social networks, activities, and travel - building links to understand behaviour
TRANSCRIPT
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Social networks, activities and travel: building links to understand behaviour
Chiara Calastri, Institute for Transport Studies & Choice Modelling Centre, University of Leeds
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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)?
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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
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I – Patterns of interactions
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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
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Research question
How do people communicate with each of their social network members?
Mode of communication (discrete choice)
Frequency of communication (continuous choice)
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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.
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Data: The Name Generator
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Data: The Name Interpreter
“multiple discrete-continuous choice”
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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
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The Multiple Discrete-Continuous Extreme Value (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
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The Multiple Discrete-Continuous Extreme Value (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.
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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
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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.
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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." Transportation Research Part B: Methodological 42.3 (2008): 274-303.
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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
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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”
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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.
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Results: ego-level effects
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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
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Results: dyad level effects
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Contact maintenance and tie strength (work in progress…)
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II – Activities and social networks
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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”
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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
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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
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The Multiple Discrete-Continuous Nested Extreme Value (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:
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The Multiple Discrete-Continuous Nested Extreme Value (MDCNEV) Model
Utility maximised subject to a time budget T
We again maximise the likelihood of observing the observed time allocation across activities:
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“Communities in Concepción”, Concepción (Chile)
Extensive dataset collected in 2012 investigating several aspects of participants’ lives:
Socio-demographic characteristicso 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
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Time Use diary (filled in)
DAY Sunday
Start EndWhat 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
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Activity Classification
N Activity Description
1 Drop off- Pick Up
2Family
time to support/attend family members in non-essential activities (Helping children with homework, attending & playing w/ children)
3Household 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.
10Travel
All the trips to/from activities.
11 Work
12 Basic needs (OUTSIDE GOOD)
eat, sleep, stay home
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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.
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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
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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
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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
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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$<$26 - - - - - - -
Age 26-40 0.449 (2.87) - - - - - -
Age 40-60 - - - - - - -
Lives w/partner - - - - - 0.472 (2.43) -
Partner works - 0.648 (2.04) - - - - -
Low Income - 1.029 (3.83) - - - - -
1 underage child 0.759 (3.32) - - - - - -
2+ underage children - 1.141 (3.17) - - - - -
Agüita de la Perdiz - - - - 0.259 (1.61) - -
La Virgen - - - - - - -
Driving License - 0.713 (2.43) - - - - -
Internet use - - - - 0.402 (1.92) - -
Network size - - - - 0.304 (1.85) - 1.4446 (4.45)
Share imm family - - - - - - -
Share friends - - - - - -0.858 (-2.48) -
Age homophily 40-60 - - - - -0.657 (-2.31) - -
Share students in network - - 1.244 (3.31) - - - -
Share employed in network 1.426 (5.57) - - - - - -
Contacts 1 km dist. - -1.838 (-2.99) -0.52 (-1.44) - - 0.861 (-2.39) -1.4028 (-2.28)
Share employed (student) - - - - - - -
SC children,female - 0.774 (2.83) - - - - -
Socio-demographic effects
Social network effects
Out-of-home activities: Utility parameters
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Satiation parameters (t-rat vs 0) Work Drop-off/Pick-up Out-of-Home Recreation Services Social Shopping Travel
Baseline γ 5.841 (46.02) 0.344 (1.7) 2.637 (18.12) 1.659 (9.52) 2.643 (19.86) 0.8574 (6.33) 0.1695 (0.55)
Age$<$26 - - - - - 0.0541 (1.55) -
Age 26-40 - - - - - - -
1 underage child -0.189 (-2.92) - - - - - -
2+ underage children - - - - - - -
Agüita de la Perdiz - - - - - - -
Network size - - - - - - -1.3054 (-4.45)
SC travel - - - - - - -0.1691 (-1.94)
Contacts 1km dist. - - - - - 0.2167 (1.89) 0.2798 (1.58)
Nesting parameters (t-rat vs 1) Work Drop-off/Pick-up Out-of-Home Recreation Services Social Shopping Travel
ϑ in home - - - - - - -
ϑ out of homeϑ family (un-nested) - - - - - - -
Socio-demographic effects
Social network effects
0.7382 (18.28)
Out-of-home activities : Satiation parameters
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Results: discrete and continuous choice
Utility parameters (t-rat vs 0) Work Drop-off/Pick-up Out-of-Home Recreation Services Social Shopping Travel
Baseline constants -3.827 (-30.12) -6.133 (-14.5) -4.0542 (-25.71) -4.1466 (-34.41)-3.9735 (-8.38) -3.4295 (-15.22) -3.9229 (-4.41)
Sex=male - - - - - - -
Age$<$26 - - - - - - -
Age 26-40 0.449 (2.87) - - - - - -
Age 40-60 - - - - - - -
Lives w/partner - - - - - 0.472 (2.43) -
Partner works - 0.648 (2.04) - - - - -
Low Income - 1.029 (3.83) - - - - -
1 underage child 0.759 (3.32) - - - - - -
2+ underage children - 1.141 (3.17) - - - - -
Agüita de la Perdiz - - - - 0.259 (1.61) - -
La Virgen - - - - - - -
Driving License - 0.713 (2.43) - - - - -
Internet use - - - - 0.402 (1.92) - -
Network size - - - - 0.304 (1.85) - 1.4446 (4.45)
Share imm family - - - - - - -
Share friends - - - - - -0.858 (-2.48) -
Age homophily 40-60 - - - - -0.657 (-2.31) - -
Share students in network - - 1.244 (3.31) - - - -
Share employed in network 1.426 (5.57) - - - - - -
Contacts 1 km dist. - -1.838 (-2.99) -0.52 (-1.44) - - 0.861 (-2.39) -1.4028 (-2.28)
Share employed (student) - - - - - - -
SC children,female - 0.774 (2.83) - - - - -
Socio-demographic effects
Social network effects
Satiation parameters (t-rat vs 0) Work Drop-off/Pick-up Out-of-Home Recreation Services Social Shopping Travel
Baseline γ 5.841 (46.02) 0.344 (1.7) 2.637 (18.12) 1.659 (9.52) 2.643 (19.86) 0.8574 (6.33) 0.1695 (0.55)
Age$<$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 homeϑ family (un-nested) - - - - - - -
Socio-demographic effects
Social network effects
0.7382 (18.28)
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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
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III – Conclusions and next steps
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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
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Our idea for an overall framework
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We need complex data for this
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Thank you!