Cycling equity and socioeconomic disadvantage

Understanding transport choices is a vital area of research worldwide, for tackling climate change and for public health. At the national level, encouraging individuals to substitute car travel for active travel (walking and cycling) or public transport will help the UK meet its CO2 emissions targets and tackle the obesity epidemic and tackling the obesity epidemic. This modal shift has a number of knock-on benefits, such as reducing the burden on the NHS.

Modal shift can also help meet important policy objectives at the regional level. The West Yorkshire Local Transport Plan (wymetro.com/wyltp/) aims to promote connectivity while reducing carbon emissions and congestion in a growing population. Yet a full understanding of the reasons why different mode splits exist in different places is still elusive, making locally targeted policies to encourage the uptake of sustainable modes difficult to design: How do people choose their mode of travel? Which determinants are most important? And these determinants equally important for everyone? These questions have yet to be tackled based on a rigourous analysis of large scale spatio-temporal datasets.

Travel mode choice has been explored by transport planners for decades, but only recently have large datasets on mode choice at high geographical resolution been made available. Previous studies have predominantly focused on geographical characteristics of either the residential location or destination location such as population density. New large datasets (e.g. origin-destination data at Output Area level for England and Wales and open data provided by the Ordnance Survey) and software (e.g. stplanr) allows the investigation of the impact of area, desire-line (e.g. hilliness profile) and route-level characteristics (e.g. the presence of green space and cycle paths or bus stops along route options) simultaneously. The project will explore the relationship between such characteristics and mode choice. Combining these new data with theory-led mathematical and statistical modelling will improve our understanding of current mode choice and enable the creation of predictive models for future scenarios.
Aim: to exploit the recent availability of novel data sources that have previously either not been available to transport researchers or been computationally infeasible to study.
Objectives: The successful candidate will fulfill 4 key objectives during their PhD
-Review and further develop existing theories of travel mode choice
-Identify the important correlates mode choice from micro-level geographical and social data
-Use modal split data from 2001 and 2011 to identify geographical correlates of modal shift
-Integrate research models with existing open source software