Fine-grained prediction of urban population is of great practical significance in many domains that require temporally and spatially detailed population information. However, fine-grained population modeling has been challenging because the urban population is highly dynamic and its mobility pattern is complex in space and time. In this study, we propose a method to predict the population at a large spatiotemporal scale in a city. This method models the temporal dependency of population by estimating the future inflow population with the current inflow pattern and models the spatial correlation of population using an artificial neural network. With a large dataset of mobile phone locations, the model’s prediction error is low and only increases gradually as the temporal prediction granularity increases, and this model is adaptive to sudden changes in population caused by special events.

Dynamic human activity intensity information is of great importance in many location-based applications. However, two limitations remain in the prediction of human activity intensity. First, it is hard to learn the spatial interaction patterns across scales for predicting human activities. Second, social interaction can help model the activity intensity variation but is rarely considered in the existing literature. To mitigate these limitations, we proposed a novel dynamic activity intensity prediction method with deep learning on graphs using the interactions in both physical and social spaces. In this method, the physical interactions and social interactions between spatial units were integrated into a fused graph convolutional network to model multi-type spatial interaction patterns. The future activity intensity variation was predicted by combining the spatial interaction pattern and the temporal pattern of activity intensity series. The method was verified with a country-scale anonymized mobile phone dataset. The results demonstrated that our proposed deep learning method with combining graph convolutional networks and recurrent neural networks outperformed other baseline approaches. This method enables dynamic human activity intensity prediction from a more spatially and socially integrated perspective, which helps improve the performance of modeling human dynamics.

Trajectory prediction plays an important role in supporting many advanced applications such as location-based services and advanced intelligent traffic managements. Most existing trajectory prediction methods employed fixed spatial division and focused on human closeness movement patterns. However, these methods could lead to a sharp boundary limitation and ignore the periodic characteristics of human mobility. This paper proposes a novel trajectory prediction method based on long short-term memory network (LSTM) called the trajectory predictor with fuzzy-long short-term memory network (TrjPre-FLSTM). First, we introduce a new fuzzy trajectory concept and extend the LSTM to a fuzzy-LSTM to overcome the sharp boundary limitation. Second, we explicitly incorporate the periodic movement patterns of moving objects in the trajectory prediction. Using a real-world mobile phone dataset, we evaluate the performance of TrjPre-FLSTM with two latest competitors. The case study results indicate that the proposed method outperforms the comparative methods in terms of the prediction accuracy.