DeepMove: Predicting Human Mobility with Attentional Recurrent Networks

By Jie Feng, Yong Li, Funing Sun, Fanchao Meng, Ang Guo, Depeng Jin. The Web Conference, 2018

801 citations recorded by the source corpus.

Human mobility prediction is of great importance for a wide spectrum of location-based applications. However, predicting mobility is not trivial because of three challenges: 1) the complex sequential transition regularities exhibited with time-dependent and high-order nature; 2) the multi-level periodicity of human mobility; and 3) the heterogeneity and sparsity of the collected trajectory data. In this paper, we propose DeepMove, an attentional recurrent network for mobility prediction from lengthy and sparse trajectories. In DeepMove, we first design a multi-modal embedding recurrent neural network to capture the complicated sequential transitions by jointly embedding the multiple factors that govern the human mobility. Then, we propose a historical attention model with two mechanisms to capture the multi-level periodicity in a principle way, which effectively utilizes the periodicity nature to augment the recurrent neural network for mobility prediction. We perform experiments on three representative real-life mobility datasets, and extensive evaluation results demonstrate that our model outperforms the state-of-the-art models by more than 10%. Moreover, compared with the state-of-the-art neural network models, DeepMove provides intuitive explanations into the prediction and sheds light on interpretable mobility prediction.

Subfields: Building and Construction, Computer Vision and Pattern Recognition, Transportation

View Paper: https://www.semanticscholar.org/paper/f73e170d611d5abfe5ad2d439cef80532b54463f

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Citations in the corpus

A Survey on Deep Learning for Human Mobility (2020, methodology): “[57] DeepMove 16K 1 Year New York City [57] Next-Loc.”
Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction (2018, background): “, periodicity), which is an important property of spatial-temporal prediction problem (Zonoozi et al. 2018; Feng et al. 2018).”
Deep Learning for Spatio-Temporal Data Mining: A Survey (2019, methodology): “The neural attention mechanism used in STDM can be generally categorized into spatial domain attention [19], [38] and temporal domain attention [37], [50], [74], [186].”
STAN: Spatio-Temporal Attention Network for Next Location Recommendation (2021, background): “DeepMove [4] combines an attention layer for learning long-term periodicity with a recurrent layer for learning short-term sequential regularity and learned from highly correlated trajectories.”
Dynamic and Multi-faceted Spatio-temporal Deep Learning for Traffic Speed Forecasting (2021, background): “The successful applications include but not limited to computer vision methods for traffic data collection [9] and regulations enforcement [2], reinforcement learning for automatic pilot [16, 18] and traffic signal control [6], big data analysis for human mobility modelling [10, 11], etc.”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, methodology): “incorporate attention mechanisms on RNN [312], to capture the complicated sequential”
Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation (2020, methodology): “Following (Feng et al. 2018), we set the dimension of embeddings and the hidden states to 500 for all deep learning-based methods.”
A Survey on Trajectory Data Management, Analytics, and Learning (2020, methodology): “GeoLife [208] 17,621 Human mobility [196] Urban area Check-in Foursquare [20] Gowalla [44] 104,478 Tourism planning [169] Mobility prediction [64] Online sharing OpenStreetMap [10] 8.7 million Commuting analytics COVID-19 KCDC [6], JHU [9] <100 Disease control Vehicle Taxi tri”