User Trajectory Prediction Unifying Global and Local Temporal Information
Wei Hao, Bin Chong, Ronghua Ji, and Chen Hou

TL;DR
This paper introduces a trajectory prediction model that combines global and local temporal features using MLP, MSCNN, and cross-attention, achieving improved accuracy without increasing inference time.
Contribution
It presents a novel multi-scale neural network architecture that effectively captures multi-resolution temporal patterns for user trajectory prediction.
Findings
Reduces MSE by 5.04% compared to ModernTCN.
Reduces MAE by 4.35% compared to ModernTCN.
Maintains similar inference time as existing models.
Abstract
Trajectory prediction is essential for formulating proactive strategies that anticipate user mobility and support advance preparation. Therefore, how to reduce the forecasting error in user trajectory prediction within an acceptable inference time arises as an interesting issue. However, trajectory data contains both global and local temporal information, complicating the extraction of the complete temporal pattern. Moreover, user behavior occurs over different time scales, increasing the difficulty of capturing behavioral patterns. To address these challenges, a trajectory prediction model based on multilayer perceptron (MLP), multi-scale convolutional neural network (MSCNN), and cross-attention (CA) is proposed. Specifically, MLP is used to extract the global temporal information of each feature. In parallel, MSCNN is employed to extract the local temporal information by modeling…
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Taxonomy
TopicsHuman Mobility and Location-Based Analysis · Traffic Prediction and Management Techniques · Data Management and Algorithms
