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Individual variability in travel behaviour: a quantitative exploration of the national travel survey on trends and predictors

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  • Full or part time
    Dr Heinen
    Dr Jopson
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Introduction
Existing research on behavioural change in transport focuses on explaining differences between individuals. Differences in behaviour within individuals (intrapersonal variability), such as variation in mode and destination, are mostly ignored. Studies consequently measure and analyse travel behaviour as if it were stable and non-variable within one individual. However, the limited research on intrapersonal variability shows that individuals do not travel with only one mode of transportation, or always at the same time of day (e.g. Heinen & Chatterjee, 2015; Kuhnimhof, 2009; Schlich & Axhausen, 2003; Nobis, 2007; Diana & Mokhtarian, 2009a&b; Susilo and Axhausen (2014)).

The means by which people combine different modes of transport within their entire mobility is one of the contemporary issues in transport research. Transport policy makers are actively promoting increasing variability in the use of modes of transport in several countries (European Commission 2011), for purposes which include reducing car dependency and making transport more sustainable and healthy.

Background
Individual stability and variability have received increasing attention from both policy makers and academics. Recent studies have focused on measuring variability at an individual level (Nobis 2007; Kuhnimhof et al. 2012; Buehler and Hamre 2015a,b) and, in the UK, one study has focused on the individuals and spatial characteristics that correlate with individual variability in mode choice using the national travel survey (NTS) (Heinen and Chatterjee, 2015).

Despite these emerging insights, it still remains unknown to what extent mode choice variability differs between trip purposes or locations. Moreover, the amount of attention that has been given to variability in the various dimensions of travel behaviour, such as the types of activities pursued, activity start times and durations, destinations visited, transport modes used and routes used has been mixed (Heinen and Chatterjee, 2015). Furthermore, although Buehler and Hamre (2015) have shown variability trends in the United States, it remains unknown towards which direction travel behaviour variability in Europe is developing despite the existence of necessary data.

Insights into variability predictors and trends are important to gain a better understanding of individual travel behaviour. This is increasingly important to encourage not only a modal shift but also to encourage a wider variety of modal options to decrease car dominance and increase quality of life and health by increasing active travel. Higher levels of variability may correspond with self-efficacy ¬to use particular modes of transport (e.g. confidence in the ability to perform certain behaviours). Self- efficacy is believed to drive behaviour change (Bandura, 1986; Strecher et al., 1986) and therefore higher levels of variability may contribute to a higher likelihood to change travel behaviour towards a more sustainable and healthy form of transport. Research is emerging that shows that variability in commute mode choice at baseline may increase the likelihood of changes in mode choice in a follow-up study (Heinen & Ogilvie (in press)). This may indicate, among other things, that differences in decision-making styles exist among individuals depending on their level of variability.

Aims and Approach
Given the potential importance of variability on travel behaviour change and the gaps in our existing knowledge regarding the trends of variability in the UK and the determinants of various aspects of travel behaviour variability in different contexts, the aims of this project are: to describe variability in travel behaviour in the UK over time and to determine the predictors of variability in travel behaviour in different geographical contexts and for different trip purposes. Given the expertise of the supervisor, it is envisioned the PhD candidate will primarily focus on variability in mode choice. However, this focus may change if explorations show more promising leads and in discussion between student and supervisor. The student is also envisioned to contribute to the greater line of research on travel behaviour change that underlies the University Academic Fellowship of Eva Heinen. In this line of research, the aim is to increase our understanding of travel behaviour change by increasing empirical evidence on the effect of changes in the built and social environment.


Please visit our LARS scholarship page for more information and further opportunities: https://www.environment.leeds.ac.uk/study/postgraduate-research-degrees/lars-scholarships/

Funding Notes

Entry requirements/necessary background
Applicants must have a minimum of a UK upper second-class honours degree (2.1), or its equivalent, in an appropriate discipline such as (but not limited to) transport, geography or civil engineering.

If English is not the applicant’s first language, they must provide evidence that they meet the University’s minimum English language requirements.

Basic statistical knowledge is essential, and experience with large datasets and more advanced statistical techniques is desired.

References

Bandura A. The Explanatory and Predictive Scope of Self-Efficacy Theory. Journal of Social and Clinical Psychology. 1986; 4: 359-373.

Buehler, R., Hamre, A., in press-a. An examination of recent trends in multimodal travel behaviour among American motorists. Int. J. Sustain. Transport. doi: 10.1080/15568318.2014.945672.

Buehler, R., Hamre, A., in press-b. The multimodal majority? Driving, walking, cycling, and public transportation use among American adults. Transportation. doi: 10.1007/s11116-014-9556-z.

Diana, M., Mokhtarian, P.L., 2009a. Desire to change one’s multimodality and its relationship to the use of different transport means. Transport. Res. Part F 12, 107–119. Diana, M., Mokhtarian, P.L., 2009b. Grouping travellers on the basis of their different car and transit levels of use. Transportation 36, 455–467

Diana M, Pirra M. In progress. A comparative assessment of synthetic indices to measure multimodality behaviours. Transportmetrica A: Transport Science. In progress.

EC, 2014. Do the right mix. European Commission’s Sustainable Urban Mobility Campaign. European Commission Directorate-General for Mobility and Transport. (accessed 08.09.14).

Heinen E., Chatterjee K. The same mode again? An exploration of mode choice variability in Great Britain using the National Travel Survey. Transportation Research Part A. 2015; 78: 266-282.

Heinen E, Ogilvie D. Variability in baseline travel behaviour as a predictor of changes in commuting by active travel, car and public transport: a natural experiment. Journal for Transport and Health. In press.

Kuhnimhof, T., Buehler, R., Wirtz, M., Kalinowska, D., 2012. Travel trends among young adults in Germany: increasing multimodality and declining car use for men. J. Transport Geogr. 24, 443–450.

Kuhnimhof, K., 2009. Measuring and modeling multimodal mode use in the longitudinal section. In: Paper Presented at the Meeting of the Transportation Research Board 88th Annual Meeting, Washington, D.C., USA.

Schlich, R., Axhausen, K.W., 2003. Habitual travel behavior. Evidence from a six-week travel diary. Transportation 30, 16–36.

Nobis, C., 2007. Multimodality – facets and causes of sustainable mobility behaviour. Transport. Res. Rec. 2010, 35–44.

Strecher VJ, McEvoy DeVellis B., Becker MH, Rosenstoc IM. The Role of Self-Efficacy in Achieving Health Behavior Change. Health Education Behaviour 1986;13(1): 73-92.

Susilo YO, Axhausen KW. Repetitions in individual daily activity-travel-location patterns: a study using the Herfindahl-Hirschman Index. Transportation. 2014; 41: 995-1011.

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