The social constraints people face are key to understanding travel behaviour

Travel decisions are sometimes or partly made at the level of a group instead of being made by an individual. For instance, parents do not just travel from home to work, but also bring children to school or day-care, do some shopping, drive children to hobby activities, etc. These constraints limit peoples’ flexibility in terms of mode choice and time of travel. Moreover, travel is necessary for building and maintaining one’s social capital. As a result, travel is increasingly taking place outside the peak hours, and the environmental implications of transport are gaining in importance compared to the congestion impact.

• These constraints reduce people’s flexibility in terms of mode and time choice.

• Therefore, the share of off-peak travel is increasing.

• With more off-peak travel, the environmental consequences of transport gain in importance.

• Travel is often caused by the need to maintain one’s social capital.

• People are constrained by other household members in their mobility choices.

Aristotle already wrote that man is, by nature, a social animal. Some minimal introspection is sufficient to understand that social aspects are key in several mobility related decisions, including the decision to travel in the first place.

A first reason why people’s mobility behaviour is affected by others is simply that decisions are sometimes or partly made at the level of a group instead of being made by a single traveller (Chorus, 2012). For instance, in the context of a household, parents do not just travel from home to work, but also take children to school or day-care (and bring them home again), do some shopping for the household, drive children to hobby activities, visit their own (maybe mobility impaired) parents etc. The recognition of these household interactions is the subject of the so-called Activity Based Modelling approach to travel modelling.

Such activity-based models focus (1) on the relationship between all activities executed in the course of a day/week/month and not just on the connection between activities in one travel tour and (2) on the interaction between household members. Thus, an activity-based travel-demand model aims at predicting which activity is executed, where, when, for how long and which transport mode is used to get to the desired location (Arentze & Timmermans, 2005a).

In the long term, individuals build up expectations and beliefs based on the outcomes of their behaviour. New experiences cause these expectations and beliefs to be updated, and behaviour to change accordingly. This type of dynamics is subject to a high degree of inertia, implying a slow response rate. For instance, long term dynamics can cause household relocation or a change in the car availability of the household (Arentze & Timmermans, 2005b; Arentze, 2005; Goodwin, 1990).

Understanding household interactions is highly relevant from a policy point-of-view. Suppose for instance that a public authority considers implementing road pricing as an instrument to promote modal shift, or to encourage people to switch their travel from peak hours to off-peak. If travel tours are considered in isolation, they may lead to the conclusion that people are more flexible in their choices of travel mode and travel time than they actually are. Explicitly integrating the activities that precede the main trip (typically the home-work trip) in the analysis gives a more realistic view of the real substitution possibilities.

A second social constraint that affects travel behaviour has been identified by Carrasco et al. (2008): individuals’ travel behaviour is conditional upon their social network. In other words, social networks are a key cause of travel, rather than an attribute of travel.

Similarly, Axhausen (2008) argues that travel is necessary for building and maintaining one’s social capital. This travel is connected to the locational choices of the network’s members. As a result, the destination choice of an individual is the result of joint choices with other persons. Therefore, he argues that a person’s social network geography and network-based decision making should be integrated in the analysis. Once travel is seen in this light, it also recasts the importance of leisure travel as central to the social capital of a population.

Dugundji et al. (2011) say that, due to large scale migration and population aging, “one can expect an increase in travel that will not fall into the home-to-work category and that will be strongly influenced by social context such as leisure trips, as well as demand for alternative modes of mobility that will rely on broader social support” (p.244). They see a self-reinforcing mechanism where the travel demand results from the need for social contact and this increase in travel demand itself leads to more opportunities for generating social interaction.

Again, there are clear policy implications to be drawn from this. Transport policy typically focuses on what happens during the peak period, as the dimensions of transport infrastructure are determined by the need to accommodate peak demand. If travel for social reasons becomes relatively more important, off-peak travel also becomes more important, and the central problem of mobility policy shifts from congestion management to its environmental impact.

  • Arentze, T.A. and H.J.P. Timmermans (2005a), ALBATROSS – version 2.0: a Learning-Based Transportation Oriented Simulation System. 1st edn. EIRASS, Technische Universiteit Eindhoven, The Netherlands.
  • Arentze, T.A., C. Pelizaro and H.J.P. Timmermans (2005), Implementation of a Model of Dynamic Activity-Travel Rescheduling Decisions: an Agent-Based Micro-Simulation Framework. Paper presented at 9th International Conference on Computers in Urban Planning and Urban Management (CUPUM).
  • Axhausen, K.W. (2008). “Social networks, mobility biographies, and travel: Survey challenges.” Environment & Planning Part B, 35, 981-996
  • Carrasco, J.A., B. Hogan, B. Wellman and E.J. Miller (2008), Collecting social network data to study social activity travel behavior: an egocentric approach, Environment & Planning B 35, 961-980.
  • Chorus, C.G. (2012), What about behaviour in travel demand modelling? An overview of recent progress, Transportation Letters: The International Journal of Transportation Research 4, 93-104.
  • Dugundji, E. and Gulyas, L. (2008). “Sociodynamic discrete choice on networks in space: Impacts of agent heterogeneity on emergent outcomes.” Environment & Planning Part B, 35, 1028-1054
  • Dugundji, E.R., A. Páez, T.A. Arentze, J.L. Walker, J.A. Carrasco, F. Marchal and H. Nakanishi (2011), Guest Editorial. Transportation and social interactions. Transportation Research Part A 45, 239–247.
  • Goodwin, P., R. Kitamura and H. Meurs (1990), Some Principles of Dynamic Analysis of Travel Behaviour, in: P. Jones (ed.), Developments in Dynamic and Activity-Based Approaches to Travel Analysis, Aldershot, England: Gower Publishing Company, 56–72.

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