Social Networking, 2014, 3, 147-149
Published Online April 2014 in SciRes.
How to cite this paper: Sharmeen, F., van den Berg, P. and Timmermans, H. (2014) Networked Individual in Networked City:
Reviewing Social Network in Transportation Literature. Social Networking, 3, 147-149.
Networked Individual in Networked City:
Reviewing Social Network in Transportation
Fariya Sharmeen, Pauline van den Berg, Harry Timmermans
Urban Planning Group, Eindhoven University of Technology, Eindhoven, Netherlands
Email: ar meen @tu , H. J.P .Ti mmer ma ns@t u e.n l
Received 3 February 2014; revised 5 March 2014; accepted 2 April 2014
Copyright © 2014 by authors and Scientific Research Publishing Inc.
This work is licensed under the Creative Commons Attribution International License (CC BY).
http://creativ ecommon icens es/by/4.0/
Networks and networking are predominant in individuals’ social and professional life. Social media has made
networking even simpler in virtual world. To connect and maintain the same in real world, one must depend on
transport network. Perhaps it is the effect of the ease and expediency of virtual media that social and recreational
trips are increasing every day, reasoning the growing interest of transportation researchers on the indepth ana-
lyses of social networks.
In recent years, a stream of transportation literature can be identified emphasizing the importance of studying
social networks to improve models of travel demand. Leisure and social activities are responsible for a growing
portion of travel. As people’s social networks are the main motivator for social activity-travel, research into the
characteristics of people’s social networks can contribute to an understanding of social travel demand [1]. The
international travel behaviour community has picked up this plea [2]-[5]. Recent research into the effect of so-
cial networks on transportation has evolved along two lines. The first line of research focuses on the influence
that people’s social networks have on travel decisions by the exchange of information and opinions. The second
line of research focuses on the more direct effects of social networks [6]. Within these lines of research, a dis-
tinction can be made between agent based simulations and empirical studies.
The persuasive power of social networks or the activity and travel party cannot be ignored. It can be preroga-
tive in deciding the location, travel mode, duration, distance and other aspects of the trip. Empirical studies
comprising the link between personal social networks and travel behavior is still a promising field. In recent
times, researchers have addressed several aspects of the influence of social network and activity and travel plan-
ning, such as duration, frequency, start time, distance, mode choice, etc (see [5], for detailed review). The re-
ported results are intuitive and demand due attention by the researchers as well as the policy makers and practi-
tioners to incorporate the social context in activity and travel demand forecasting.
To see the big picture, we need to broaden our view from egocentric social network to a population wide pro-
jection. Agent based simulation environment is a potential platform to serve the purpose and has been utilized
accordingly. Further to simulate the potentiality of a tie formation, the negotiation of a social meeting (mode
choice, place, time etc) multi-agent simulation has been employed in transportation research [3] [7] [8].
In order to improve our understanding of the role of social networks in social activity-travel behavior, a series
F. Sharmeen et al.
of challenges remain on the future agenda. A first challenge is to negate the idea that personal social networks
are stagnant. So far, social networks in transportation research have been studied as if they were. On the contrary,
it is indeed very much dynamic. People’s social network size and composition change over time, sometimes
triggered by lifecycle events, such as changing household composition, job or home location. The contemporary
shift in travel behavior analysis towards dynamics necessitates the inclusion of social network dynamics. The
concept has been coined recently [5].
Regarding data collection on social networks and social activity-travel patterns still some challenges remain
as well. To get a good impression of the heterogeneity of leisure activities, it is desirable to collect longitudinal
diary data. Traditional travel diary surveys collect one or two days of travel data from participants. While cross-
sectional travel diary surveys are useful in determining the overall average travel behaviour of the regional pop-
ulation, they do not capture repetitive patterns in social activities, for instance weekly routines of people. New
GPS technologies provide a promising way of collecting longitudinal travel data without asking too much effort
from respondents.
In addition, the study of the effect of ICT’s on social activity-travel remains a topic for future research. Al-
though this topic has been studied recently [4] [9], the possibilities of ICT’s are increasing rapidly. These
changes will affect social travel, for instance in arranging a social trip (e.g. making reservations, buying tickets,
checking routes, weather and travel conditions), making additional research necessary.
