TL;DR
Experiments show that MoRAL consistently outperforms statistical and LLM-based baselines under the GEO-BLEU metric, improving the mean score over the strongest baseline by 5.82% and achieving a 2.6 times reduction in per-user inference time relative to recent LLM baselines using the same backbone.
Accurate next-location prediction remains a challenging problem in spatio-temporal mobility modeling. Many existing approaches rely primarily on raw trajectory sequences and overlook the semantic and contextual factors that govern human movement behavior. To address this limitation, this study proposes a novel Spatio-T...
LE4Mob is proposed, an inductive, distance-aware, and geography-derived location embedding framework for mobility modelling that extends contrastive language-location pre-training while introducing a distance-aware regularisation objective that encourages the embedding space to preserve spatial relationships.
Directional analysis shows that LLMs often rely on coarse, stable predictor-level priors that remain similar across outcomes and cities, including asymmetric treatment of protected-group predictors, and can partially recover aggregate mobility patterns from urban context.
SeMoFlow is proposed, a Semantic human Mobility generation framework based on latent Flow matching that achieves higher trajectory fidelity than existing baselines, preserves city-specific mobility motifs, and supports both joint multi-city generation and effective cross-city transfer.
Human mobility predictability concerns the best prediction performance attainable from a given target and input information, but its ground truth is not directly observable on real mobility data. We present BER-PEF, a Bayes-error-rate-based framework that converts BER estimation into mobility predictability estimation...
The model incorporates three core components: spatiotemporal embedding module, spatiotemporal fusion module, and LLM backbone, which adopts a differentiated parameter adaptation strategy to balance training efficiency and traffic data adaptability.
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