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Docked bike systems have been widely deployed in many cities around the world. To the service provider, predicting the demand and supply of bikes at any station is crucial to offering the best service quality. The docked bike prediction problem is highly challenging because of the complicated joint spatial-temporal (ST) dependency as bikes are picked up and dropped off, the so-called “flows”, between stations. Prior works often considered the spatial and temporal dependencies separately using sequential network models, and based on locality assumptions. Without sufficiently capturing the joint spatial and temporal features, these approaches are not optimal for attaining the best prediction accuracy. We propose STGNN-DJD, a novel data-driven SpatialTemporal Graph Neural Network to solve the bike demand and supply prediction problem by unifiedly embedding the Dynamic and Joint ST Dependency in two novel ST graphs. Given station locations and historical rental data on bike flow over the past time slots 0 to t − 1, we seek to predict online the bike demand and supply at any station at time t. To extract joint spatial-temporal dependency, STGNN-DJD employs a graph generator to construct, at the beginning of time t, two graphs that embed the flow relationships between stations at various time slots (flow-convoluted graph) and dynamic demand-supply pattern correlation between stations (pattern correlation graph), respectively. Given the two spatial-temporal graphs, STGNN-DJD subsequently employs a graph neural network with novel flowbased and attention-based aggregators to generate embedding of each station for docked bike prediction. We have conducted extensive experiments on two large bike-sharing datasets. Our results confirm the effectiveness of STGNN-DJD as compared with other state-of-the-art approaches, with significant improvement on RMSE and MAE (by 20%−50%). We also provide a case study on dynamic dependencies between stations and demonstrate that the locality as

With the rapid advances and the penetration of the Internet of Things and sensors, a massive amount of trajectory data, given by discrete locations at certain timestamps, have been extracted or collected. Knowing the similarity between trajectories is fundamental to understanding their spatial-temporal correlation, with direct and far-reaching applications in contact tracing, companion detection, personalized marketing, etc. In this work, we consider the general and realistic sensing scenario that the locations of the trajectories may be noisy, and that these trajectories are sporadically sampled with randomness and asynchrony from the underlying continuous paths. Most of the prior work on trajectory similarity has not sufficiently considered the temporal dimension, or the issues of location noise and sporadic sampling, while others have limitations of strong assumptions such as a fixed known speed of users or the availability of a large amount of training data.We propose a novel and effective spatial-temporal measure termed STS (Spatial-Temporal Similarity) to evaluate the spatial-temporal overlap between any two trajectories. In order to account for the location noise and sporadic sampling, STS models each location in a trajectory as an observable outcome drawn from a probability distribution. With that, it efficiently reduces the need for training data by estimating a personalized spatial-temporal probability distribution of the object position from its own trajectory. Based on that, it subsequently computes the co-location probability and hence derives the similarity of any two trajectories. We have conducted extensive experiments to evaluate STS using real large-scale indoor (mall) and outdoor (taxi) datasets. Our results show that STS is substantially more accurate and robust than the state-of-the-art approaches, with an improvement of 63% on precision and 85% on mean rank.