Understanding the determinants of young commuters’ metro-bikeshare usage frequency using big data

This paper examines the determinants of young commuters’ frequency of using public bikes as a feeder mode to/from metro. Using three-week metro- and public bike- smart card data from Nanjing, 1,154 metro-bikeshare commuters aged 18–35 were extracted. As possible factors influencing the use of the combined mode, individual and household socio-demographics, travel-related attributes and built environment characteristics were extracted from multi-source data. A negative binomial regression model was used to examine the effects of these factors on usage frequency. We found that young commuters are the biggest group using metro-bikeshare system. They use public bikes frequently to transfer to/from metro when the cycling time is less than 10 min and the transfer happens during the morning peak. Built environment characteristics also influence usage frequencies, with high-density bike facilities being related to higher cycling rates in inner areas, and residential /employment locations related to lower rates of cycling in the core areas. This suggests that different measures and policies designed to encourage the integrated use of metro-bikeshare should be put forward for different regions.