The development of multi-mode transportation systems, e.g., bus, metro, taxi, and bike-sharing, presents a fundamental challenge in forecasting demand across heterogeneous, noisy, and complexly interacting data streams. From a feature modeling perspective, this requires a shift from simple data fusion to a more principled approach. This paper introduces a novel end-to-end framework, Multi-mode Spatiotemporal Adaptive Fusion Network (MSTAFN), that systematically addresses this challenge through a two-stage process: 1) unsupervised shared feature selection, and 2) dynamic asymmetric feature interaction modeling. For the first stage, we design an Infomax module that employs an information-theoretic principle to obtain a clean low-dimensional shared latent representation from cross-mode data. This representation captures the underlying semantic drivers of demand, such as latent commuting patterns, while mitigating noise and redundancy. For the second stage, we propose a Multi-Flashback module to explicitly model the complex asymmetric interactions between heterogeneous features, particularly across different temporal granularities. Its Cross-Flashback mechanism is designed to allow low-frequency modes (e.g., metro) to be informed by the latest fine-grained dynamics of high-frequency modes (e.g., bike-sharing). Experiments on a large-scale real-world dataset from New York City demonstrate that our two-stage paradigm outperforms state-of-the-art baselines, especially on the mode with the coarsest time granularity. This validates the superiority of our proposed modeling framework, supporting the improvement of operational efficiency for multi-mode transportation systems.

While considerable research has been dedicated to the prediction of area trip demand, relatively little attention has been paid to the area trip attraction prediction problem. A primary challenge in short-term trip attraction prediction stems from the partial observability of destination information, as trips are either still en route or have recently arrived but not yet confirmed as completed, leading to delayed and incomplete measurement of attraction dynamics. This paper proposes the field theory-guided area trip attraction prediction fusion network (FG-ATAPFN), which leverages potential energy fields (PEFs) to model underlying physical principles of urban dynamics. Furthermore, it establishes a connection between trip attraction and traffic flow through the continuity equation derived from field theory. FG-ATAPFN consists of three pivotal components: spatial-temporal Transformer to model spatial-temporal dependencies in historical area trip attraction sequences effectively, an OD flow PEF prediction network to derive precise trip destination insights, and an edge flow PEF prediction network to track and predict real-time human mobility flows dynamically, all leveraging both historical and real-time data to enable a comprehensive and precise analysis of travel behaviors within urban transportation systems. The proposed model is validated on an open-sourced real-world dataset comprising human movement trajectories from 100,000 users in Nagoya, Japan, and benchmarked against state-of-the-art prediction models. Experimental results indicate that FG-ATAPFN achieves a 15.5% reduction in mean absolute percentage error, surpassing baseline models in predicting trip attraction. We demonstrate that field theory effectively enhances the accuracy of trip attraction prediction, which is critical for optimizing transportation infrastructure design and guiding urban policy.

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.

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.

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.