Exploring the uptake of cycling to work through an agent-based model focusing on social interactions and social norms
Reducing car use and increasing cycling can generate substantial physical and mental health benefits. Technological change alone will be inadequate to achieve emissions targets and will not resolve problems such as congestion, community severance and sedentary lifestyles. Researchers have argued for a focus on transport’s social dimensions, on people and the local scale. Yet an ESRC Scanning Study found social science still struggles to influence transport, energy and climate debates.
This ESRC funded project will use existing qualitative and quantitative datasets and workshops to develop an agent-based model exploring the potential for transitions towards more sustainable commuting. Agent based modelling (ABM) is increasingly used in social science, but is underused in this area. ABMs have been used to show how complex and recognisable social systems can be generated from small scale interactions. In ABM agents, usually individual people, behave according to specified rules but can also learn and change how they respond to events. Agents interact with other agents in geographical space and/or in social networks. They act based on the information available to them about their environment and what other agents are doing.
The model will focus on social influence, social values and social learning, and how these shape commuting practices over time in a heterogeneous population. We will investigate how behaviours might change as policies are implemented and habits disrupted and how change might spread or dissipate in a given context. The model will be parameterised for three English cities (Bristol, Cambridge and Chester) all of which have received investment in cycling but are starting from very different positions.
The project is running from February 2013 to July 2014.
Investigators
- James Woodcock (CEDAR, Principal Investigator).
- Rachel Aldred (University of Westminster)
- Zaid Chalabi (LSHTM)
- Rita Newton (University of Salford)
