Research Topic Title: Providing insights into multi-day traveller behaviour to inform sustainable transport policies and practices
A good understanding of traveller behaviour underpins all policies which are effective in influencing travel behaviour to reduce congestion, increase activity levels, improve air quality and/or reduce carbon emissions. To encourage a more substantial change in behaviour which can be maintained over a longer period of time, we need to focus on people’s travel behaviour over weeks and months, rather than focusing on a particular trip or a particular day only. We also need to be prepared for the fundamental changes in transport systems which may occur over the coming decades, such as driverless cars and a shift away from individual vehicle ownership.
During the fellowship I am building on the methods I developed during my PhD to gain insights into multi-day travel behaviour. These insights are important as they allow us to examine the suitability of transport costs charged weekly, monthly or annually, and the impact on travel patterns of broader societal changes, such as changing working patterns and changing family dynamics. I am working with a charity, a local authority and a bus operator to see how the methods can provide useful insights relating to their specific priorities using different kinds of data. I will also be developing my skills through courses in data analytics and science communication.
Mentor: Associate Professor Kiron Chatterjee
Crawford, F., Watling, D. P. & Connors, R. D. (2018) Identifying road user classes based on repeated trip behaviour using Bluetooth data. Transportation Research Part A: Policy and Practice, 113, 55-74.
Crawford, F., Watling, D. P. & Connors, R. D. (2017) A statistical method for estimating predictable differences between daily traffic flow profiles. Transportation Research Part B: Methodological, 95, 196-213.