Context-aware Deep Model for Joint Mobility and Time Prediction

Cheng Long · Gao Cong · Chenliang Li

78 citations0/0 files storedACM International Conference on Web Search and Data Mining (WSDM)

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Human Mobility and Location-Based Analysis Data Management and Algorithms Traffic Prediction and Management Techniques

Abstract

Mobility prediction, which is to predict where a user will arrive based on the user's historical mobility records, has attracted much attention. We argue that it is more useful to know not only where but also when a user will arrive next in many scenarios such as targeted advertising and taxi service. In this paper, we propose a novel context-aware deep model called DeepJMT for jointly performing mobility prediction (to know where) and time prediction (to know when). The DeepJMT model consists of (1) a hierarchical recurrent neural network (RNN) based sequential dependency encoder, which is more capable of capturing a user's mobility regularities and temporal patterns compared to vanilla RNN based models; (2) a spatial context extractor and a periodicity context extractor to extract location semantics and the user's periodicity, respectively; and (3) a co-attention based social & temporal context extractor which could extract the mobility and temporal evidence from social relationships. Experiments conducted on three real-world datasets show that DeepJMT outperforms the state-of-the-art mobility prediction and time prediction methods.

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References

1970A Computer Movie Simulating Urban Growth in the Detroit Region 2011Friendship and mobility 2016Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts 2018DeepMove 2013Exploring temporal effects for location recommendation on location-based social networks 2012Predicting future locations with hidden Markov models 2012Finding your friends and following them to where you are 2019Revisiting User Mobility and Social Relationships in LBSNs: A Hypergraph Embedding Approach

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2021Self-supervised human mobility learning for next location prediction and trajectory classification 2021MobTCast: Leveraging Auxiliary Trajectory Forecasting for Human Mobility Prediction 2021A Survey on Deep Learning for Human Mobility 2024City Foundation Models for Learning General Purpose Representations from OpenStreetMap 2024Human mobility prediction with causal and spatial-constrained multi-task network 2023

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