Cross-city-aware Spatiotemporal BERT

Abstract

Predicting human mobility has been actively studied for the past decade because of its various possible applications, such as traffic optimization and urban planning. Despite the increasing interest in human mobility prediction, the training and evaluation of prediction methods are often constrained by the use of different datasets (i.e., each study uses their own dataset for an evaluation). In considering these, the Human Mobility Prediction Challenge (HuMob Challenge) 2024 was held aiming at evaluating state-of-the-art models for the prediction of human mobility patterns using large-scale open dataset.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 6 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.