Plataforma para visualização geo-temporal de apinhamento turístico

• URL: http://arxiv.org/abs/2504.13952v1
• Date: Wed, 16 Apr 2025 11:11:11 GMT
• Title: Plataforma para visualização geo-temporal de apinhamento turístico
• Authors: Rodrigo Simões, Fernando Brito e Abreu, Adriano Lopes,
• Abstract summary: Tourist crowding degrades the visitor experience and negatively impacts the environment and the local population.<n>This motivated us to develop, within the framework of the European RESETTING project related to the digital transformation of tourism, a platform to visualize this crowding.<n>The platform takes into account the carrying capacity of the target sites when calculating crowding density.
• Score: 45.498315114762484
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tourist crowding degrades the visitor experience and negatively impacts the environment and the local population, potentially making tourism in popular destinations unsustainable. This motivated us to develop, within the framework of the European RESETTING project related to the digital transformation of tourism, a platform to visualize this crowding, exploring historical data, detecting patterns and trends and predicting future events. The ultimate goal is to support short- and medium-term decision-making to mitigate the phenomenon. To this end, the platform takes into account the carrying capacity of the target sites when calculating crowding density. The integration of data from different sources is achieved with an extensible, connector-based architecture. Three scenarios for using the platform are described, relating to major annual crowding events. Two of them, in the municipality of Lisbon, are based on data from a mobile network provided by the LxDataLab initiative. The third, in Melbourne, Australia, using public data from a network of movement sensors called the Pedestrian Counting System. An experiment to evaluate the usability of the proposed platform using NASA-TLX is also described. -- -- O apinhamento tur\'istico degrada a experi\^encia dos visitantes e impacta negativamente o ambiente e a popula\c{c}\~ao local, podendo tornar insustent\'avel o turismo em destinos populares. Isto motivou-nos a desenvolver, no \^ambito do projeto europeu RESETTING relacionado com a transforma\c{c}\~ao digital do turismo, uma plataforma para visualizar este apinhamento, explorando dados hist\'oricos, detetando padr\~oes e tend\^encias e prevendo eventos futuros. O objetivo final \'e apoiar a tomada de decis\~ao, a curto e m\'edio prazo, para mitigar o fen\'omeno. Para tal, a plataforma considera a capacidade de carga dos locais alvo no c\'alculo da densidade de apinhamento. A integra\c{c}\~ao de dados de diversas fontes \'e conseguida com uma arquitetura extens\'ivel, \`a base de conetores. S\~ao descritos tr\^es cen\'arios de utiliza\c{c}\~ao da plataforma, relativos a eventos anuais de grande apinhamento. Dois deles, no munic\'ipio de Lisboa, baseados em dados de uma rede m\'ovel disponibilizados pela iniciativa LxDataLab. O terceiro, em Melbourne na Austr\'alia, utilizando dados p\'ublicos de uma rede de sensores de movimento designada de Pedestrian Counting System. \'E ainda descrita uma experi\^encia de avalia\c{c}\~ao da usabilidade da plataforma proposta, usando o NASA-TLX.

