Generating realistic human mobility data is essential for various application domains, including transportation, urban planning, and epidemic control, as real data is often inaccessible to researchers due to high costs and privacy concerns. Existing deep generative models learn from real trajectories to generate synthetic ones. Despite the progress, most of them suffer from training stability issues and scale poorly with increasing data size. More importantly, they often lack control mechanisms to guide the generated trajectories under constraints such as enforcing specific visits. To address these limitations, we formally define the controlled trajectory generation problem for effectively handling multiple spatiotemporal constraints. We introduce Geo-Llama, a novel LLM finetuning framework that can enforce multiple explicit visit constraints while maintaining contextual coherence of the generated trajectories. In this approach, pre-trained LLMs are fine-tuned on trajectory data with a visit-wise permutation strategy where each visit corresponds to a specific time and location. This strategy enables the model to capture spatiotemporal patterns regardless of visit orders while maintaining flexible and in-context constraint integration through prompts during generation. Extensive experiments on real-world and synthetic datasets validate the effectiveness of Geo-Llama, demonstrating its versatility and robustness in handling a broad range of constraints to generate more realistic trajectories compared to existing methods.

Human mobility data, represented as sequences of visits, are crucial for various application domains, including transportation, urban planning, and public health. However, large-scale human mobility data is typically inaccessible to researchers due to the high cost of data collection and privacy concerns. This limitation has led to several studies proposing learned models to generate synthetic visit sequences. Despite this progress, existing approaches lack mechanisms to control the generation process, which prevents the incorporation of prior knowledge and the spatiotemporal specification of certain visits. To address these limitations, we formally define the Constraint Trajectory Generation problem and introduce Geo-CETRA (Constraint Enforced Trajectory Generation), a novel framework that operates within the continuous spatiotemporal space, enabling direct generation of geographical coordinates and durations of each visit in a trajectory. Geo-CETRA reparameterizes the sampling space for effective enforcement of various spatiotemporal constraints. Furthermore, incorporating a constraint factorization approach along with an innovative beam decoding module, Geo-CETRA facilitates the production of high-quality synthetic trajectories that realistically emulate human movement while satisfying predefined spatiotemporal constraints. Experiments on real and synthetic datasets demonstrate Geo-CETRA's superior precision and contextual accuracy compared to existing approaches.

In various application domains like transportation, urban planning, and public health, analyzing human mobility, represented as a sequence of consecutive visits (aka trajectories), is crucial for uncovering essential mobility patterns. Current practices often discretize space and time to model trajectory data with sequence-analysis techniques like Transformers and LSTM, but this discretization tends to obscure the intrinsic spatial and temporal characteristics inherent in trajectories. Recent work shows the effectiveness of modeling trajectories directly in continuous space and time using the spatiotempo-ral point process (STPP). However, these approaches often assume that all observed trajectories originate from a single underlying dynamic. In reality, real-world trajectories exhibit varying dynamics or moving patterns. We hypothesize that grouping trajectories governed by similar dynamics into clusters before trajectory modeling could enhance modeling effectiveness. Thus, we present a novel approach that simultaneously models trajectories in continuous space and time using STPP while clustering them. Our method leverages a variational Expectation-Maximization (EM) framework to iteratively improve the learning of trajectory dynamics and refine cluster assignments within a single training phase. Extensive tests on synthetic and real-world data demonstrate its effectiveness in clustering and modeling trajectories.

Forecasting the number of visits to Points-of-Interest (POI) in an urban area is critical for planning and decision making in various application domains, from urban planning and transportation management to public health and social studies. Although this forecasting problem can be formulated as a multivariate time-series forecasting task, current approaches cannot fully exploit the ever-changing multi-context correlations among POIs. Therefore, we propose Busyness Graph Neural Network (BysGNN), a temporal graph neural network designed to learn and uncover the underlying multi-context correlations between POIs for accurate visit forecasting. Unlike other approaches where only time-series data is used to learn a dynamic graph, BysGNN utilizes all contextual information and time-series data to learn an accurate dynamic graph representation. By incorporating all contextual, temporal, and spatial signals, we observe a significant improvement in our forecasting accuracy over state-of-the-art forecasting models in our experiments with real-world datasets across the United States.

