Taking another look at cycling demand modeling: A comparison between two cities in Canada and New Zealand

Abstract

Cities around the world are investing in their cycle network as a way to reduce traffic congestions and improve population health. For effective infrastructure investment decision-making, it is critical to understand the factors affecting cycling demand. This study investigates the combined effects of spatial and temporal factors on the demand of cycling in Auckland, New Zealand and Kelowna, Canada. A latent segmentation-based negative binomial (LSNB) model is developed utilizing daily cycling counts over a year from Auckland and Kelowna. The LSNB model captures unobserved heterogeneity based on the temporal attributes, and land use and neighborhood characteristics of the cycling facilities. The models confirm the influence of weather, bicycle facility type, built environment, traffic, land use, and accessibility attributes. The model results suggest that higher temperature, lower rainfall, increased length of shared paths, lower AADT, and closer distance to bus stop are likely to increase cycling demand in Auckland. The model also confirms heterogeneity. For example, road-connectivity index and bike index vary across the urban and suburban areas of Auckland. For Kelowna, higher temperature, lower rainfall, lower snowfall, and closer distance to water bodies are likely to increase cycling demand. In the case of heterogeneity, the effects of road-connectivity index, bike index, and AADT varies over the urban and suburban areas of Kelowna. Analysis suggests that increasing bike index is one of the critical factors to increase cycling demand in the suburban areas of Auckland and Kelowna.