Investigating the impact of spatial-temporal grid size on the microscopic forecasting of the inflow and outflow gap in a free-floating bike-sharing system

Free-floating bike-sharing systems have rapidly gained popularity as a viable short-distance transportation mode. As users play a significant role in the movement of the bikes in such a system, the basis for evaluating bike usage is to predict the gap between the locking and unlocking behaviour in a specified grid, which is defined as the inflow and outflow gap. The Spatial-Temporal grid size, including its time and space dimensions, has a significant impact on the microscopic forecasting of the inflow and outflow gap. In this study, a flexible framework for testing the impact of the grid on inflow and outflow gap predictions is proposed. The performance of four algorithms, i.e., linear regression, support vector regression, random forest, and gradient boost machine, was compared for different grid sizes based on the dataset provided by the Shanghai Big Data Joint Innovation Laboratory. The results show that the proposed framework can be used to evaluate the impact of grid size on microscopic forecasts. A smaller grid helps to achieve better model prediction results, but it also involves longer calculation time. Comparisons of the four algorithms show that the larger the spatial dimensions of the grid, the worse the predicted results. Excluding random forest, the other algorithms tend to achieve better results when the temporal dimension of the grid is larger. The gradient boost machine algorithm provides the best results in most scenarios; the optimal spatial-temporal grid side length is 200 m, 24 h with 5 by 5 grid combination. Dispatchers or analysts in bike-sharing companies can refer to the proposed framework to select the bike delivery scale and management scope. In addition, this study will help researchers and practitioners quickly select the appropriate grid size and machine learning algorithms for bike-sharing analysis.