Accessing realistic human movements (aka trajectories) is essential for many application domains, such as urban planning, transportation, and public health. However, due to privacy and commercial concerns, real-world trajectories are not readily available, giving rise to an important research area of generating synthetic but realistic trajectories. Inspired by the success of deep neural networks (DNN), data-driven methods learn the underlying human decision-making mechanisms and generate synthetic trajectories by directly fitting real-world data. However, these DNN-based approaches do not exploit people's moving behaviors (e.g., work commute, shopping purpose), significantly influencing human decisions during the generation process. This paper proposes MBP-GAIL, a novel framework based on generative adversarial imitation learning that synthesizes realistic trajectories that preserve moving behavior patterns in real data. MBP-GAIL models temporal dependencies by Recurrent Neural Networks (RNN) and combines the stochastic constraints from moving behavior patterns and spatial constraints in the learning process. Through comprehensive experiments, we demonstrate that MBP-GAIL outperforms state-of-the-art methods and can better support decision making in trajectory simulations.

Human mobility data is useful for various applications in urban planning, transportation, and public health, but collecting and sharing real-world trajectories can be challenging due to privacy and data quality issues. To address these problems, recent research focuses on generating synthetic trajectories, mainly using generative adversarial networks (GANs) trained by real-world trajectories. In this paper, we hypothesize that by explicitly capturing the modality of transportation (e.g., walking, biking, driving), we can generate not only more diverse and representative trajectories for different modalities but also more realistic trajectories that preserve the geographical density, trajectory, and transition level properties by capturing both cross-modality and modality-specific patterns. Towards this end, we propose a Clustering-based Sequence Generative Adversarial Network (CSGAN) that simultaneously clusters the trajectories based on their modalities and learns the essential properties of real-world trajectories to generate realistic and representative synthetic trajectories. To measure the effectiveness of generated trajectories, in addition to typical density and trajectory level statistics, we define several new metrics for a comprehensive evaluation, including modality distribution and transition probabilities both globally and within each modality. Our extensive experiments with real-world datasets show the superiority of our model in various metrics over state-of-the-art models.

Human mobility clustering is an important problem for understanding human mobility behaviors (e.g., work and school commutes). Existing methods typically contain two steps: choosing/learning a mobility representation and applying a clustering algorithm to the representation. However, these methods rely on strict visiting orders in trajectories and cannot take advantage of multiple types of mobility representations. This paper proposes a novel mobility clustering method for mobility behavior detection. First, the proposed method contains a permutation-equivalent operation to handle sub-trajectories that might have different visiting orders but similar impacts on mobility behaviors. Second, the proposed method utilizes a variational autoencoder architecture to simultaneously perform clustering in both latent and original spaces. Also, in order to handle the bias of a single latent space, our clustering assignment prediction considers multiple learned latent spaces at different epochs. This way, the proposed method produces accurate results and can provide reliability estimates of each trajectory’s cluster assignment. The experiment shows that the proposed method outperformed state-of-the-art methods in mobility behavior detection from trajectories with better accuracy and more interpretability.

Grammatical Error Correction (GEC) task is always considered as low resource machine translation task which translates a sentence in an ungrammatical language to a grammatical language. As the state-of-the-art approach to GEC task, transformer-based neural machine translation model takes input sentence as a token sequence without sentence's structure information, and may be misled by some strange ungrammatical contexts. In response, to lay more attention on a given token's correct collocation rather than the misleading tokens, we propose dependent self-attention to relatively increase the attention score between correct collocations according to the dependency distance between tokens. However, as the source sentence is ungrammatical in GEC task, the correct collocations can hardly be extracted by normal dependency parser. Therefore, we propose dependency parser for ungrammatical sentence to get the dependency distance between tokens in the ungrammatical sentence. Our method achieves competitive results on both BEA-2019 shared task, CoNLL-2014 shared task and JFLEG test sets.