Analyzing and comprehending check-in sequences is crucial for various applications in smart cities. However, publicly available check-in datasets are often limited in scale due to privacy concerns. This poses a significant obstacle to academic research and downstream applications. Thus, it is urgent to generate realistic check-in datasets. The denoising diffusion probabilistic model (DDPM) as one of the most capable generation methods is a good choice to achieve this goal. However, generating check-in sequences using DDPM is not an easy feat. The difficulties lie in handling check-in sequences of variable lengths and capturing the correlation from check-in sequences' distinct characteristics. This paper addresses the challenges by proposing a Spatio-Temporal Contrastive Diffusion Model (STCDM). This model introduces a novel spatio-temporal lossless encoding method that effectively encodes check-in sequences into a suitable format with equal length. Furthermore, we capture the spatio-temporal correlations with two disentangled diffusion modules to reduce the impact of the difference between spatial and temporal characteristics. Finally, we incorporate contrastive learning to enhance the relationship between diffusion modules. We generate four realistic datasets in different scenarios using STCDM and design four metrics for comparison. Experiments demonstrate that our generated datasets are more realistic and free of privacy leakage.

Location-based services (LBS) have accumulated extensive human mobility data on diverse behaviors through check-in sequences. These sequences offer valuable insights into users’ intentions and preferences. Yet, existing models analyzing check-in sequences fail to consider the semantics contained in these sequences, which closely reflect human visiting intentions and travel preferences, leading to an incomplete comprehension. Drawing inspiration from the exceptional semantic understanding and contextual information processing capabilities of large language models (LLMs) across various domains, we present Mobility-LLM, a novel framework that leverages LLMs to analyze check-in sequences for multiple tasks. Since LLMs cannot directly interpret check-ins, we reprogram these sequences to help LLMs comprehensively understand the semantics of human visiting intentions and travel preferences. Specifically, we introduce a visiting intention memory network (VIMN) to capture the visiting intentions at each record, along with a shared pool of human travel preference prompts (HTPP) to guide the LLM in understanding users’ travel preferences. These components enhance the model’s ability to extract and leverage semantic information from human mobility data effectively. Extensive experiments on four benchmark datasets and three downstream tasks demonstrate that our approach significantly outperforms existing models, underscoring the effectiveness of Mobility-LLM in advancing our understanding of human mobility data within LBS contexts.

The rapid growth of location-based services (LBS) has yielded massive amounts of data on human mobility. Effectively extracting meaningful representations for user-generated check-in sequences is pivotal for facilitating various downstream services. However, the user-generated check-in data are simultaneously influenced by the surrounding objective circumstances and the user's subjective intention. Specifically, the temporal uncertainty and spatial diversity exhibited in check-in data make it difficult to capture the macroscopic spatial-temporal patterns of users and to understand the semantics of user mobility activities. Furthermore, the distinct characteristics of the temporal and spatial information in check-in sequences call for an effective fusion method to incorporate these two types of information. In this paper, we propose a novel Spatial-Temporal Cross-view Contrastive Representation (STCCR) framework for check-in sequence representation learning. Specifically, STCCR addresses the above challenges by employing self-supervision from “spatial topic” and “temporal intention” views, facilitating effective fusion of spatial and temporal information at the semantic level. Besides, STCCR leverages contrastive clustering to uncover users’ shared spatial topics from diverse mobility activities, while employing angular momentum contrast to mitigate the impact of temporal uncertainty and noise. We extensively evaluate STCCR on three real-world datasets and demonstrate its superior performance across three downstream tasks.

A core step of mining human mobility data is to learn accurate representations for user-generated check-in sequences. The learned representations should be able to fully describe the spatial-temporal mobility patterns of users and the high-level semantics of traveling. However, existing check-in sequence representation learning is usually implicitly achieved by end-to-end models designed for specific downstream tasks, resulting in unsatisfactory generalizable abilities and poor performance. Besides, although the sequence representation learning models that follow the contrastive learning pre-training paradigm have achieved breakthroughs in many fields like NLP, they fail to simultaneously consider the unique spatial-temporal characteristics of check-in sequences and need manual adjustments on the data augmentation strategies. So, directly applying them to check-in sequences cannot yield a meaningful pretext task. To this end, in this paper we propose a contrastive pre-training model with adversarial perturbations for check-in sequence representation learning (CACSR). Firstly, we design a novel spatial-temporal augmentation block for disturbing the spatial-temporal features of check-in sequences in the latent space to relieve the stress of designing manual data augmentation strategies. Secondly, to construct an effective contrastive pretext task, we generate “hard” positive and negative pairs for the check-in sequence by adversarial training. These two designs encourage the model to capture the high-level spatial-temporal patterns and semantics of check-in sequences while ignoring the noisy and unimportant details. We demonstrate the effectiveness and versatility of CACSR on two kinds of downstream tasks using three real-world datasets. The results show that our model outperforms both the state-of-the-art pre-training methods and the end-to-end models.

In intelligent transportation systems, accurate long-term traffic forecasting is informative for administrators and travelers to make wise decisions in advance. Recently proposed spatial-temporal forecasting models perform well for short-term traffic forecasting, but two challenges hinder their applications for long-term forecasting in practice. Firstly, existing traffic forecasting models do not have satisfactory scalability on effectiveness and efficiency, i.e., as the prediction time spans extend, existing models either cannot capture the long-term spatial-temporal dynamics of traffic data or equip global receptive fields at the cost of quadratic computational complexity. Secondly, the dilemma between the models’ strong appetite for high-quality training data and their generalization ability is also a challenge we have to face. Thus how to improve data utilization efficiency deserves thoughtful thinking. Aiming at solving the long-term traffic forecasting problem and facilitating the deployment of traffic forecasting models in practice, this paper proposes an efficient and effective Self-supervised Spatial-Temporal Bottleneck Attentive Network (SSTBAN). Specifically, SSTBAN follows a multi-task framework by incorporating a self-supervised learner to produce robust latent representations for historical traffic data, so as to improve its generalization performance and robustness for forecasting. Besides, we design a spatial-temporal bottleneck attention mechanism, reducing the computational complexity meanwhile encoding global spatial-temporal dynamics. Extensive experiments on real-world long-term traffic forecasting tasks, including traffic speed forecasting and traffic flow forecasting under nine scenarios, demonstrate that SSTBAN not only achieves the overall best performance but also has good computation efficiency and data utilization efficiency.