Exploring the Potentials of Open-Source Big Data and Machine Learning in Shared Mobility Fleet Utilization Prediction

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The urban transportation landscape has been rapidly growing and dynamically changing in recent years, supported by the advancement of information and communication technologies (ICT). One of the new mobility trends supported by ICT is shared mobility, which has a positive potential to reduce car use externalities. These systems’ recent and sudden introduction was not adequately planned for, and their rapidly growing popularity was not expected, which resulted in the urgent need for different stakeholders’ intervention to ensure efficient services’ integration within the urban transportation networks and to grant an effective system operation. Several challenges face shared mobility, including fleet size management, vehicle distribution, demand balancing, and the definition of equitable prices. In this research, we developed a practical, straightforward methodology that utilizes big open-source data and different machine learning (ML) algorithms to predict the daily shared-e-scooter fleet utilization (the daily number of trips per vehicle) that could be used to drive the system’s operation policies. We used four ML algorithms with different levels of complexity, namely; Linear Regression, Support Vector Regression, Gradient Boosting Machine, and Long Short-Term Memory Neural Network, to predict the fleet utilization in Louisville, Kentucky, using the knowledge the models get from the training data in Austin, Texas. The Gradient Boosting Machine (LightGBM) was the model with the best performance prediction based on the different evaluation measures. The most critical factors impacting daily fleet utilization prediction were temporal time series features, sociodemographics, meteorological data, and the built environment.