This paper models the availability of bikes at San Francisco Bay Area Bike Share stations using machine learning algorithms. Random Forest (RF) and Least-Squares Boosting (LSBoost) were used as univariate regression algorithms, and Partial Least-Squares Regression (PLSR) was applied as a multivariate regression algorithm. The univariate models were used to model the number of available bikes at each station. PLSR was applied to reduce the number of required prediction models and reflect the spatial correlation between stations in the network. Results clearly show that univariate models have lower error predictions than the multivariate model. However, the multivariate model results are reasonable for networks with a relatively large number of spatially correlated stations. Results also show that station neighbors and the prediction horizon time are significant predictors. The most effective prediction horizon time that produced the least prediction error was 15 minutes.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Modeling bike availability in a bike-sharing system using machine learning




    Publication date :

    2017-06-01


    Size :

    395332 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    SHARING BIKE AND SHARING BIKE SYSTEM

    YUE WEI | European Patent Office | 2019

    Free access

    Dynamic linear models to predict bike availability in a bike sharing system

    Almannaa, Mohammed H. / Elhenawy, Mohammed / Rakha, Hesham A. | Taylor & Francis Verlag | 2020


    Bike-Sharing-Systeme

    Brinkmann, Jan | Springer Verlag | 2023


    Bike Sharing Systems

    Bracher, Tilman / Aichinger, Wolfgang / Wiechmann, Susanne | FID move | 2012

    Free access

    Incremental Learning Models of Bike Counts at Bike Sharing Systems

    Almannaa, Mohammed H. / Elhenawy, Mohammed / Guo, Feng et al. | IEEE | 2018