TY - JOUR
T1 - Hybrid cluster-regression approach to model bikeshare station usage
AU - Hyland, Michael
AU - Hong, Zihan
AU - Pinto, Helen Karla Ramalho de Farias
AU - Chen, Ying
N1 - Funding Information:
The authors are thankful to the Northwestern University Transportation Center for financially supporting this research. The authors are also thankful to Professor Hani S. Mahmassani for his guidance and support. The authors remain solely responsible for the content of this paper.
Publisher Copyright:
© 2017 Elsevier Ltd
PY - 2018/9
Y1 - 2018/9
N2 - This paper proposes a hybrid approach to model usage at public bikeshare system (PBS) stations. The proposed Cluster Stations and Regress (CSR) modeling approach involves first clustering PBS stations based on the types of trips they attract using k-means or fuzzy c-means clustering techniques. After obtaining station-cluster membership values for each station, we estimate multilevel mixed-effect regression models with interactions between the station-cluster membership variables and determinants of PBS station usage. Determinants considered in the empirical models include the socio-demographic and commute characteristics of the residents in each PBS station's census tract, weather variables, temporal variables, and PBS station proximity to restaurants, jobs, transit stops, rail stations, the CBD, bicycle infrastructure, and other PBS stations. The model results clearly indicate that determinants of PBS station usage vary across station-clusters and including station-cluster interaction terms significantly improves model fit. Additionally, the results of cross-validation tests indicate that the CSR approach is a promising method to model monthly PBS station usage. The empirical results also clear up conflicting findings in the literature in terms of the impact of nearby PBS stations on station usage. We find that station usage increases with the number of other PBS stations within 1–5 km for member trips. However, after controlling for this effect, station usage decreases as the number of other PBS stations within 0.8 km increases.
AB - This paper proposes a hybrid approach to model usage at public bikeshare system (PBS) stations. The proposed Cluster Stations and Regress (CSR) modeling approach involves first clustering PBS stations based on the types of trips they attract using k-means or fuzzy c-means clustering techniques. After obtaining station-cluster membership values for each station, we estimate multilevel mixed-effect regression models with interactions between the station-cluster membership variables and determinants of PBS station usage. Determinants considered in the empirical models include the socio-demographic and commute characteristics of the residents in each PBS station's census tract, weather variables, temporal variables, and PBS station proximity to restaurants, jobs, transit stops, rail stations, the CBD, bicycle infrastructure, and other PBS stations. The model results clearly indicate that determinants of PBS station usage vary across station-clusters and including station-cluster interaction terms significantly improves model fit. Additionally, the results of cross-validation tests indicate that the CSR approach is a promising method to model monthly PBS station usage. The empirical results also clear up conflicting findings in the literature in terms of the impact of nearby PBS stations on station usage. We find that station usage increases with the number of other PBS stations within 1–5 km for member trips. However, after controlling for this effect, station usage decreases as the number of other PBS stations within 0.8 km increases.
KW - Bikesharing
KW - Clustering
KW - Demand modeling
KW - Multilevel mixed models
KW - Shared mobility
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U2 - 10.1016/j.tra.2017.11.009
DO - 10.1016/j.tra.2017.11.009
M3 - Article
AN - SCOPUS:85038376245
SN - 0965-8564
VL - 115
SP - 71
EP - 89
JO - Transportation Research, Part A: Policy and Practice
JF - Transportation Research, Part A: Policy and Practice
ER -