Related papers

• Short-video Propagation Influence Rating: A New Real-world Dataset and A New Large Graph Model [55.58701436630489]
  Cross-platform Short-Video dataset includes 117,720 videos, 381,926 samples, and 535 topics across 5 biggest Chinese platforms.<n>Large Graph Model (LGM) named NetGPT can bridge heterogeneous graph-structured data with the powerful reasoning ability and knowledge of Large Language Models (LLMs)<n>Our NetGPT can comprehend and analyze the short-video propagation graph, enabling it to predict the long-term propagation influence of short-videos.
  arXiv  Detail & Related papers  (2025-03-31T05:53:15Z)
• Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks [82.82866901799565]
  We build a new set of multimodal remote sensing benchmark datasets (including hyperspectral, multispectral, SAR) for the study purpose of the cross-city semantic segmentation task. Beyond the single city, we propose a high-resolution domain adaptation network, HighDAN, to promote the AI model's generalization ability from the multi-city environments. HighDAN is capable of retaining the spatially topological structure of the studied urban scene well in a parallel high-to-low resolution fusion fashion.
  arXiv  Detail & Related papers  (2023-09-26T23:55:39Z)
• Smart City Digital Twin Framework for Real-Time Multi-Data Integration and Wide Public Distribution [2.864893907775703]
  Digital Twins are digital replica of real entities and are becoming fundamental tools to monitor and control the status of entities. Digital Twins are becoming fundamental tools to monitor and control the status of entities. Snap4City platform is released as open-source, and made available through GitHub and as docker compose.
  arXiv  Detail & Related papers  (2023-09-23T14:53:04Z)
• Data-driven quantitative analysis of an integrated open digital ecosystems platform for user-centric energy retrofits: A case study in Northern Sweden [0.0]
  We present an open digital ecosystem based on web-framework with a functional back-end server in user-centric energy retrofits. Data-driven web framework is proposed for building energy renovation benchmarking.
  arXiv  Detail & Related papers  (2023-09-21T08:05:10Z)
• Crowd Safety Manager: Towards Data-Driven Active Decision Support for Planning and Control of Crowd Events [1.3764085113103222]
  The paper introduces the Bowtie model, a comprehensive framework designed to assess and predict risk levels. The proposed framework is applied to the Crowd Safety Manager project in Scheveningen.
  arXiv  Detail & Related papers  (2023-07-31T18:47:56Z)
• Semi-supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation [59.6553058160943]
  We propose a semi-supervised learning (SSL) method of automatically estimating building height from Mapillary SVI and OpenStreetMap data. The proposed method leads to a clear performance boosting in estimating building heights with a Mean Absolute Error (MAE) around 2.1 meters. The preliminary result is promising and motivates our future work in scaling up the proposed method based on low-cost VGI data.
  arXiv  Detail & Related papers  (2023-07-05T18:16:30Z)
• LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting [65.71129509623587]
  Road traffic forecasting plays a critical role in smart city initiatives and has experienced significant advancements thanks to the power of deep learning. However, the promising results achieved on current public datasets may not be applicable to practical scenarios. We introduce the LargeST benchmark dataset, which includes a total of 8,600 sensors in California with a 5-year time coverage.
  arXiv  Detail & Related papers  (2023-06-14T05:48:36Z)
• PANet: Perspective-Aware Network with Dynamic Receptive Fields and Self-Distilling Supervision for Crowd Counting [63.84828478688975]
  We propose a novel perspective-aware approach called PANet to address the perspective problem. Based on the observation that the size of the objects varies greatly in one image due to the perspective effect, we propose the dynamic receptive fields (DRF) framework. The framework is able to adjust the receptive field by the dilated convolution parameters according to the input image, which helps the model to extract more discriminative features for each local region.
  arXiv  Detail & Related papers  (2021-10-31T04:43:05Z)
• A Data-Driven Analytical Framework of Estimating Multimodal Travel Demand Patterns using Mobile Device Location Data [5.902556437760098]
  This paper presents a data-driven analytical framework to extract multimodal travel demand patterns from smartphone location data. A jointly trained single-layer model and deep neural network for travel mode imputation is developed. The framework also incorporates the multimodal transportation network in order to evaluate the closeness of trip routes to the nearby rail, metro, highway and bus lines.
  arXiv  Detail & Related papers  (2020-12-08T22:49:44Z)
• Urban Crowdsensing using Social Media: An Empirical Study on Transformer and Recurrent Neural Networks [0.7090165638014329]
  We utilize publicly available social media datasets and use them as the basis for two urban sensing problems. One main contribution of this work is our collected dataset from Twitter and Flickr. We demonstrate the usefulness of this dataset with two preliminary supervised learning approaches.
  arXiv  Detail & Related papers  (2020-12-05T15:36:50Z)

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.