TY - JOUR
T1 - Understanding the determinants of young commuters’ metro-bikeshare usage frequency using big data
AU - Liu, Yang
AU - Ji, Yanjie
AU - Feng, Tao
AU - Timmermans, Harry
PY - 2020/10
Y1 - 2020/10
N2 - 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.
AB - 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.
KW - Metro-bikeshare integration
KW - Negative binomial regression
KW - Smart card data
KW - Transfer frequency
KW - Young commuter
UR - http://www.scopus.com/inward/record.url?scp=85087360179&partnerID=8YFLogxK
U2 - 10.1016/j.tbs.2020.06.007
DO - 10.1016/j.tbs.2020.06.007
M3 - Article
AN - SCOPUS:85087360179
VL - 21
SP - 121
EP - 130
JO - Travel Behaviour and Society
JF - Travel Behaviour and Society
SN - 2214-367X
ER -