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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: 20 July 2025 | Viewed by 1844

Special Issue Editors


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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

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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

Manuscript Submission Information

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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 (1 paper)

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Research

25 pages, 15932 KiB  
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 1166
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
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