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Related Experiment Video

Updated: Jan 12, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Probabilistic Bayesian learning with long-tail awareness for trajectory-user linking.

Haolun Ding¹, Zhengwen Fu², Rong Zhang²

¹Engineering Research Center of Intelligent Finance, Ministry of Education, Southwestern University of Finance and Economics, Chengdu, China.

Neural Networks : the Official Journal of the International Neural Network Society

|November 7, 2025

Summary

This summary is machine-generated.

This study introduces LongTUL, a new method for linking user trajectories in location-based social networks. It effectively addresses the long tail phenomenon, improving accuracy for less active users.

Keywords:

Check-in data Human mobility Long tail Probabilistic learning Trajectory-user linking

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Last Updated: Jan 12, 2026

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Area of Science:

GeoAI
Data Science
Machine Learning

Background:

Location-based social networks (LBSN) generate vast user check-in data, enabling mobility pattern analysis.
Trajectory-User Linking (TUL) aims to associate unlabeled trajectories with their creators, a key task in GeoAI.
The 'long tail phenomenon' in user check-ins, where some users have many check-ins and others very few, poses a significant challenge for TUL accuracy.

Purpose of the Study:

To propose a novel probabilistic Bayesian learning solution, LongTUL, to address the long tail issue in Trajectory-User Linking (TUL).
To improve the accurate association of unlabeled check-in trajectories with their corresponding users, particularly for less active (tail) users.

Main Methods:

Developed a Check-in Engagement Compromise (CEC) mechanism to balance user participation levels before training.
Implemented a Probabilistic Trajectory Learning (PTL) procedure using variational Bayes to encode trajectories into a latent space.
Applied Laplacian approximation to latent representations to mitigate amortization errors and long-tail effects.
Designed a reweighted classifier for equitable inference between frequent (head) and infrequent (tail) users.

Main Results:

The proposed LongTUL method demonstrated superior performance compared to existing TUL solutions.
LongTUL effectively addressed the long tail issue, significantly improving the classification accuracy for tail users.
Experiments on three real-world datasets validated the effectiveness of the LongTUL approach.

Conclusions:

LongTUL offers a robust solution for Trajectory-User Linking, particularly in datasets exhibiting the long tail phenomenon.
The method enhances the understanding of mobility patterns by accurately linking trajectories from all user groups.
This work contributes to advancing GeoAI by providing a more equitable and accurate approach to user trajectory analysis.