Trajectory computing is a pivotal domain encompassing trajectory data management and mining, garnering widespread attention due to its crucial role in various practical applications such as location services, urban traffic, and public safety. Traditional methods, focusing on simplistic spatio-temporal features, face challenges of complex calculations, limited scalability, and inadequate adaptability to real-world complexities. In this paper, we present a comprehensive review of the development and recent advances in deep learning for trajectory computing (DL4Traj). We first define trajectory data and provide a brief overview of widely-used deep learning models. Systematically, we explore deep learning applications in trajectory management (pre-processing, storage, analysis, and visualization) and mining (trajectory-related forecasting, trajectory-related recommendation, trajectory classification, travel time estimation, anomaly detection, and mobility generation). Notably, we encapsulate recent advancements in Large Language Models (LLMs) that hold the potential to augment trajectory computing. Additionally, we summarize application scenarios, public datasets, and toolkits. Finally, we outline current challenges in DL4Traj research and propose future directions. Relevant papers and open-source resources have been collated and are continuously updated at: \href{https://github.com/yoshall/Awesome-Trajectory-Computing}{DL4Traj Repo}.

Deep Learning for Trajectory Data Management and Mining: A Survey and Beyond

Urban computing has emerged as a multidisciplinary field that harnesses data-driven technologies to address challenges and improve urban living. Traditional approaches, while beneficial, often face challenges with generalization, scalability, and contextual understanding. The advent of Large Language Models (LLMs) offers transformative potential in this domain. This survey explores the intersection of LLMs and urban computing, emphasizing the impact of LLMs in processing and analyzing urban data, enhancing decision-making, and fostering citizen engagement. We provide a concise overview of the evolution and core technologies of LLMs. Additionally, we survey their applications across key urban domains, such as transportation, public safety, and environmental monitoring, summarizing essential tasks and prior works in various urban contexts, while highlighting LLMs’ functional roles and implementation patterns. Building on this, we propose potential LLM-based solutions to address unresolved challenges. To facilitate in-depth research, we compile a list of available datasets and tools applicable to diverse urban scenarios. Finally, we discuss the limitations of current approaches and outline future directions for advancing LLMs in urban computing.

pdf/urban-computing-in-the-era-of-large-language-models-rcj7rz80jjny.pdf

Urban Computing in the Era of Large Language Models

This paper provides a comprehensive survey of recent advancements in leveraging machine learning techniques, particularly Transformer models, for predicting human mobility patterns during epidemics. Understanding how people move during epidemics is essential for modeling the spread of diseases and devising effective response strategies. Forecasting population movement is crucial for informing epidemiological models and facilitating effective response planning in public health emergencies. Predicting mobility patterns can enable authorities to better anticipate the geographical and temporal spread of diseases, allocate resources more efficiently, and implement targeted interventions. We review a range of approaches utilizing both pretrained language models like BERT and Large Language Models (LLMs) tailored specifically for mobility prediction tasks. These models have demonstrated significant potential in capturing complex spatio-temporal dependencies and contextual patterns in textual data. 

pdf/a-short-survey-of-human-mobility-prediction-in-epidemic-ett4w74fs5.pdf

A Short Survey of Human Mobility Prediction in Epidemic Modeling from
  Transformers to LLMs

Accurate and timely defect detection in industrial edge networks is becoming increasingly important in intelligent manufacturing. However, the privacy concern and limited resources of edge devices hinder the applications of existing defect detection models. This paper presents a new defect detection approach based on federated learning (FL), where the transmission of privacy-sensitive data can be avoided and user privacy and data security can be well preserved. We first propose a lightweight defect detection model based on YOLOv5s. We use ShuffleNet as the backbone of YOLOv5s, use the SE attention mechanism and LRELU to improve the learning accuracy. We then develop a clustering-based device selection approach to address the degradation of accuracy caused by imbalanced data distributions of devices in FL. Corroborated by simulations, the accuracy of the proposed defect detection model increases by 26.86% as compared with existing models, and the proposed device selection approach is also superior to the benchmarks in terms of learning accuracy. 

