Rapidly developing location acquisition technologies provide a powerful tool for understanding and predicting human mobility in cities, which is very significant for urban planning, traffic regulation, and emergency management. However, with the existing methodologies, it is still difficult to accurately predict millions of peoples’ mobility in a large urban area such as Tokyo, Shanghai, and Hong Kong, especially when collected data used for model training are often limited to a small portion of the total population. Obviously, human activities in city are closely linked with point-of-interest (POI) information, which can reflect the semantic meaning of human mobility. This motivates us to fuse human mobility data and city POI data to improve the prediction performance with limited training data, but current fusion technologies can hardly handle these two heterogeneous data. Therefore, we propose a unique POI-embedding mechanism, that aggregates the regional POIs by categories to generate an artificial POI-image for each urban grid and enriches each trajectory snippet to a four-dimensional tensor in an analogous manner to a short video. Then, we design a deep learning architecture combining CNN with LSTM to simultaneously capture both the spatiotemporal and geographical information from the enriched trajectories. Furthermore, transfer learning is employed to transfer mobility knowledge from one city to another, so that we can fully utilize other cities’ data to train a stronger model for the target city with only limited data available. Finally, we achieve satisfactory performance of human mobility prediction at the citywide level using a limited amount of trajectories as training data, which has been validated over five urban areas of different types and scales.

Nowadays, with the rapid development of IoT (Internet of Things) and CPS (Cyber-Physical Systems) technologies, big spatiotemporal data are being generated from mobile phones, car navigation systems, and traffic sensors. By leveraging state-of-the-art deep learning technologies on such data, urban traffic prediction has drawn a lot of attention in AI and Intelligent Transportation System community. The problem can be uniformly modeled with a 3D tensor (T, N, C), where T denotes the total time steps, N denotes the size of the spatial domain (i.e., mesh-grids or graph-nodes), and C denotes the channels of information. According to the specific modeling strategy, the state-of-the-art deep learning models can be divided into three categories: grid-based, graph-based, and multivariate timeseries models. In this study, we first synthetically review the deep traffic models as well as the widely used datasets, then build a standard benchmark to comprehensively evaluate their performances with the same settings and metrics. Our study named DL-Traff is implemented with two most popular deep learning frameworks, i.e., TensorFlow and PyTorch, which is already publicly available as two GitHub repositories https://github.com/deepkashiwa20/DL-Traff-Grid and https://github.com/deepkashiwa20/DL-Traff-Graph. With DL-Traff, we hope to deliver a useful resource to researchers who are interested in spatiotemporal data analysis.

Swarm robots are coordinated via simple control laws to generate emergent behaviors such as flocking, rendezvous, and deployment. Human-swarm teaming has been widely proposed for scenarios, such as human-supervised teams of unmanned aerial vehicles (UAV) for disaster rescue, UAV and ground vehicle cooperation for building security, and soldier-UAV teaming in combat. Effective cooperation requires an appropriate level of trust, between a human and a swarm. When an UAV swarm is deployed in a real-world environment, its performance is subject to real-world factors, such as system reliability and wind disturbances. Degraded performance of a robot can cause undesired swarm behaviors, decreasing human trust. This loss of trust, in turn, can trigger human intervention in UAVs' task executions, decreasing cooperation effectiveness if inappropriate. Therefore, to promote effective cooperation we propose and test a trust-repairing method (Trust-repair) restoring performance and human trust in the swarm to an appropriate level by correcting undesired swarm behaviors. Faulty swarms caused by both external and internal factors were simulated to evaluate the performance of the Trustrepair algorithm in repairing swarm performance and restoring human trust. Results show that Trust-repair is effective in restoring trust to a level intermediate between normal and faulty conditions.

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