A Deep Learning Model with the Residual Network for Deployment of Shared Bikes

In recent times, shared bikes have become a new trend for improving mobility in many cities. More and more people choose shared bikes as their "final 1-mile"solution for urban transportation. However, modeling to estimate the optimal number of shared bikes deployed has not been well addressed. To support bike-sharing companies in better deploying shared bikes, in this research, we propose a new deep residual network model to determine the optimal number of shared bikes. The novelty of this model is that residual networks are adopted to create a deep learning model, which is the first to be used in the shared bike deployment domain. Moreover, in the proposed model, three strategies have been considered to balance the profit of the service providers and the welfare of the public. Simulation results show that our model has achieved a coefficient of determination (R2 score) of 0.8998, showing that the model performs satisfactorily in determining the optimal number of shared bikes when compared to several typical prediction approaches, such as (a) gradient boosters, (b) support vector machines, (c) boosting trees, and (d) extreme gradient boosting trees.