Achieving ‘Active’ 30 Minute Cities: How Feasible Is It to Reach Work within 30 Minutes Using Active Transport Modes?

Abstract

Confronted with rapid urbanization, population growth, traffic congestion, and climate change, there is growing interest in creating cities that support active transport modes including walking, cycling, or public transport. The ‘30 minute city’, where employment is accessible within 30 min by active transport, is being pursued in some cities to reduce congestion and foster local living. This paper examines the spatial relationship between employment, the skills of residents, and transport opportunities, to answer three questions about Australia’s 21 largest cities: (1) What percentage of workers currently commute to their workplace within 30 min? (2) If workers were to shift to an active transport mode, what percent could reach their current workplace within 30 min? and (3) If it were possible to relocate workers closer to their employment or relocate employment closer to their home, what percentage could reach work within 30 min by each mode? Active transport usage in Australia is low, with public transport, walking, and cycling making up 16.8%, 2.8%, and 1.1% respectively of workers’ commutes. Cycling was found to have the most potential for achieving the 30 min city, with an estimated 29.5% of workers able to reach their current workplace were they to shift to cycling. This increased to 69.1% if workers were also willing and able to find a similar job closer to home, potentially reducing commuting by private motor vehicle from 79.3% to 30.9%.

A study on the factors that influenced the choice of transport mode before, during, and after the first lockdown in Milan, Italy

“…Considering the three models of CBL, CDL, and CAL, we found that people who were residing outside Milan and had to commute to their work or other activities to the metropolitan area were, respectively, 5.4, and 3.7 times more likely to commute by private car and public transport versus active mode of transport. These results, as expected, are in line with prior studies ( Ashrafi & Neumann, 2017 ; Both et al, 2022 ; Cheng et al, 2019 ; De Vos et al, 2022 ; Yu et al, 2019 ). In the second model, related to the choice of transport mode during the lockdown, these explanatory variables show a different relation.…”

“…Longer commutes can cause traffic congestion, and using motorised vehicles. While, compact cities can foster shorter daily travel time and consequently encourage the use of active modes ( Both et al, 2022 ). Besides, some individual (socio-demographics) characteristics such as gender, age, income, and availability or possession of transport mode significantly determine travel mode behaviour.…”

Demographic Analysis of Active Transport Mode Users in Urban Context

“…Noting that short trips were defined as journeys lasting 30 minutes or less. This definition aligns with typical durations for active transportation modes like walking, cycling, and micro-mobility, focusing on urban travel patterns (Both et al, 2022).…”

Inference of dynamic origin–destination matrices with trip and transfer status from individual smart card data

“…Considering indicators such as flow, transfer or trip features (e.g., trip time, transfer location), studies have been able to identify mobility patterns and travel preferences, for instance: de Magalhães et al [33] say that comfort, direct lines and decrease travel time are key factors to create attractiveness for commuting private users and they are willing to walk around 500 m to transfers; Espino et al [36] analyse travel preferences and suggest that, from a policy point of view, decrease travel time and the transfer cost encourages the use of bus transport system. Indeed, the assessment of these statistical indicators has been pivotal to aiding the operators' planning, help redesigning the network (e.g., 30 min city or even 15 min city projects), and increasing the public transport attractiveness, especially for private car users [37][38][39].…”