Social networks, social influence and activity-travel behaviour: a review of models and empirical evidence

Figures & data

Table 1. Models of social network properties based on ego-centric approaches in travel behaviour research.

Research domain	Reference	Identified network (applied in model)^a	Type of model	Attributes and model specification^b
				Ego-level attributes	Ego-network-level attributes	Ego–alter-level attributes
Network size	Frei and Axhausen (2007)	1. Very close 2. Spend leisure time	Negative binominal regression	[E] Socio-economics [E] Mobility characteristics	[D] Network size	(Nothing)
	Van den Berg et al. (2009)	1. Very close 2. Somewhat close	Linear regression	[E] Socio-economics [E] Mobility characteristics [E] Residential characteristics	[E] Number of clubs/unions [D] Network size	(Nothing)
Geographical distribution of residential location	Frei and Axhausen (2007)	1. Very close 2. Spend leisure time	Tobit; Probit	[E] Socio-economics [E] Mobility characteristics	[E] Network size [D] Size of geographical distribution of alters	(Nothing)
	Carrasco et al. (2008)	1. Very close 2. Somewhat close	Multilevel	[E] Socio-economics	[E] Network composition (proportion) [E] Network subgrouping	[E] Types of relationship [E] Alter’s socio-economics [E] Alter’s degree centrality [E] Tie strength [D] Geographical distance
	Van den Berg et al. (2009)	1. Very close 2. Somewhat close	Linear regression	[E] Socio-economics [E] Mobility characteristics [E] Residential characteristics	[E] Network size [E] Number of clubs/unions	[E] Types of relationship [D] Geographical distance
	Kowald et al. (2013)	Various (different data sets from five cities)	Multilevel	[E] Socio-economics [E] Mobility characteristics	[E] Network composition (proportion)	[E] Types of relationship [E] Similarity of socio-economics [E] Tie strength [E] Duration of relationship [D] Geographical distance

Note: ^a“Very close” indicates “people who you discuss important matters with, or who you regularly keep in touch with, or who are there for you if you need help.”; “Somewhat close” indicates “people who are more than just casual acquaintances, but not very close.”

^b[E]: Explanatory variable; [D]: Dependent variable.

Table 2. Models of social activity participation based on ego-centric approaches.

