Graph Network Modeling Techniques for Visualizing Human Mobility Patterns

• URL: http://arxiv.org/abs/2504.03119v1
• Date: Fri, 04 Apr 2025 02:21:44 GMT
• Title: Graph Network Modeling Techniques for Visualizing Human Mobility Patterns
• Authors: Sinjini Mitra, Anuj Srivastava, Avipsa Roy, Pavan Turaga,
• Abstract summary: We develop a methodology by embedding graphs into a continuous space, which alleviates issues related to fast graph matching, graph time-series modeling, and visualization of mobility dynamics.<n>We demonstrate how mobility data collected from trajectories could be transformed into network structures and patterns of mobility flow changes, and can be used for downstream tasks reporting approx 40% decrease in error on average in matched graphs vs unmatched ones.
• Score: 8.537183852577687
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human mobility analysis at urban-scale requires models to represent the complex nature of human movements, which in turn are affected by accessibility to nearby points of interest, underlying socioeconomic factors of a place, and local transport choices for people living in a geographic region. In this work, we represent human mobility and the associated flow of movements as a grapyh. Graph-based approaches for mobility analysis are still in their early stages of adoption and are actively being researched. The challenges of graph-based mobility analysis are multifaceted - the lack of sufficiently high-quality data to represent flows at high spatial and teporal resolution whereas, limited computational resources to translate large voluments of mobility data into a network structure, and scaling issues inherent in graph models etc. The current study develops a methodology by embedding graphs into a continuous space, which alleviates issues related to fast graph matching, graph time-series modeling, and visualization of mobility dynamics. Through experiments, we demonstrate how mobility data collected from taxicab trajectories could be transformed into network structures and patterns of mobility flow changes, and can be used for downstream tasks reporting approx 40% decrease in error on average in matched graphs vs unmatched ones.

Related papers

• Identifying and Characterising Higher Order Interactions in Mobility Networks Using Hypergraphs [1.1060425537315088]
  We propose co-visitation hypergraphs, a model that leverages temporal observation windows to extract group interactions between locations.<n>Using frequent pattern mining, our approach constructs hypergraphs that capture dynamic mobility behaviors across different spatial and temporal scales.<n>Our results demonstrate that our hypergraph-based mobility analysis framework is a valuable tool with potential applications in diverse fields.
  arXiv  Detail & Related papers  (2025-03-24T11:29:06Z)
• Improving Traffic Flow Predictions with SGCN-LSTM: A Hybrid Model for Spatial and Temporal Dependencies [55.2480439325792]
  This paper introduces the Signal-Enhanced Graph Convolutional Network Long Short Term Memory (SGCN-LSTM) model for predicting traffic speeds across road networks. Experiments on the PEMS-BAY road network traffic dataset demonstrate the SGCN-LSTM model's effectiveness.
  arXiv  Detail & Related papers  (2024-11-01T00:37:00Z)
• A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation [59.14165404728197]
  We provide an up-to-date and forward-looking review of deep graph learning under distribution shifts.<n>Specifically, we cover three primary scenarios: graph OOD generalization, training-time graph OOD adaptation, and test-time graph OOD adaptation.<n>To provide a better understanding of the literature, we introduce a systematic taxonomy that classifies existing methods into model-centric and data-centric approaches.
  arXiv  Detail & Related papers  (2024-10-25T02:39:56Z)
• Deep Learning-driven Mobile Traffic Measurement Collection and Analysis [0.43512163406552007]
  In this thesis, we harness the powerful hierarchical feature learning abilities of Deep Learning (DL) techniques in both spatial and temporal domains. We develop solutions for precise city-scale mobile traffic analysis and forecasting.
  arXiv  Detail & Related papers  (2024-10-14T06:53:45Z)
• Human Mobility Modeling with Limited Information via Large Language Models [11.90100976089832]
  We propose an innovative Large Language Model (LLM) empowered human mobility modeling framework. Our proposed approach significantly reduces the reliance on detailed human mobility statistical data. We have validated our results using the NHTS and SCAG-ABM datasets.
  arXiv  Detail & Related papers  (2024-09-26T03:07:32Z)
• Deep Activity Model: A Generative Approach for Human Mobility Pattern Synthesis [11.90100976089832]
  We develop a novel generative deep learning approach for human mobility modeling and synthesis. It incorporates both activity patterns and location trajectories using open-source data. The model can be fine-tuned with local data, allowing it to adapt to accurately represent mobility patterns across diverse regions.
  arXiv  Detail & Related papers  (2024-05-24T02:04:10Z)
• Dynamic Graph Unlearning: A General and Efficient Post-Processing Method via Gradient Transformation [24.20087360102464]
  We study the dynamic graph unlearning for the first time and propose an effective, efficient, general, and post-processing method to implement DGNN unlearning.<n>Our method has the potential to handle future unlearning requests with significant performance gains.
  arXiv  Detail & Related papers  (2024-05-23T10:26:18Z)
• Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning [53.81365215811222]
  We provide a review and summary of the latest approaches, strategies, and insights that address distribution shifts within the context of graph learning. We categorize existing graph learning methods into several essential scenarios, including graph domain adaptation learning, graph out-of-distribution learning, and graph continual learning. We discuss the potential applications and future directions for graph learning under distribution shifts with a systematic analysis of the current state in this field.
  arXiv  Detail & Related papers  (2024-02-26T07:52:40Z)
• Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach [71.67506068703314]
  Long-term urban mobility predictions play a crucial role in the effective management of urban facilities and services. Traditionally, urban mobility data has been structured as videos, treating longitude and latitude as fundamental pixels. In our research, we introduce a fresh perspective on urban mobility prediction. Instead of oversimplifying urban mobility data as traditional video data, we regard it as a complex time series.
  arXiv  Detail & Related papers  (2023-12-04T07:39:05Z)
• Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation [19.419836274690816]
  We propose a new spatial-temporal graph learning model (GraphST) for enabling effective self-supervised learning. Our proposed model is an adversarial contrastive learning paradigm that automates the distillation of crucial multi-view self-supervised information. We demonstrate the superiority of our proposed GraphST method in various spatial-temporal prediction tasks on real-life datasets.
  arXiv  Detail & Related papers  (2023-06-19T03:09:35Z)
• PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction [78.05103666987655]
  spatial-temporal Graph Neural Network (GNN) models have emerged as one of the most promising methods to solve this problem. We propose a novel propagation delay-aware dynamic long-range transFormer, namely PDFormer, for accurate traffic flow prediction. Our method can not only achieve state-of-the-art performance but also exhibit competitive computational efficiency.
  arXiv  Detail & Related papers  (2023-01-19T08:42:40Z)
• Flow descriptors of human mobility networks [0.0]
  We propose a systematic analysis to characterize mobility network flows and topology and assess their impact into individual traces. This framework is suitable to assess urban planning, optimize transportation, measure the impact of external events and conditions, monitor internal dynamics and profile users according to their movement patterns.
  arXiv  Detail & Related papers  (2020-03-16T15:27:00Z)
• Graph Representation Learning via Graphical Mutual Information Maximization [86.32278001019854]
  We propose a novel concept, Graphical Mutual Information (GMI), to measure the correlation between input graphs and high-level hidden representations. We develop an unsupervised learning model trained by maximizing GMI between the input and output of a graph neural encoder.
  arXiv  Detail & Related papers  (2020-02-04T08:33:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.