Is Sydney a 30-minute city? Big data analytics assisting to bring political rhetoric into practice

In 2016 the Australian Federal Government launched the ‘Smart City Plan’. One of the goals is that Australian cities should be planned, designed and managed in a way that residents are able to perform daily trips for their needs, such as commuting to work, within a 30-minute travel time (one way). This was coined as the ’30-minute city’ goal. The rationale is based on empirical evidence from international research that there are some universal rules driving human behaviour associated to urban mobility. Despite some consistency among findings from aggregated studies, research with disaggregated data has failed to find similar results, and therefore the validity of a travel time budget is still a contested concept. The chapter argues that the mean travel time budget concept is a highly coarse and aggregated metric that does not respond to the actual government needs for metrics able to assist in the planning for smart, healthy and resilient urban mobility. Enabled by big data and advanced data analytical and visualisation methods, this study recommends and tests a set of related metrics that unravel spatial and socio-economic dimensions of the travel time within cities. It presents the results of an investigation of travel time distributions of commuting trips by public transport in Greater Sydney (Australia’s largest city) to employment centres based on data from the automated Opal smartcard ticketing system. This is big data with fine spatial and temporal resolutions. By analysing individual (expenditure) and aggregated (budget) travel times by location, this study assesses to what extent each employment centre deviates from the 30-minute travel time goal and qualifies the socio-economics involved in different ranges of travel time. The results are discussed here and communicated through an interactive online dashboard.