Research domain	Reference	Identified network (Applied in model)^a	Type of model	Attributes and model specification^b
				Ego-level attributes	Ego-network-level attributes	Ego–alter-level attributes
Ego frequency of social activity participation	Van den Berg et al. (2010)	1. Interact face to face during 2 days 2. Very close (Number only) 3. Somewhat close (Number only)	Poisson	[E] Socio-economics [E] Residential characteristics	[E] Network size [E] Number of clubs/unions [E] Day of the week [D] Face-to-face contact frequency per day	(Nothing)
	Van den Berg et al. (2012c)	1. Very close 2. Somewhat close	Structural equation –path analysis	[E] Socio-economics [E] Residential characteristics [E] Mobility characteristics	[ED] Network size [ED] Network composition (proportion) [ED] Number of clubs [ED] Frequency of going to club per month	(Nothing)
	Lin and Wang (2014)	1. Interact face to face and through ICTs in the past week (Number only)	Structural equation –path analysis	[E] Socio-economics [ED] Major source of: (1) emotional support, (2) instrumental support, (3) social companionship	[ED] Number of alters contacted in the past week [ED] Proportion of contacts with family in the past week [ED] Number of out-of-home non-work activities with: (1) Family/relatives, (2) Friends/acquaintances, (3) Alone (solo activity), in the past week [ED] Number of travel with: (1) Family/relatives (2) Friends/acquaintances, (3) Alone (solo travel), in the past week	(Nothing)
Ego–alter frequency of social activity participation	Carrasco and Miller (2009)	1. Very close 2. Somewhat close	Multilevel	[E] Socio-economics	[E] Network composition (proportion) [E] Density [E] Network subgrouping [E] Group degree centralisation	[E] Types of relationship [E] Alter’s socio-economics [E] Alter’s degree centrality [E] Tie strength [E] Geographical distance [E] Contact frequencies per year: (1) phone, (2) instant message [D] Face-to-face contact frequency per year
	Frei and Axhausen (2008)	1. Very close 2. Spend leisure time	Structural equation – Multilevel path analysis	[E] Socio-economics [E] Mobility characteristics	(Nothing)	[E] Types of relationship [E] Geographical distance [E] Duration of relationship [ED] Face-to-face contact frequency per year [ED] Contact frequency by phone per year [ED] Contact frequency by e-mail per year [ED] Contact frequency by SMS per year
	Van den Berg et al. (2009)	1. Very close 2. Somewhat close	Ordinal regression	[E] Socio-economics [E] Mobility characteristics [E] Residential characteristics	[E] Network size [E] Number of clubs/unions	[E] Types of relationship [E] Geographical distance [D1] [E4] Contact frequency by phone per month [D2] [E4] Contact frequency by SMS per month [D3] [E4] Contact frequency by e-mail per month [D4] Face-to-face contact frequency per month
	Van den Berg et al. (2012b)	1. Very close 2. Somewhat close	Structural equation – multilevel path analysis	[E] Socio-economics	(Nothing)	[E] Types of relationship [E] Geographical distance [E] Tie strength [E] Duration of relationship [ED] Face-to-face contact frequency per month [ED] Contact frequency by phone per month [ED] Contact frequency by e-mail per month [ED] Contact frequency by SMS per month
	Sharmeen et al. (2014)	1. Gained or changed due to a life event within the past 2 years 2. Very close 3. Somewhat close	Ordered logit	[E] Socio-economics [E] Residential characteristics [E] Life cycle events	[E] Network size [E] Number of club memberships	[E] Types of relationship [E] Similarity of socio-economics [E] Geographical distance [E] Tie strength [E] Face-to-face contact frequency in the previous time frame [D] Face-to-face contact frequency
	Van den Berg et al. (2015)	1. Interact face to face during 2 days 2. Very close (Number only) 3. Somewhat close (Number only)	Multilevel	[E] Socio-economics [E] Perceived neighbourhood characteristics [E] Mobility characteristics	[E] Network size	[E] Day of week of the interaction [D] Local social interaction
Propensity of socialising	Carrasco and Miller (2006)	1. Very close and somewhat close (Number only) 2. Very close and somewhat close who interact with (i) at least once a week (ii) between once a week and once a month (Number only)	Structural equation	[E] Socio-economics	[E] Network size [ED] Propensity to perform: (1) hosting/visiting with strong ties, (2) hosting/visiting with weak ties, (3) bar/restaurants with strong ties, (4) bar/restaurants with weak ties	(Nothing)
	Carrasco et al. (2008a)	1. Very close 2. Somewhat close	Multilevel	[E] Socio-economics	[E] Network size [E] Network composition (proportion) [E] Network subgrouping [E] Group degree centralisation [E] Group betweenness centralisation	[E] Types of relationship [E] Alter’s betweenness [E] Similarity of socio-economics [E] Tie strength [E] Geographical distance [D] Degree of socialising

Note: ^a“Very close” indicates “people who you discuss important matters with, or who you regularly keep in touch with, or who are there for you if you need help.”; “Somewhat close” indicates “people who are more than just casual acquaintances, but not very close.”

^b[E]: Explanatory variable; [D]: Dependent variable; [D#]: Dependent variable of model #; [ED]: Endogenous variable.

Table 3. Models of social activity-travel characteristics based on ego-centric approaches.

