Human Mobility Datasets Enriched With Contextual and Social Dimensions

• URL: http://arxiv.org/abs/2510.02333v1
• Date: Fri, 26 Sep 2025 07:45:27 GMT
• Title: Human Mobility Datasets Enriched With Contextual and Social Dimensions
• Authors: Chiara Pugliese, Francesco Lettich, Guido Rocchietti, Chiara Renso, Fabio Pinelli,
• Abstract summary: We present two datasets of semantically enriched human trajectories, together with a pipeline to build them.<n>The trajectories are publicly available GPS traces retrieved from OpenStreetMap.<n>A novel semantic feature is the inclusion of synthetic, realistic social media posts generated by Large Language Models.
• Score: 1.0268257686354103
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this resource paper, we present two publicly available datasets of semantically enriched human trajectories, together with the pipeline to build them. The trajectories are publicly available GPS traces retrieved from OpenStreetMap. Each dataset includes contextual layers such as stops, moves, points of interest (POIs), inferred transportation modes, and weather data. A novel semantic feature is the inclusion of synthetic, realistic social media posts generated by Large Language Models (LLMs), enabling multimodal and semantic mobility analysis. The datasets are available in both tabular and Resource Description Framework (RDF) formats, supporting semantic reasoning and FAIR data practices. They cover two structurally distinct, large cities: Paris and New York. Our open source reproducible pipeline allows for dataset customization, while the datasets support research tasks such as behavior modeling, mobility prediction, knowledge graph construction, and LLM-based applications. To our knowledge, our resource is the first to combine real-world movement, structured semantic enrichment, LLM-generated text, and semantic web compatibility in a reusable framework.

Related papers

• Mapping the Web of Science, a large-scale graph and text-based dataset with LLM embeddings [0.722741581069214]
  Large text data sets inherit two types of features: the text itself, and its information conveyed through semantics, and its relationship to other texts through links, references, or shared attributes.<n>We propose a new embedding method for the Web of Science dataset, containing 56 million scientific publications through the lens of our proposed embedding method.
  arXiv  Detail & Related papers  (2026-02-04T15:02:32Z)
• TrajSceneLLM: A Multimodal Perspective on Semantic GPS Trajectory Analysis [0.0]
  We propose TrajSceneLLM, a multimodal perspective for enhancing semantic understanding of GPS trajectories.<n>We validate the proposed framework on Travel Mode Identification (TMI), a critical task for analyzing travel choices and understanding mobility behavior.<n>This semantic enhancement promises significant potential for diverse downstream applications and future research in artificial intelligence.
  arXiv  Detail & Related papers  (2025-06-19T15:31:40Z)
• Spatial Knowledge Graph-Guided Multimodal Synthesis [78.11669780958657]
  We introduce a novel multimodal synthesis approach guided by spatial knowledge graphs, grounded in the concept of knowledge-to-data generation.<n>In experiments, data synthesized from diverse types of spatial knowledge, including direction and distance, enhance the spatial perception and reasoning abilities of MLLMs markedly.<n>We hope that the idea of knowledge-based data synthesis can advance the development of spatial intelligence.
  arXiv  Detail & Related papers  (2025-05-28T17:50:21Z)
• RouteNator: A Router-Based Multi-Modal Architecture for Generating Synthetic Training Data for Function Calling LLMs [3.41612427812159]
  In digital content creation tools, users express their needs through natural language queries that must be mapped to API calls.<n>Existing approaches to synthetic data generation fail to replicate real-world data distributions.<n>We present a novel router-based architecture that generates high-quality synthetic training data.
  arXiv  Detail & Related papers  (2025-05-15T16:53:45Z)
• MapExplorer: New Content Generation from Low-Dimensional Visualizations [60.02149343347818]
  Low-dimensional visualizations, or "projection maps," are widely used to interpret large-scale and complex datasets.<n>These visualizations not only aid in understanding existing knowledge spaces but also implicitly guide exploration into unknown areas.<n>We introduce MapExplorer, a novel knowledge discovery task that translates coordinates within any projection map into coherent, contextually aligned textual content.
  arXiv  Detail & Related papers  (2024-12-24T20:16:13Z)
• Capturing research literature attitude towards Sustainable Development Goals: an LLM-based topic modeling approach [0.7806050661713976]
  The Sustainable Development Goals were formulated by the United Nations in 2015 to address these global challenges by 2030. Natural language processing techniques can help uncover discussions on SDGs within research literature. We propose a completely automated pipeline to fetch content from the Scopus database and prepare datasets dedicated to five groups of SDGs.
  arXiv  Detail & Related papers  (2024-11-05T09:37:23Z)
• Deciphering Human Mobility: Inferring Semantics of Trajectories with Large Language Models [10.841035090991651]
  This paper defines semantic inference through three key dimensions: user occupation category, activity, sequence and trajectory description. We propose Trajectory Semantic Inference with Large Language Models (TSI-LLM) framework to leverage semantic analysis of trajectory data.
  arXiv  Detail & Related papers  (2024-05-30T08:55:48Z)
• Large Language Models on Graphs: A Comprehensive Survey [77.16803297418201]
  We provide a systematic review of scenarios and techniques related to large language models on graphs. We first summarize potential scenarios of adopting LLMs on graphs into three categories, namely pure graphs, text-attributed graphs, and text-paired graphs. We discuss the real-world applications of such methods and summarize open-source codes and benchmark datasets.
  arXiv  Detail & Related papers  (2023-12-05T14:14:27Z)
• DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text [73.68051228972024]
  Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when relying on their internal knowledge. Retrieval-augmented LLMs have emerged as a potential solution to ground LLMs in external knowledge.
  arXiv  Detail & Related papers  (2023-10-31T04:37:57Z)
• MMSum: A Dataset for Multimodal Summarization and Thumbnail Generation of Videos [106.06278332186106]
  Multimodal summarization with multimodal output (MSMO) has emerged as a promising research direction. Numerous limitations exist within existing public MSMO datasets. We have meticulously curated the textbfMMSum dataset.
  arXiv  Detail & Related papers  (2023-06-07T07:43:11Z)
• Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning [51.90524745663737]
  A key innovation is our use of explanations as features, which can be used to boost GNN performance on downstream tasks. Our method achieves state-of-the-art results on well-established TAG datasets. Our method significantly speeds up training, achieving a 2.88 times improvement over the closest baseline on ogbn-arxiv.
  arXiv  Detail & Related papers  (2023-05-31T03:18:03Z)
• TRoVE: Transforming Road Scene Datasets into Photorealistic Virtual Environments [84.6017003787244]
  This work proposes a synthetic data generation pipeline to address the difficulties and domain-gaps present in simulated datasets. We show that using annotations and visual cues from existing datasets, we can facilitate automated multi-modal data generation.
  arXiv  Detail & Related papers  (2022-08-16T20:46:08Z)

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.