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

Abstract

Numerous studies have explored associations between bicycle network characteristics and bicycle ridership. However, the majority of these studies have been conducted in inner metropolitan regions and as such, there is limited knowledge on how various characteristics of bicycle networks relate to bicycle trips within and across entire metropolitan regions, and how the size and composition of study regions impact on the association between bicycle network characteristics and bicycle ridership. We conducted a retrospective analysis of household travel survey data and bicycle infrastructure in the Greater Melbourne region, Australia. Seven network metrics were calculated (length of the bicycle network, betweenness centrality, degree centrality, network density, network coverage, intersection density and average weighted slope) and Bayesian spatial models were used to explore associations between these network characteristics and bicycle ridership. We demonstrated that bicycle ridership was associated with several network characteristics, and that these characteristics varied according to the outcome (count of the number of trips made by bike or the proportion of trips made by bike) and the size and characteristics of the study region. These findings challenge the utility of approaches based on spatially modeling network characteristics and bicycle ridership when informing the monitoring and evaluation of bicycle networks. Further efforts are required to be able to quantify network characteristics that reflect the myriad of factors that influence comfort and safety for people of all ages and abilities.

Keywords:

• Bicycling
• participation
• spatial variation
• transportation

Acknowledgements

The authors would like to acknowledge the assistance provided by the Victorian Department of Transport, and in particular, Geoffrey Simmons.

Disclosure statement

There are no conflicts of interest to declare.

Additional information

Funding

BB was supported by an Australian Research Council Future Fellowship (FT210100183). The CYCLED (CitY-wide biCycLing Exposure modeling) Study is funded by an Australian Research Council Discovery Project (DP210102089). KN and SS were supported by a National Health and Medical Research Council (NHMRC) and United Kingdom Research and Innovation (UKRI) grant (#1194959).