The potential for bike riding across entire cities: quantifying spatial variation in interest in bike riding

Abstract

Background: Riding a bike is beneficial for health, the environment and for reducing traffic congestion. Despite this, bike riding participation in the state of Victoria, Australia, is low. To inform planning and practice, there is a need to understand the proportion of the population ("the near-market") that are interested in riding a bike, and how this varies across regions. The Geller typology classifies individuals into one of four groups, based on their confidence to ride a bike in various infrastructure types, and frequency of bike riding. The typology has been used at a city, state and country-wide scale, however not at a smaller spatial scale. We aimed to characterise and quantify the distribution of the Geller typology within Local Government Areas (LGAs) in the state of Victoria, Australia. Methods: An online survey was conducted in 37 LGAs in Victoria, including all LGAs in Greater Melbourne, and a selection of six key regional centres. Participants were recruited from an opt-in online research company panel with the objective of recruiting a representative sample of adults across each LGA. The Geller typology classified individuals as either: "Strong and Fearless", "Enthused and Confident", "Interested but Concerned", or "No Way No How". "Interested but Concerned" participants are those that would ride a bike if protected infrastructure were provided. Results: The survey was completed by 3999 individuals. Most participants owned a bike (58%), however only 20% rode at least once per week. The distribution of the Geller groups was: "Strong and Fearless" (3%), "Enthused and Confident" (3%), "Interested but Concerned" (78%), and "No Way No How" (16%). While variation in the distributions of the Geller groups was observed between LGAs within Greater Melbourne., the "Interested but Concerned" group, reflecting people who are comfortable riding only in protected lanes or off-road paths, was high across all LGAs and all demographic sub-groups. Even though the frequency of riding a bike was lower in women, interest in riding a bike was high and comparable to men. Participants who resided in the outer urban fringe regions of Greater Melbourne had high interest, but low participation in bike riding. Conclusions: While there was variation in interest in bike riding across an entire metropolitan region and across population groups, interest was high across all areas and demographics. Our results show the potential for substantial increases in cycling participation, but only when high-quality cycling infrastructure is provided. Further research is required to understand the policy and practice barriers to equitable provision of protected infrastructure.

Association between network characteristics and bicycle ridership across a large metropolitan region

“…During the study period, only 8 km of the 1173 km (0.7%) of the on-road infrastructure were protected bike lanes, 16 and it is acknowledged that marked on-road bicycle lanes are inadequate in creating safe spaces for people on bikes and are likely only supportive of bicycling for the most confident riders. 4,31 This also reflects current ridership in our region in which the vast majority of people who ride bikes are considered 'Strong and fearless' (people who are comfortable riding with traffic in any road environment) and 'Enthused and confident' (people who are comfortable riding in traffic, but prefer bike lanes and like using segregated facilities). 4 There has been some progress towards better characterising bicycle networks, such as the use of multi-objective methodologies to assess bikeability between origindestination locations over an entire network.…”

“…4,31 This also reflects current ridership in our region in which the vast majority of people who ride bikes are considered 'Strong and fearless' (people who are comfortable riding with traffic in any road environment) and 'Enthused and confident' (people who are comfortable riding in traffic, but prefer bike lanes and like using segregated facilities). 4 There has been some progress towards better characterising bicycle networks, such as the use of multi-objective methodologies to assess bikeability between origindestination locations over an entire network. 32 However, it is evident that we need to advance the science of measuring AAA bicycle networks to support the implementation of such networks for health, sustainability and equity benefits.…”

“…1,2 The presence and quality of bicycling infrastructure has a significant impact on bicycling, 3 and there is considerable scope for increases in bicycling participation when high-quality and connected infrastructure is provided. 4 The length of bicycling infrastructure and the extent of newly implemented bicycling infrastructure are commonly used in planning documents and academic studies to assess a city's bicycling network. 5,6 However, there is growing recognition of the importance of how 'connected' the network is; that is, enabling people to use continuous, safe and low-stress routes to access everyday destinations.…”

Developing urban biking typologies: quantifying the complex interactions of bicycle ridership, bicycle network and built environment characteristics

“…It is well known that the presence and quality of bicycling infrastructure has a significant impact on bicycle ridership, 7 and there is considerable scope for increases in bicycle ridership participation when high-quality and connected infrastructure is provided. 8 Numerous studies have explored the association between bicycle network, population and land use characteristics, and measures of bicycle ridership (such as mode share). 2,[9][10][11][12][13][14][15] These studies have been important in quantifying the importance of built environment characteristics and connected bicycle networks in enabling bicycling.…”

“…22 Previous research has also develop bicyclist/population typologies based on individual characteristics, such as the Geller Typology, which characteristics individuals based on their confidence riding in a variety of road/street/path configurations. 8,23,24 However, to our knowledge, typologies have not previously been used to measure the complexity of the interaction between bicycle mode share, bicycle infrastructure, and population and land use characteristics. Therefore, this study aimed to develop novel urban biking typologies using unsupervised machine learning methods to classify the complex interaction of bicycle ridership, bicycle network, population and land use characteristics.…”