A Review of Urban Digital Twins Integration, Challenges, and Future Directions in Smart City Development

Sustainability, Journal Year: 2024, Volume and Issue: 16(19), P. 8337 - 8337

Published: Sept. 25, 2024

This review paper explores Urban Digital Twins (UDTs) and their crucial role in developing smarter cities, focusing on making urban areas more sustainable well-planned. The methodology adopted an extensive literature across multiple academic databases related to UDTs smart sustainability, environments, conducted by a bibliometric analysis using VOSviewer identify key research trends qualitative through thematic categorization. shows how can significantly change cities are managed planned examining examples from like Singapore Dubai. study points out the main hurdles gathering data, connecting systems, handling vast amounts of information, different technologies work together. It also sheds light what is missing current research, such as need for solid rules effectively, better cooperation between various city deeper look into affect society. To address gaps, this highlights necessity interdisciplinary collaboration. calls establishing comprehensive models, universal standards, comparative studies among traditional UDT methods. Finally, it encourages industry, policymakers, academics join forces realizing sustainable, cities.

Language: Английский

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Towards the next generation of Geospatial Artificial Intelligence

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2025, Volume and Issue: 136, P. 104368 - 104368

Published: Jan. 20, 2025

Language: Английский

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Optimization Strategies in Consumer Choice Behavior for Personalized Recommendation Systems Based on Deep Reinforcement Learning

Journal of Organizational and End User Computing, Journal Year: 2025, Volume and Issue: 37(1), P. 1 - 35

Published: Jan. 24, 2025

In domains such as e-commerce and media recommendations, personalized recommendation systems effectively alleviate the issue of information overload. However, existing still face challenges in multimodal data processing, sparsity, dynamic changes user preferences. This paper proposes a Hierarchical Generative Reinforcement Learning Recommendation Optimization framework (HG-RLRO) that addresses these issues by integrating data, Adversarial Networks (GAN), Inverse (IRL), Temporal Difference (HTD). HG-RLRO employs multi-agent architecture to handle textual image utilizes GAN generate simulated behavior mitigate sparsity. IRL dynamically infers preferences across multiple time scales.

Language: Английский

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Enriching the metadata of map images: a deep learning approach with geographic information systems-based data augmentation

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 181 - 203

Published: Jan. 1, 2025

Language: Английский

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A survey on data fusion approaches in IoT-based smart cities: Smart applications, taxonomies, challenges, and future research directions

Information Fusion, Journal Year: 2025, Volume and Issue: unknown, P. 103102 - 103102

Published: March 1, 2025

Language: Английский

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Data and data collection for pedestrian planning

Advances in transport policy and planning, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

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Big data fusion with knowledge graph: a comprehensive overview

Applied Intelligence, Journal Year: 2025, Volume and Issue: 55(7)

Published: April 21, 2025

Language: Английский

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Modelling super-diffusion in urban human mobility: a quantum walk approach

Cities, Journal Year: 2025, Volume and Issue: 163, P. 106000 - 106000

Published: April 24, 2025

Language: Английский

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Score-based Graph Learning for Urban Flow Prediction

ACM Transactions on Intelligent Systems and Technology, Journal Year: 2024, Volume and Issue: 15(3), P. 1 - 25

Published: April 1, 2024

Accurate urban flow prediction (UFP) is crucial for a range of smart city applications such as traffic management, planning, and risk assessment. To capture the intrinsic characteristics flow, recent efforts have utilized spatial temporal graph neural networks to deal with complex dependence between in adjacent areas. However, existing network based approaches suffer from several critical drawbacks, including improper representation data, lack semantic correlation modeling among nodes, coarse-grained exploitation external factors. address these issues, we propose DiffUFP , novel probabilistic graph-based framework UFP. consists two key designs: (1) region dynamic extraction method that effectively captures underlying topology, (2) conditional denoising score-based adjacency matrix generator takes spatial, temporal, factors into account when constructing rather than simply concatenation studies. Extensive experiments conducted on real-world datasets demonstrate superiority over state-of-the-art UFP models effect specific modules.

Language: Английский

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An efficient cross-view image fusion method based on selected state space and hashing for promoting urban perception

Information Fusion, Journal Year: 2024, Volume and Issue: unknown, P. 102737 - 102737

Published: Oct. 1, 2024

Language: Английский

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