Bike-sharing system is a new transportation that has emerged in recent years. More and more people will choose to ride bicycle sharing at home and abroad. While we use shared bicycles conveniently, there are also unfavorable factors that affect the customer's riding experience in the bicycle-sharing system. Due to the rents or returns of bikes at different stations in different periods are imbalanced, the bikes in the system need to be rebalanced frequently. Therefore, there is an urgent need to predict and reallocate the bikes in advance. In this paper, we propose a hierarchical forecasting model that predicts the number of rents or returns to each station cluster in a future period to achieve redistribution. First, we propose a two-level affinity propagation clustering algorithm to divide bike stations into groups where migration trends of bikes among stations as well as geographical locations information are considered. Based on the two-level hierarchy of stations, the total rents of bikes are predicted. Then, we use a multi-similarity-based inference model to forecast the migration proportion of inter-cluster and across cluster, based on which the rents or returns of bikes at each station can be deduced. In order to verify the effectiveness of our two-level hierarchical prediction model, we validate it on the bike-sharing system of New York City and compare the results with those of other popular methods obtained. Experimental results demonstrate the superiority over other methods.

Traffic forecasting is the foundation of intelligent transportation systems (ITS). In recent, graph neural networks (GNNs) have successfully captured spatial-temporal dependencies to forecast traffic conditions by transforming traffic data in the graph domain. Nevertheless, the existing methods focus only on learning informative graph representations and fail to model informative graph structures, which hinders the capture of dynamic spatial-temporal dependencies caused by dynamic factors such as weather, accidents, and special events. In this paper, we propose a novel task-oriented Spatial Graph Structure Learning (SGSL) method, which aims to capture dynamic dependencies by jointly learning graph structures and graph representations. Compared to methods that use spectral graph representations, we exploit a learnable spatial graph to effectively model dynamic dependencies in traffic data. Moreover, we directly define graph convolutions on spatial relations to specify different edge weights when aggregating the information of spatial neighbours. Thus, the graph structure alterations, i.e., the relation changes, and the time-varying weights of relations can be encapsulated, thereby effectively representing dynamic dependencies. The gradient descent strategy is introduced to periodically learn a spatial graph through joint optimization with a newly designed deep graph learning model named GAT-nLSTM. In this manner, the intrinsic behaviours of nodes are learned to capture correlations across periods. Notably, the optimization process is performed under the traffic forecasting constraint to ensure that the learned spatial graph is specific to this task. Compared with those of state-of-the-art baselines, the experimental results obtained on real-world traffic datasets show significant improvement, which verifies the superiority of the proposed SGSL.

Human mobility flow prediction forecasts the number of passengers coming into (inflows) or leaving from (outflows) every region of a city. It is crucial for many applications such as mobile marketing or route optimization. People have proposed deep learning methods to predict human mobility flows. However, existing methods neglect the hierarchical nature of human mobility behaviors. Each of us is unique, and we live beyond our neighborhoods. In the process of cross-regional activities, humans migrate in the hierarchical structure of buildings, neighborhoods, regions, cities, and countries. In this article, we propose a predictive framework, hierarchical fuzzy C-means (Hierarchical-FCM)-residual networks (ResNets), to capture the hierarchical structure of human mobility for prediction. First, we offer a Hierarchical-FCM clustering algorithm that is trained to learn the relationship between proximity and road network hierarchically on large human mobility data. Second, we design a fusion model to incorporate the knowledge learned from hierarchical clusters into deep ResNets to improve prediction accuracy. We compare our framework with 25 existing state-of-the-art models, ranging from traditional time-series and machine learning predictors, such as ARIMA and RNN, to the latest deep learning methods designed for human mobility prediction, such as ST-ResNets and DeepST. Our framework outperforms all existing models, by a margin of 2%–68% in terms of prediction accuracy, showing its effectiveness. We are among the first to incorporate the hierarchical structure of human mobility into location clustering for human mobility flow prediction. We empirically show that incorporating such knowledge significantly improves prediction performance.

Human mobility prediction is crucial for epidemic control, urban planning, and traffic forecasting systems. We observe urban traffic flow prediction has a hierarchical structure, in which human mobility prediction should consider not only the spatial and the temporal relationships, but also the high-level mobility trend between individuals and regions. In this paper, we propose a human mobility clustering algorithm based on trend iteration of spectral clustering (TISC) to incorporate the high-level human mobility trend between individuals and regions. We integrate our TISC clustering algorithm with two existing urban traffic flow predictive models: namely, deep spatio-temporal residual network (ST-ResNet) and deep spatio-temporal 3D network (ST-3DNet). By adapting our TISC clustering algorithm, the prediction accuracy of both algorithms has been improved significantly (30.96 $\%$ for ST-ResNet and 24.66 $\%$ for ST-3DNet). We also compare the TISC-based predictive framework with 26 state-of-the-art human mobility prediction algorithms. We observe that our TISC algorithm considerably outperforms all 26 methods, reducing the predictive error from 6.93% to 69.55 $\%$ .