Mapping Bicycling Patterns with an Agent-Based Model, Census and Crowdsourced Data

As our cities continue to grow issues such as congestion, air pollution and population health are also on the increase. Active transport can play an important part in activating multi-benefits for citizens and the city. In this research we focus our attention on understanding the patterns and behaviours of bicyclists as a form of active transport. There are a number of data sources which can be used to analyse patterns of cycling across cities. With the advent of smart phones with GPS and cycling specification apps, crowdsourced approaches can be used to acquire fine scale individual cycle travel patterns. In this research we analyse such crowdsourced data acquired through the riderlog application with specific focus on the City of Sydney. We use this rich data source along with other a more traditional journey to work and household travel survey data to create an agent based model using the open source GAMA platform. The work in this paper is early work in building a more sophisticated Agent-Based Model (ABM) to understanding cycling patterns across the City of Sydney to hence we commence by first testing the simple hypothesis is the shortest distance the main criteria for commuting by bicycle?