Data-Driven Cycling Policy Guidance using GIS

Sustainable mobility is a major societal challenge. Cycling is considered a particularly attractive option for urban mobility. However, cycling does not capture significant shares of the modal split except for some select cities globally. We want to shed light on this cycling shortfall by means of a data-driven approach based on GIS data and highlight possibilities of cycling policy decision support through IS, also considering e-bikes. To this end, we first illustrate the value of geographic data for improving statistical estimates of modal splits. Subsequently, we present a data-driven framework for cycling policy guidance. We illustrate this approach by exploring the efficacy of varying cycling policies and determine to which extent cycling can benefit from certain policies. Thereby, we highlight that e-bikes could fill a gap in urban individual mobility and stakeholder’s decision support.