Understanding bicycling in cities using system dynamics modelling.

Area of Science:

• Urban planning and transportation science
• Systems dynamics and policy analysis
• Public health and environmental studies

Background:

• Urban cycling offers significant health and environmental benefits.
• Effective policy implementation requires understanding complex, dynamic urban cycling systems.
• Collaborative approaches integrating policy, community, and research are needed.

Purpose of the Study:

• To develop a dynamic causal model of urban cycling.
• To build consensus on the optimal order of policies for increasing cycling.
• To address context-specific challenges in different urban environments.

Main Methods:

• Participatory system dynamics modeling was employed.
• Causal loop diagrams (CLDs) were developed for Auckland, London, and Nijmegen.
• Qualitative interviews and workshops informed CLD development.

Main Results:

• Common reinforcing patterns identified: increased political will, safety in numbers, and normalization of cycling.
• Growth limitations varied by context, including perceived danger (Auckland, London) and congestion (Netherlands).
• Context-specific barriers to normalization were observed in London.

Conclusions:

• A generalizable dynamic causal theory for urban cycling can guide policy recommendations.
• Participatory methods ensure stakeholder knowledge is integrated with research evidence.
• Further research in diverse global contexts, including low-middle income countries, is recommended.