Research domain	Reference	Identified network^a	Type of model	Attributes and model specification^b
				Ego-level attributes	Ego-network-level attributes	Ego–alter-level attributes
Location type	Van den Berg et al. (2010)	1. Interact face to face during 2 days 2. Very close (Number only) 3. Somewhat close (Number only)	Multinomial logistic	[E] Socio-economics [E] Residential characteristics	[E] Network size [E] Number of clubs/unions [E] Face-to- face contact frequency per day	[E] Day of the week [D1] [E2] Interaction purpose [D2] Location type
	Van den Berg et al. (2014)	1. Interact face to face during 2 days	Latent class choice (Ego–Alter)	(Nothing)	(Nothing)	[D] Location type
Travel distance	Van den Berg et al. (2010)	1. Interact face to face during 2 days 2. Very close (Number only) 3. Somewhat close (Number only)	Tobit	[E] Socio-economics [E] Residential characteristics	[E] Network size [E] Number of clubs/unions [E] Face-to- face contact frequency per day	[E] Day of the week [E] Interaction purpose [E] Location type [D] Distance travelled
	Moore et al. (2013)	1. Very close 2. Somewhat close	Structural equation –Path analysis	[E] Socio-economics [E] Residential characteristics [E] Time pressure	[E] Network size [E] Density of network [E] Homophily of socio-economics [E] Network composition (proportion)	[E] Mean geographical distance between: (1) ego home and alters’ homes involved in the activity, (2) the activity place and alters’ homes involved in the activity [ED] Geographical distance between ego home and the activity place [ED] Activity duration [ED] Number of alters involved in the activity
	Van den Berg et al. (2013)	1. Interact face to face or through ICTs during 2 days 2. Very close (Number only) 3. Somewhat close (Number only)	Structural equation –Path analysis	[E] Socio-economics [E] Residential characteristics	[E] Number of active clubs [ED] Network size [ED] Contact frequency by phone for 2 days [ED] Internet interactions frequency for 2 days [ED] Number of trips for social interactions for 2 days [ED] Total social travel distance for 2 days	(Nothing)
Activity duration	Habib et al. (2008)	1. Interact face to face during a week	Integration of logit and accelerated hazard	[E] Socio-economics	[E] Network size [E] Network composition (proportion)[E] Variability of with whom	[E] Number of alters involved in the activity [E] Number of potential locations of the activity [E] Activity duration flexibility [E] Travel time for the activity [D1&D2] With whom [D1] Activity duration [D2] Start time of the activity
	Habib and Carrasco (2011)	1. Interact face to face during a week	Integration of logit and accelerated hazard	[E] Socio-economics	[E] Network composition (proportion) [E] Variability of with whom	[E] Number of alters involved in the activity [E] Activity duration flexibility [E] Travel time [D] With whom [D] Activity duration [D] Start time of the activity
	Van den Berg et al. (2012a)	1. Interact face to face during 2 days	Latent class accelerated hazard	(Nothing)	(Nothing)	[E] Types of relationship [E] Geographical distance [E] Tie strength [E] Number of alters involved in the activity [E] Social activity characteristics [D] Social activity duration
	Moore et al. (2013)	(see above “Travel distance”)	(see above “Travel distance”)	(see above “Travel distance”)	(see above “Travel distance”)	(see above “Travel distance”)
Travel mode	Sharmeen and Timmermans (2014)	1. Gained or changed due to a life event within the past 2 years 2. Very close 3. Somewhat close	Logit	[E] Socio-economics [E] Life cycle events [E] Mobility characteristics (before the event) [D] Travel mode (most often used) for social activity	[E] Network size	(Nothing)
	Rezende et al. (2016)	1. Discuss important matters for the last 3 months	Logit	[E] Socio-economics [E] Residential characteristics [D1] Mode choice of car owner for social event [D2] Mode choice of non-car owner for social event	[E] Density of network [E] Contact frequency [E] Duration of relationships [E] Homophily of socio-economics [E] Heterogeneity of socio-economics	(Nothing)

Note: ^a“Very close” indicates “people who you discuss important matters with, or who you regularly keep in touch with, or who are there for you if you need help.”; “Somewhat close” indicates “people who are more than just casual acquaintances, but not very close.”

^b[E]: Explanatory variable; [E#]: Explanatory variable of [D#]; [D]: Dependent variable; [D#]: Dependent variable of model #; [ED]: Endogenous variable.