A further challenge is to link the a-spatial aspect of social network to the spatial one. There is a spatial facet
attached to an individual’s social network as far as travel and transportation is concerned. The distance and ac-
cessibility of the peers should matter in planning and maintaining social networks [10]. This topic has been
largely overlooked by contemporary research. The social environment should not be studied in isolation from
the geographical environment.
Finally, the ageing of the population is a topic that deserves future research with regard to social networks and
travel behavior. On average, older people have more leisure time compared to younger (working) people. They
may therefore spend more time on social activities. On the other hand, the elderly on average have a smaller so-
cial network and may be less mobile. Social network analysis can give relevant insights in social activity-travel
behavior of senior citizens, as well as matters of accessibility, social capital and social equity.
The analysis of social networks has a far reaching potential in understanding almost all aspects of human be-
haviour. These potentials have been realized for long. A recent growing field of exploration is activity and travel
behaviour. The history of social networks and transportation literatures is nonetheless in an exploration stage.
There have been some commendable works already. Further comprehension and integration of the local social
context, social externalities and social dynamics to the travel behaviour models remain on the contemporary
[1] Axhausen, K.W. (2005 ) Social Networks and Travel: Some Hypotheses. In: Donaghy, K.P., Poppelreuter, S. and Ru-
dinger, G., Eds., Social Aspects of Sustainable Transport: Transatlantic Perspectives, Ashgate, Aldershot, 90-108.
[2] Carras co, J. and Miller, E. (2006) Exploring the Propensity to Perform Social Activities: A Social Network Approach.
Tra ns por tat ion , 33, 463-480. http:/ /d x. doi .o rg/1 0. 10 07 /s11 1 16 -006-8074-z
[3] Dugundji, E. R . , Páez, A., Arentze, T.A., Walker, J.L. Carrasco , J. A. Marchal, F. and Nakanishi, H. (2011) Transporta-
tion and Social Interactions. Transportation Research Part A: Policy and Practice, 45, 239-247.
[4] van den Berg, P., Arentze, T. and Timmermans, H. (2013) A Path Analysis of Social Networks, Telecommunication
and Social Activity-Travel Pat terns. Transportation Research Part C, 26, 256-268.
[5] Sh armeen, F., Arentze, T. and Timmer mans, H. (2010) Modelling the Dynamics between Social Networks and Activi-
ty-Travel Behavior: Literature Review and Research Agenda. Proceedings 12th World Conference on Transport Re-
search, Lisbon, 11-15.
[6] Arentze, T. and Timmermans, H. (2008) Social Networks, Social Interactions, and Activity-Travel Behavior: A Frame-
work for Microsimulation. Environment and Planning B: Planning and Design, 35, 1012-1027.
[7] Ronald, N., Dignum, V., Jonker, C., Arentze, T. and Timmermans, H. (2012) On the Engineering of Agent-Based Si-
mulations of Social Activities with Social Networks. Information and Software Technology, 54, 625-638.
F. Sharmeen et al.
[8] Hackne y, J. and Marchal, F. (2011 ) A Coupled Multi-Agent Microsimulation of Social Interactions and Transportation
Behavio r. Transportation Research Part A: Policy and Practice, 45, 296-309.
[9] Sharmeen, F., Arentze, T. and Timmermans, H. (2013) A Multilevel Path Analysis of Social Network Dynamics and
The Mutual Interdependencies Between Face-to-Face and ICT Modes of Social Interaction in The Context of Life-
Cycle Events. In: Roorda, M.J. and Miller, E. J . , Eds., Travel Behaviour Research: Current Foundations, Future Pros-
pects, Lulu Press, Toronto, 411-432 .
[10] Sharmeen, F., Arentze, T.A. and Timmermans, H.J.P. (2014) Dynamics of Face-To-Face Social Interaction Frequency:
Role of Accessibility, Urbanization, Changes in Geographical Distance and Path dependence. Journal of Transport
Geography, 34, 211-220.