Dynamic spatio-temporal interactive clustering strategy for free-floating bike-sharing

As an important part of green travel mode, operation service of bike-sharing system is increasingly intelligent and refined. Operators can effectively match supply to demand through reasonable delivery and rebalancing methods. One of the most important foundations of these strategies is zone management. Therefore, this paper establishes a new framework of management area division, which includes three parts: data reconstruction, clustering model and model performance. Firstly, we reconstruct the original demand data through Coarse Grain and Visibility Graph methods. The reconstructed data highlights the characteristics of demand fluctuation and filters the noise. Secondly, we build a new spatio-temporal interactive clustering model. Through time dimension clustering analysis, we not only divide the clustering window, but also give three types of temporal labels of the demand series. At the same time, we give the clustering results of spatial dimensions. We interact the spatial clustering results of the demand series with the temporal labels under the combination of the Integration and Constraint criterions in each clustering window. Thirdly, we use the information entropy index to measure the stability of the clustering results and compares the distance cost of rebalancing before and after clustering. Empirical results show that after the spatio-temporal interaction clustering, demand fluctuation in each cluster has high consistency and stability in both time and space dimensions. The average information entropy not only decreases by more than 69 % compared with the results before clustering, but also smaller than the average level of merging without spatio-temporal interaction. Moreover, the rebalancing cost after spatio-temporal interactive clustering is only 61.26 % of the actual rebalancing cost. This helps operators give more efficient rebalancing strategies and more stable rebalancing routing than before.