - - 0.4
Partner works - 0.648 (2.04) - - -
Low Income - 1.029 (3.83) - - -
1 underage child 0.759 (3.32) - - - -
2+ underage children - 1.141 (3.17) - - -
Agüita de la Perdiz - - - - 0.259 (1.61)
La Virgen - - - - -
Driving License - 0.713 (2.43) - - -
Internet use - - - - 0.402 (1.92)
Network size - - - - 0.304 (1.85)
Share imm family - - - - -
Share friends - - - - - -0.8
Age homophily 40-60 - - - - -0.657 (-2.31)
Share students in network - - 1.244 (3.31) - -
Share employed in network 1.426 (5.57) - - - -
Contacts 1 km dist. - -1.838 (-2.99) -0.52 (-1.44) - - 0.86
Share employed (student) - - - - -
Socio-demographic effects
Social network effects
Satiation parameters (t-rat vs 0) Work Drop-off/Pick-up Out-of-Ho
Baseline γ 5.841 (46.02) 0.344 (1.7) 2.63
Age$<$26 - -
Age 26-40 - -
1 underage child -0.189 (-2.92) -
2+ underage children - -
Agüita de la Perdiz - -
Network size - -
SC travel - -
Contacts 1km dist. - -
Nesting parameters (t-rat vs 1) Work Drop-off/Pick-up Out-of-Ho
ϑ in home - -
ϑ out of home
ϑ family (un-nested) - -
Socio-demogr
Social netw

39. Model comparison
 The nested model performs better than the MDCEV
 Estimation of models inclusive of Socio-demographics only and
Social network measures only excluded confounding effects
between the two

40. 
III – Conclusions and next steps

41. Does our work matter?
 Choice modelling can really make a difference here!
 We gain important insights into interactions between people and the
role of the social network in activities
 Working on methodological contributions to get further insights:
 Multiple budgets & product-specific upper limits on consumption
 Important in a multi-day context, for example
 Evolution of discrete and continuous elements over time
 We can use the models to forecast changes in activities, which have
repercussions on transport demand

42. Our idea for an overall framework

43. We need complex data for this

44. 
Thank you!