Personalized location prediction is key to many mobile applications and services. In this paper, motivated by both statistical and visualized preliminary analysis on three real datasets, we observe a strong spatiotemporal correlation for user trajectories among the visited area-of-interests (AoIs) and different time periods on both weekly and daily basis, which directly motivates our time-aware location prediction model design called "t-LocPred". It models the spatial correlations among AoIs by coarse-grained convolutional processing of the user trajectories in AoIs of different time periods ("ConvAoI"); and predicts his/her fine-grained next visited PoI using a novel memory-augmented attentive LSTM model ("mem-attLSTM") to capture long-term behavior patterns. Experimental results show that t-LocPred outperforms 8 baselines. We also show the impact of hyperparameters and the benefits ConvAoI can bring to these baselines.

Spatial crowdsourcing (SC) has been proved quite successful by employing human participants to achieve certain tasks like Uber and Gigwalk. Meanwhile, with the fast devel-opment of unmanned aerial vehicles (e.g., drones), they have become a new source of data collectors equipped with a variety of different sensors. In this paper, we propose a novel SC scenario, enabling human participants to work collaboratively with drones in the presence of multiple charging stations to achieve certain data collection tasks, like videography and surveillance. We propose a novel deep reinforcement learning (D RL) framework called “FD- MAPPO (Cubic Map)”, which consists of a fully de-centralized multi-agent DRL (MADRL) algorithm called “Fully Decentralized Multi-Agent Proximal Policy Optimization (FD-MAPPO)”, and a spatiotemporal memory augmented neural network with novel cubic writing and spatially contextual reading mechanisms called “Cubic Map”. Cubic Map extracts long-term spatiotemporal features, navigates drones to accurately locate the position of the target, i.e., charging stations or sensors. Extensive results on two real datasets of KAIST and NCSU campuses show that FD- MAPPO (Cubic Map) consistently outperforms six other baselines in terms of efficiency.

Personalized location prediction is key to many mobile applications and services. In this paper, motivated by both statistical and visualized preliminary analysis on three real datasets, we observe a strong spatiotemporal correlation for user trajectories among the visited area-of-interests (AoIs) and different time periods on both weekly and daily basis, which directly motivates our time-aware location prediction model design called “$t$t-LocPred”. It models the spatial correlations among AoIs by coarse-grained convolutional processing of the user trajectories in AoIs of different time periods (“ConvAoI”); and predicts his/her fine-grained next visited PoI using a novel memory-augmented attentive LSTM model (“mem-attLSTM”) to capture long-term behavior patterns. Experimental results show that$t$t-LocPred outperforms 8 baselines. We also show the impact of hyperparameters and the benefits ConvAoI can bring to these baselines.

Understanding the movement patterns of humans and vehicles traveling in a city is important for many applications like emergency evacuation and rescue, as well as city planning and management. In this paper, we aim to predict citywide crowd flows within a period in the future to give aid to urban management, through modeling spatiotemporal patterns of recent crowd flows. We present a novel deep model for this task, called "AttConvLSTM", which leverages a convolutional LSTM (ConvLSTM), Convolutional Neural Networks (CNNs) along with an attention mechanism, where ConvLSTM keeps spatial information as intact as possible during sequential analysis, and the attention mechanism can focus important crowd flow variations which cannot be identified by the recurrent module. We conducted extensive experiments for performance evaluation using three large datasets, including Beijing Taxi dataset, Rome Taxi dataset, and Chengdu Didi chauffeuring trace. The experimental results show that AttConvLSTM significantly outperforms several widely-used baselines in terms of Root Mean Squared Error (RMSE), and Mean Average Percentage Error (MAPE), indicating that our approach can deal with crowd flows with different dynamics in both spatial and temporal domains, and make valid predictions several steps ahead.