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Data Mining and Artificial Intelligence for Urban Informatics in Smart City

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: closed (20 February 2026) | Viewed by 3152

Special Issue Editors

Prof. Dr. Yunyun Niu

E-Mail Website
Guest Editor
School of Information Engineering, China University of Geosciences in Beijing, Beijing 100083, China
Interests: path planning; logistics scheduling; smart city; intelligent transportation system; reinforcement learning; multi-objective optimization
Prof. Dr. Jianhua Xiao

E-Mail Website
Guest Editor
The Research Center of Logistics, Nankai University, Tianjin 300071, China
Interests: logistics system optimization; big data; smart logistics

Special Issue Information

Dear Colleagues,

With the accelerating process of urbanization, smart cities have started to represent an important direction for future urban development. In this context, the application of data mining and artificial intelligence (AI) technologies plays a crucial role in the development of smart cities. The application of data mining and AI technologies in the field of smart city informatics covers various aspects, including disaster warning, transportation, route planning, logistics scheduling, and more. Through the application of these technologies, urban management and service levels can be effectively improved, thereby achieving the goal of sustainable urban development. This Special Issue aims to explore the application of data mining and AI in smart cities, delve into the challenges faced in the development of smart cities, and propose solutions. The suggested topics related to this Special Issue include, but are not limited to, the following:

  1. The optimization and management of smart city logistics dispatching systems;
  2. Algorithm research on traffic flow prediction;
  3. Disaster warning systems and response strategies in smart cities;
  4. Research on artificial-intelligence-based vehicle routing algorithms;
  5. The optimization of waste recycling processes based on artificial intelligence and data mining;
  6. Urban supply chain risk management under epidemic conditions;
  7. Reinforcement-learning-based traffic signal control at intersections.

Prof. Dr. Yunyun Niu
Prof. Dr. Jianhua Xiao
Guest Editors

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Keywords

  • route planning
  • traffic management
  • waste management
  • supply chain
  • disaster warning
  • artificial intelligence
  • data mining
  • smart city

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Published Papers (2 papers)

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Research

31 pages, 9962 KB  
Article
Adaptive Spatio-Temporal Federated Learning for Traffic Flow Prediction: Framework and Aggregation Approaches Evaluation
by Basma Alsehaimi, Ohoud Alzamzami, Nahed Alowidi and Manar Ali
Appl. Sci. 2026, 16(5), 2402; https://doi.org/10.3390/app16052402 - 28 Feb 2026
Viewed by 326
Abstract
Traffic flow prediction (TFP) is a fundamental component of intelligent transportation systems (ITS) that supports traffic management, congestion mitigation, and route planning. Although recent advances in deep learning have demonstrated strong capability in modeling non-linear spatio-temporal correlations, most existing approaches rely on centralized [...] Read more.
Traffic flow prediction (TFP) is a fundamental component of intelligent transportation systems (ITS) that supports traffic management, congestion mitigation, and route planning. Although recent advances in deep learning have demonstrated strong capability in modeling non-linear spatio-temporal correlations, most existing approaches rely on centralized training paradigms, which incur substantial communication costs, high computational overhead, and significant data privacy risks. Federated Learning (FL) has emerged as a promising alternative by enabling decentralized model training across distributed clients while reducing privacy risks and communication overhead. However, existing FL-based TFP frameworks often employ local models with limited capacity to capture complex spatio-temporal dependencies, and their reliance on the conventional FedAvg aggregation approach restricts robustness under heterogeneous traffic data distributions. To address these challenges, this study proposes the FedASTAM framework, which integrates FL with the Adaptive Spatio-Temporal Attention-based Multi-Model (ASTAM) to effectively model complex and non-linear spatio-temporal traffic correlations in a data-local FL setting. Within FedASTAM, the road network is divided into sub-regions using spectral clustering, allowing each sub-region to train a local ASTAM model tailored to localized and heterogeneous traffic patterns. At the central server, locally trained models are aggregated using seven aggregation schemes, including the classical FedAvg, to optimize global model updates while preserving data locality. Extensive experiments conducted on two real-world benchmark datasets, PeMS04 and PeMS08, demonstrate that FedASTAM achieved strong and stable predictive performance while keeping raw data localized throughout the federated training process. The results further indicate that the aggregation approaches used in the proposed FedASTAM framework generally outperform classical FedAvg under heterogeneous traffic conditions, highlighting FedASTAM as an effective approach for traffic flow prediction in complex, distributed ITS environments. Full article
(This article belongs to the Special Issue Data Mining and Artificial Intelligence for Urban Informatics in Smart City)
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25 pages, 15932 KB  
Article
An Optimization Framework for Waste Treatment Center Site Selection Considering Nighttime Light Remote Sensing Data and Waste Production Fluctuations
by Junbao Xia, Yanping Liu, Haozhong Yang and Guodong Zhu
Appl. Sci. 2024, 14(22), 10136; https://doi.org/10.3390/app142210136 - 5 Nov 2024
Cited by 1 | Viewed by 1813
Abstract
As urbanization accelerates, the management of urban solid waste poses increasingly intricate challenges. Traditional urban metrics, such as GDP and per capita consumption rates, have become inadequate for accurately reflecting the realities of waste generation; moreover, the linear correlation between these metrics and [...] Read more.
As urbanization accelerates, the management of urban solid waste poses increasingly intricate challenges. Traditional urban metrics, such as GDP and per capita consumption rates, have become inadequate for accurately reflecting the realities of waste generation; moreover, the linear correlation between these metrics and waste production is progressively diminishing. Consequently, this study introduces a novel methodology leveraging nighttime light remote sensing data to enhance the precision of urban solid waste production forecasts. By processing remote sensing data to mitigate noise and integrating it with conventional urban datasets, an innovative index system and predictive model were developed. Using Beijing as a case study, the gradient boosting regression algorithm yielded a prediction accuracy of 92%. Furthermore, in light of the substantial costs associated with waste recovery route planning and site selection for treatment facilities, this research further devised a location and distribution framework for waste treatment centers based on high-precision predictions of waste production while employing multi-objective evolutionary algorithms (MOEAs) alongside the non-dominated sorting genetic algorithm II (NSGA-II) for optimization. Distinct from prior studies, this study suggests that service point waste quantities are not fixed values but rather adhere to a normal distribution within specified ranges and thus provides a more realistic simulation of fluctuations in waste production while enhancing both the robustness and predictive accuracy of the model. In conclusion, by incorporating nighttime light remote sensing data along with advanced machine learning techniques, this study markedly improves forecasting accuracy for waste production while offering effective optimization strategies for site selection and recovery route planning—thereby establishing a robust data foundation aimed at refining urban solid waste management systems. Full article
(This article belongs to the Special Issue Data Mining and Artificial Intelligence for Urban Informatics in Smart City)
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