Metro bears a large number of passenger flows in urban transportation systems. Short-term metro origin-destination (OD) passenger flow prediction is an essential component of intelligent transportation systems (ITS), which allows operators to better monitor the metro system and improve the level of service for passengers. In this paper, we exploit a novel data structure, hypergraph, to represent the complex correlation between OD pairs, and propose an elaborately designed Spatio-Temporal Dynamic Attentive Multi-HyperGraph Network (ST-DAMHGN) to tackle the short-term OD passenger flow prediction problem. In the proposed framework, we construct multiple hypergraphs to model the relationship between OD pairs and adopt the perceptual field to realize efficient and effective vertex feature extraction. Then, we utilize the attention mechanism to adaptively and dynamically synthesize information from multiple hypergraphs and make a trade-off between exploration and exploitation. A case study is conducted on the metro system of Hangzhou, China. The results of extensive experiments show that ST-DAMHGN outperforms baseline models. The efficiency is validated for the multi-hypergraph model, perceptual field, spatial feature extraction, and attention mechanism. The hypergraph structure used in our model is verified suitable for modeling traffic data without physical road networks to map directly, e.g., OD passenger flow. ST-DAMHGN can be widely implemented by defining proper correlations for model relationships.

Accurately predicting human mobility is crucial for various applications, e.g., transportation services, epidemic control, and advertisement recommendation. Although numerous sequential modeling based methods (e.g., recurrent neural networks) have been proposed for human mobility prediction, accurately modeling individuals’ high-order travel preferences and the influence of social neighbors on their travel decisions remains challenging. In this paper, we construct a novel multi-context aware model for next location prediction, which aggregates multi-dimensional contextual features, including individual preferences, social relations, and activity-location associations. First, we define activity prediction as an auxiliary task and propose an activity-location association pruning method to mitigate the impact of data sparsity on model prediction. Second, we present a novel motif-preserving individual travel preference learning method that leverages a motif-induced hypergraph convolutional network to capture high-order travel preference features explicitly. Third, we identify virtual social neighbors with similar preferences based on individual travel preference learning results, and design a new social gated fusion structure to model the influence of social neighbors on individual travel choices. Finally, experimental results on two real-world travel datasets demonstrate the superiority of the proposed model over baseline models. Our proposed universal method can be seamlessly integrated with other sequential prediction models to improve the accuracy and stability of human mobility prediction.

Individual next location prediction plays a crucial role in location-based applications, such as route navigation and service recommendation. Although the existing research based on deep learning effectively captures users' spatiotemporal travel preferences, there are challenges in the interpretability of location prediction, heavily relying on large-scale historical travel data for model training. Drawing inspiration from the powerful reasoning capabilities of large language models (LLMs), this study proposes a novel multiround continuous dialogue mechanism and candidate set enhancement method, leveraging LLMs for next location prediction through step-by-step reasoning. In the first round of dialogue, we introduce activity prediction as an auxiliary task to narrow down the candidate locations. Subsequently, we establish an activity-aware prompt to enable LLM to achieve accurate location prediction and provide corresponding reasoning. Finally, we incorporate a third round of dialogue to prompt LLM to make necessary corrections by integrating the prediction results of deep learning models. To address the issues of LLMs being affected by element ranking within the candidate set, we propose a new candidate set enhancement method based on the entropy-weighted Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Our model can understand user travel preferences by fusing location, activity, and time information through natural language. Extensive experiments are conducted on two public datasets of user check-ins, and the results show that our model achieves prediction performance comparable to deep learning models in full-sample prediction and outperforms them in the few-shot settings. Our model provides logical and explainable reasoning, offering insightful guidance for downstream application tasks.