Social networks, activities and travel: building links to understand behaviour

Chiara Calastri, Institute for Transport Studies & Choice Modelling Centre, University of Leeds

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)?

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

I – Patterns of interactions

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

Research question

How do people communicate with each of their social network members?

Mode of communication (discrete choice)

Frequency of communication (continuous choice)

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.

Data: The Name Generator

Data: The Name Interpreter

“multiple discrete-continuous choice”

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

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

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.

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

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.

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.

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

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”

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.

Results: ego-level effects

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

Results: dyad level effects

Contact maintenance and tie strength (work in progress…)

II – Activities and social networks

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”

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

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

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:

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:

“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

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

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

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.

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

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

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

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

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

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)

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

III – Conclusions and next steps

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

Our idea for an overall framework

We need complex data for this

Thank you!