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Hybrid cluster-regression approach to model bikeshare station usage

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  • Hyland, Michael
  • Hong, Zihan
  • Pinto, Helen Karla Ramalho de Farias
  • Chen, Ying

Abstract

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.

Suggested Citation

  • Hyland, Michael & Hong, Zihan & Pinto, Helen Karla Ramalho de Farias & Chen, Ying, 2018. "Hybrid cluster-regression approach to model bikeshare station usage," Transportation Research Part A: Policy and Practice, Elsevier, vol. 115(C), pages 71-89.
    • Handle: RePEc:eee:transa:v:115:y:2018:i:c:p:71-89
    DOI: 10.1016/j.tra.2017.11.009
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    References listed on IDEAS

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    Cited by:

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    2. Kumar Dey, Bibhas & Anowar, Sabreena & Eluru, Naveen, 2021. "A framework for estimating bikeshare origin destination flows using a multiple discrete continuous system," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 119-133.
    3. Morton, Craig, 2020. "The demand for cycle sharing: Examining the links between weather conditions, air quality levels, and cycling demand for regular and casual users," Journal of Transport Geography, Elsevier, vol. 88(C).
    4. Leonardo Caggiani & Rosalia Camporeale & Zahra Hamidi & Chunli Zhao, 2021. "Evaluating the Efficiency of Bike-Sharing Stations with Data Envelopment Analysis," Sustainability, MDPI, vol. 13(2), pages 1-21, January.
    5. Giménez-Nadal, José Ignacio & Gracia-Lázaro, Carlos & Molina, José Alberto, 2022. "Increasing the use of public bicycles: Efficiency and demand," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 745-754.
    6. Wang, Kailai & Chen, Yu-Jen, 2020. "Joint analysis of the impacts of built environment on bikeshare station capacity and trip attractions," Journal of Transport Geography, Elsevier, vol. 82(C).
    7. Morton, Craig & Kelley, Scott & Monsuur, Fredrik & Hui, Tianwen, 2021. "A spatial analysis of demand patterns on a bicycle sharing scheme: Evidence from London," Journal of Transport Geography, Elsevier, vol. 94(C).

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