Reflections on our end of project seminar

Back on 16 June 2014 we held an end of project event at the Department of Transport (DfT) in London. DfT was a partner in our project (a ‘policy user’), helping to direct the project, and we were delighted that it agreed to host the end of project event. It was actually two events. First of all, there was an hour-long high-level policy briefing with DfT staff. Then in the afternoon we held a fuller seminar, open to those from outside DfT. There were about 30 people in attendance at each with the afternoon audience including university academics, local authority transport practitioners and transport and marketing consultants.

On each occasion we presented our research findings (see slides and evidence summaries under project outputs) and had a discussion afterwards about them and their implications.  Our findings show how changes in car ownership and commuting mode are far more likely when people experience certain events in their lives. This provoked interest from the audience in gaining a richer understanding of why this was the case. For example, why is there a tendency for two-car households to get rid of a car at the time they have a child? Other questions that were raised about the knowledge gained from the research are listed at the end of this article. We are keen to continue the research in future so that we can further develop understanding of the role of life events in behavioural change.

At the end of our presentations, we put forward the argument that any policy or initiative to change behaviour which ignores life events is missing an important consideration, because people are more open to considering alternative ways of travelling at the time of life events. We were challenged to explain how our evidence could be used to inform deliverable actions. For example, how can we ‘find’ individuals that are experiencing different life events? Questions like this which were raised on how the research findings could be used to inform policy responses are again listed at the end of this article.

Since holding the event, we have spent more time considering the policy and practice implications of the research findings (benefiting also from a workshop we held with practitioners in Bristol) and have put together a set of recommendations that link our findings to potential actions. We will add these soon in another blog.


Questions about the knowledge generated:

  • Understanding the results:
    • How do life events affect different population groups (e.g. home moves for those that are retired)?
    • What characterises those that change behaviour at the time of life events (e.g. get rid of a car on having a child)?
    • What difference is there in the effects on behaviour of events that are planned and unplanned (e.g. planned job changes versus unexpected job changes)?
  • Extending the understandings gained:
    • How do changes in the transport system interact with life events in influencing travel behaviour?
    • Are those that combine use of different modes in their everyday travel more likely to change behaviour when they experience a life event?
    • How does stability of car use differ between those that are primarily car drivers and those that are car passengers/sharers?

Questions about using the research evidence to inform action:

  • New employees represent a target group with high potential to take up non-car commuting. How do we work with employers to reach this group in a timely manner? How do we persuade employers of the co-benefits (e.g. employee wellbeing)? How do we tailor our approach to work with different types of employers and work locations with different transport provision?
  • In recent years with the economic downturn there has been opportunity to ‘go with the grain’ in assisting individuals to reduce car use and cut costs. How is this best approached?
  • In places where there is investment in new transport systems, how do we identify those undergoing life events who will be more amenable to use new options?
  • Can we build analytical tools to predict locations where there are people in the population who are likely to experience life events?
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