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Article
Hyper-relational knowledge graph neural network for next POI recommendation
With the advancement of mobile technology, Point of Interest (POI) recommendation systems in Location-based Social Networks (LBSN) have brought numerous benefits to both users and companies. Many existing work...
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Article
A geometry-driven neural topic model for trip purpose inference
Understanding urban human mobility, particularly trip purposes, is essential for optimizing traffic management, personalized recommendations, and urban planning. However, in real-world scenarios, trip purposes...
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Chapter and Conference Paper
Returning Home Strategy Analysis Using Mobile Sensing Data in Tohoku Earthquake
In recent decades, there has been a significant increase in the frequency and intensity of natural disasters. Such catastrophic events often result in large-scale population movements and evacuations. Analyzin...
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Chapter and Conference Paper
How to Be a Well-Prepared Organizer: Studying the Causal Effects of City Events on Human Mobility
The analysis of how city events causally affect human mobility is of critical importance. The city government will be thrilled to know how an impending event will influence mobility beforehand, so that they ca...
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Article
HMGCL: Heterogeneous multigraph contrastive learning for LBSN friend recommendation
Friend recommendation from user trajectory is a vital real-world application of location-based social networks (LBSN) services. Previous statistical analysis indicated that social network relationships could e...
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Chapter and Conference Paper
Route to Time and Time to Route: Travel Time Estimation from Sparse Trajectories
Due to the rapid development of Internet of Things (IoT) technologies, many online web apps (e.g., Google Map and Uber) estimate the travel time of trajectory data collected by mobile devices. However, in real...
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Chapter and Conference Paper
MepoGNN: Metapopulation Epidemic Forecasting with Graph Neural Networks
Epidemic prediction is a fundamental task for epidemic control and prevention. Many mechanistic models and deep learning models are built for this task. However, most mechanistic models have difficulty estimat...
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Chapter and Conference Paper
Countrywide Origin-Destination Matrix Prediction and Its Application for COVID-19
Modeling and predicting human mobility are of great significance to various application scenarios such as intelligent transportation system, crowd management, and disaster response. In particular, in a severe ...
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Article
Trajectory fingerprint: one-shot human trajectory identification using Siamese network
Extracting identifiable information from human trajectories is a fundamental task in many location-based services (LBS), such as personalized POI recommendation system, irregular human movement detection and p...
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Chapter
Big Data-Driven Citywide Human Mobility Modeling for Emergency Management
Human mobility modeling for emergency management plays an critical role in guaranteeing people safety and saving people’s life. However, many traditional methods for regular human mobility modeling fail on eme...
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Chapter and Conference Paper
Monocular Pedestrian Tracking from a Moving Vehicle
Tracking of pedestrians from a moving vehicle equipped with a monocular camera is still considered as a challenging problem in the fields of both computer vision and robotics. In this paper, we address this pr...