Lightweight and Data-imbalance-aware Defect Detection Approach Based on Federated Learning in Industrial Edge Networks

Rapid community response and resilience to typhoon Doksuri in coastal Fujian: insights from large-scale human mobility data

Generative models have shown promising results in capturing human mobility characteristics and generating synthetic trajectories. However, it remains challenging to ensure that the generated geospatial mobility data is semantically realistic, including consistent location sequences, and reflects real-world characteristics, such as constraining on geospatial limits. To address these issues, we reformat human mobility modeling as an autoregressive generation task, leveraging Generative Pre-trained Transformer (GPT). To ensure its controllable generation to alleviate the above challenges, we propose a geospatially-aware generative model, MobilityGPT. We propose a gravity-based sampling method to train a transformer for semantic sequence similarity. Then, we constrained the training process via a road connectivity matrix that provides the connectivity of sequences in trajectory generation, thereby keeping generated trajectories in geospatial limits. Lastly, we constructed a Reinforcement Learning from Trajectory Feedback (RLTF) to minimize the travel distance between training and the synthetically generated trajectories. Our experiments on real-world datasets demonstrate that MobilityGPT outperforms state-of-the-art methods in generating high-quality mobility trajectories that are closest to real data in terms of origin-destination similarity, trip length, travel radius, link, and gravity distributions.

pdf/mobilitygpt-enhanced-human-mobility-modeling-with-a-gpt-50k3bzrash.pdf

MobilityGPT: Enhanced Human Mobility Modeling with a GPT model

Differentially-private (DP) Generative Adversarial Networks (GAN) can be used to protect the privacy of training data and support public downstream learning tasks with synthetic data. However, typical DP mechanisms add noise to the training process and can lead to various convergence problems. We propose HE-GAN, a DP generative framework that eliminates noise addition by using Exponential Mechanism (EM) on the privacy-factor-adjusted posterior predictive distribution of a classifier trained on the private data. EM is more general than many other DP mechanisms including Laplacian and Gaussian mechanisms. EM’s reliance on sampling the output space also prevents the DP noise from corrupting the training process. However, there are two challenges: first, sampling the posterior distribution of the private discriminative classifier may not be able to produce high-quality synthetic samples. Instead, we sample from the latent space of a publicly-trained GAN to optimize the private posterior. Second, we use the highly effective Hamiltonian Monte Carlo (HMC) method for latent space sampling. We perform experiments on MNIST and Fashion-MNIST under public-private splits. Results show that HE-GAN can achieve downstream classification accuracy on par with or better than state-of-the-art scheme over a wide range of privacy budgets. 

He-Gan: Differentially Private Gan Using Hamiltonian Monte Carlo Based Exponential Mechanism

High-dimensional data are widely used in the era of deep learning with numerous applications. However, certain data which has sensitive information are not allowed to be shared without privacy protection. In this paper, we propose a novel differentially private data releasing method called Differentially Private Data Publishing with Gaussian Optimized Model Inversion (DPGOMI) to address this issue. Our approach involves mapping private data to the latent space using a public generator, followed by a lower-dimensional DP-GAN with better convergence properties. We evaluate the performance of DPGOMI on standard datasets CIFAR10 and SVHN. Our results show that DPGOMI outperforms the standard DP-GAN method in terms of Inception Score, Fr\'echet Inception Distance, and classification performance, while providing the same level of privacy. Our proposed approach offers a promising solution for protecting sensitive data in GAN training while maintaining high-quality results.

pdf/dpgomi-differentially-private-data-publishing-with-gaussian-1hozumw3a8.pdf

DPGOMI: Differentially Private Data Publishing with Gaussian Optimized Model Inversion

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MobilityGPT: Enhanced Human Mobility Modeling with a GPT model

He-Gan: Differentially Private Gan Using Hamiltonian Monte Carlo Based Exponential Mechanism

DPGOMI: Differentially Private Data Publishing with Gaussian Optimized Model Inversion