Predicting human mobility remains a fundamental challenge, especially when individuals deviate from routine patterns due to exploration, disruptions, or rare events. While sequential models like Transformers excel at capturing regular movement patterns, their performance often degrades under nonroutine scenarios involving rare or unfamiliar transitions. To address this, we propose ROAM (Routine-Oriented Adaptive Mobility Predictor), a novel framework that jointly models human mobility from both sequential and relational perspectives, enabling adaptive handling of both routine and nonroutine behaviors during prediction. ROAM combines a sequential encoder that captures historically frequent transitions with a complementary graph-based relational reasoning module that encodes both user-specific and group-level mobility structures. To dynamically integrate these views, we introduce a hierarchical confidence-aware gating mechanism that adaptively balances sequential and relational predictions based on their internal reliability. Extensive experiments on real-world mobility datasets show that ROAM consistently outperforms state-of-the-art baselines in next location prediction. Further analysis reveals that the combination of sequential and relational reasoning substantially improves robustness, particularly under out-of-routine scenarios.

Next location prediction is a critical task in human mobility modeling, with broad applications in personalized recommendation, urban planning, and location-based services. Recently, researchers have used prompt-based large language models (LLMs) to improve next location prediction with pre-trained knowledge. However, they face inherent challenges in bridging the gap between textual prompts for semantic contextual understanding and human mobility data for transition pattern modeling. In this paper, we introduce SILO, a framework designed for Semantic Integration in LOcation prediction via LLMs. We first construct a hybrid semantic space that seamlessly integrates ID-based embeddings, text-derived semantics, and auxiliary contextual information, enabling comprehensive modeling of sequential mobility patterns alongside contextual nuances. We then propose user-centric prompts that specify the prediction task for LLMs while embedding user context within a special token. Further, we utilize LLMs as the prediction backbone to process both user-specific prompts and hybrid ID-context embeddings of location sequences. To enhance predictive performance, we finally introduce a dual-logits strategy, combining sequential transition logits with user profile-guided semantic preference logits. Extensive experiments on two large-scale real-world mobility datasets demonstrate that SILO significantly outperforms state-of-the-art baselines, validating its effectiveness in modeling complex mobility patterns through semantic integration using LLMs.

Next location prediction is a crucial task in human mobility modeling, and is pivotal for many downstream applications like location-based recommendation and transportation planning. Although there has been a large body of research tackling this problem, the usefulness of user preference and temporal regularity remains underrepresented. Specifically, previous studies usually neglect the explicit user preference information entailed from human trajectories and fall short in utilizing the arrival time of next location, as a key determinant on next location. To address these limitations, we propose a Multi-Context aware Location Prediction model (MCLP) to predict next locations for individuals, where it explicitly models user preference and the next arrival time as context. First, we utilize a topic model to extract user preferences for different types of locations from historical human trajectories. Second, we develop an arrival time estimator to construct a robust arrival time embedding based on the multi-head attention mechanism. The two components provide pivotal contextual information for the subsequent prediction. Finally, we utilize the Transformer architecture to mine sequential patterns and integrate multiple contextual information to predict the next locations. Experimental results on two real-world mobility datasets show that our proposed MCLP outperforms baseline methods.

A crucial area of research in Human-AI Interaction focuses on understanding how the integration of AI into social systems influences human behavior, for example, how news-feeding algorithms affect people’s voting decisions. But little attention has been paid to how human behavior shapes AI performance. We fill this research gap by introducingroutinenessto measure human behavior for the AI system, which assesses the degree of routine in a person’s activity based on their past activities. We apply the proposedroutinenessmetric to two extensive human behavior datasets: the human mobility dataset with over 700 million data samples and the social media dataset with over 3.8 million data samples. Our analysis revealsroutinenesscan effectively detect behavioral changes in human activities. The performance of AI algorithms is profoundly determined by humanroutineness, which provides valuable guidance for the selection of AI algorithms.