Table 4. Discrete choice models of social influences on activity-travel decisions.

Application domain	Reference	Choice alternatives	Type of choice model	Social influence variable specification
				Definition of reference groups	Approach to measure the group behaviour	Measurement of the group behaviour
Travel mode choice	Dugundji and Walker (2005)	(1) Public transit (2) Bicycle/ motorcycle (3) Car driver/passenger	Mixed-cross nested logit	1. People living in the same residential district 2. People living in the same postcode 3. People having similar socio-economics 4. People in the overlap between groups 1 and 3 5. People in the overlap between groups 2 and 3	Proxy variable	Mode share of each travel mode
	Dugundji and Gulyás (2008)	(1) Public transit (2) Bicycle/ motorcycle (3) Car driver/ passenger	Logit/Nested logit	People living in the same residential district and having similar socio-economics	Proxy variable	Mode share of each travel mode
	Goetzke (2008)	(1) Drive alone (2) Transit	Logit	Neighbours: - including the 40 geographically closest observations of each individual - excluding observations living further away than 1.2 km from each individual	Proxy variable	Percentage of transit-use
	Goetzke and Rave (2011)	(1) Bicycle (2) Other mode	Logit	People living in the same municipality	Proxy variable	Percentage of bicycle use (corrected for endogeneity)
	Walker et al. (2011)	(1) Public transit (2) Bicycle/ motorcycle (3) Car driver/ passenger	Logit	1. People living in the same post code 2. People having similar income level	Proxy variable	Mode share of each travel mode (corrected for endogeneity)
	Dugundji and Gulyás (2013)	(1) Public transit (2) Bicycle/ motorcycle (3) Car driver/ passenger	Nested logit	1. People living in the same residential district 2. People having similar socio-economics 3. People in the overlap between groups 1 and 2 4. People living in the same post code and having similar socio-economics	Proxy variable	Mode share of each travel mode
	Pike (2014)	(1) Bike (2) Drive (3) Bus	Logit	1. Social network members (max. five persons selected by ego) 2. People living within 1.25 miles	1. Ego-centric 2. Proxy variable	1. Proportion of alters using each travel mode 2. Mode share of each travel mode
	Pike (2015)	(1) Bicycle (2) Other mode	Probit	Social network members (max. five persons selected by ego)	Ego-centric	Proportion of alters using bicycle (corrected for endogeneity)
	Pike and Lubell (2016)	(1) Drive (2) Bus (3) Bike	Logit	Social network members (max. five persons selected by ego)	Ego-centric	Proportion of alters using bicycle
Car/bicycle ownership	Goetzke and Weinberger (2012)	(1) Forgoing auto ownership (2) Owning least one automobile	Probit	Households in the same census tract	Proxy variable	Percentage of households with automobile (corrected for endogeneity)
	Maness and Cirillo (2016)	(1) Owning a bicycle (2) Not owning a bicycle	Latent class choice	Households in the same metropolitan area	Proxy variable	Percentage of households with bicycle (Endogeneity was checked, but it was not found to be present.)
Electric vehicle purchase	Kuwano et al. (2012)	(1) Gasoline (2) Hybrid (3) Electric	Latent class choice	Overall market	Experimental	Market share of electric vehicle sales
	Rasouli and Timmermans (2013); Rasouli and Timmermans (2016); Kim et al. (2014)	(1) Purchase electric vehicles (2) Do-nothing	Logit; mixed logit; hybrid choice	1. Friends 2. Family 3. Colleagues 4. Peers 5. Reviewers	Experimental	1–4. Percentage of members who purchase electric vehicle; 5. Opinion (positive or negative, etc.)
Joining car-sharing	Kim et al. (2016)	(1) Do-nothing (2) Buy a car (3) Join car-sharing	Regret-based hybrid choice	1. Family (three persons selected by ego) 2. Friends (three persons selected by ego) 3. Others (three persons selected by ego)	Combined ego-centric and experimental	Interpersonally weighted average number of alters who join car-sharing

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