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Information
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AI and Data Analysis in Smart Cities
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AI and Data Analysis in Smart Cities

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 7798

Special Issue Editors

Prof. Dr. Bin Meng

E-Mail Website
Guest Editor
Department of Urban Sciences, College of Arts & Sciences, Beijing Union University, Beijing 100191, China
Interests: big data mining; spatial data analysis; spatiotemporal behavior; cultural perception
Special Issues, Collections and Topics in MDPI journals
Dr. Shaohua Wang

E-Mail Website
Guest Editor
State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: remote sensing AI; GeoAI; spatiotemporal big data analysis; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid urbanization and technological advancements of the 21st century have given rise to the concept of smart cities, where digital technologies are integrated to enhance the quality and performance of urban services. This Special Issue focuses on the pivotal role of artificial intelligence (AI) and data analysis in shaping the future of smart cities. We aim to gather cutting-edge research and innovative applications that demonstrate how AI and data-driven approaches can address urban challenges, improve sustainability, optimize resource management, and enhance the overall quality of life for city dwellers.

We invite original research articles, reviews, and case studies that explore, but are not limited to, the following topics:

  • AI-driven urban planning and infrastructure development;
  • Data analysis for smart transportation and mobility solutions;
  • Machine learning applications in energy efficiency and management;
  • Integration of IoT devices and sensor networks in urban settings;
  • Predictive analytics for public safety and emergency response;
  • AI in environmental monitoring and sustainable practices;
  • Big data platforms and architectures for urban data management;
  • Human-centric AI solutions for citizen engagement and services;
  • Ethical considerations and data privacy in AI implementations;
  • Case studies on AI-enabled smart city initiatives and projects.

Prof. Dr. Bin Meng
Dr. Shaohua Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • data analysis
  • smart cities
  • urban ryhthms
  • spatial analysis
  • sustainable cities and communities
  • spatial computing
  • urban analytics and GeoAI

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

18 pages, 1308 KB  
Article
A New Chaotic Interval-Based Multi-Objective Honey Badger Algorithm for Real-Time Fire Localization
by Khedija Arour, Hadhami Kaabi, Mohamed Ben Farah and Raouf Abozariba
Information 2026, 17(2), 144; https://doi.org/10.3390/info17020144 - 2 Feb 2026
Viewed by 129
Abstract
Real-time fire localization in urban environments remains a significant challenge due to sparse IoT sensor deployments, measurement uncertainties, and the computational uses of AI-based estimation techniques. To address these limitations, this paper proposes a Chaotic Interval-Based Multi-Objective Honey Badger Algorithm (CI-MOHBA) designed to [...] Read more.
Real-time fire localization in urban environments remains a significant challenge due to sparse IoT sensor deployments, measurement uncertainties, and the computational uses of AI-based estimation techniques. To address these limitations, this paper proposes a Chaotic Interval-Based Multi-Objective Honey Badger Algorithm (CI-MOHBA) designed to improve the accuracy and reliability of fire source localization under uncertain and limited sensor data. The approach formulates localization as a multi-objective optimization problem that simultaneously minimizes source estimation error, false alarm rates, and computation time. CI-MOHBA integrates a new chaotic map to improve global search capability and interval arithmetic to effectively manage sensor uncertainty within sparse measurement environments. Experimental evaluation of the proposed chaotic map, supported by entropy convergence analysis and Lyapunov exponent verification, demonstrates the stability and robustness of the proposed technique. Results indicate that CI-MOHBA achieves an average localization error of 0.73 m and a false alarm rate of 8.2%, while maintaining high computational efficiency. Results show that the proposed algorithm is well-suited for real-time fire localization in urban IoT-based monitoring systems. Full article
(This article belongs to the Special Issue AI and Data Analysis in Smart Cities)
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18 pages, 853 KB  
Article
Safety in Smart Cities—Automatic Recognition of Dangerous Driving Styles
by Vincenzo Dentamaro, Lorenzo Di Maggio, Stefano Galantucci, Donato Impedovo and Giuseppe Pirlo
Information 2026, 17(1), 44; https://doi.org/10.3390/info17010044 - 4 Jan 2026
Viewed by 288
Abstract
Road safety ranks among the most apparent concerns in present-day urban existence, with risky driving the most prevalent cause of road crashes. In this paper, we present an external camera video-based automatic hazardous driving behavior detection model for use in smart cities. We [...] Read more.
Road safety ranks among the most apparent concerns in present-day urban existence, with risky driving the most prevalent cause of road crashes. In this paper, we present an external camera video-based automatic hazardous driving behavior detection model for use in smart cities. We addressed the problem with a holistic approach covering data collection to hazardous driving behavior classification including zig-zag driving, risky overtaking, and speeding over a pedestrian crossing. Our strategy employs a specially generated dataset with diverse driving situations under diverse traffic conditions and luminosities. We advocate for a Multi-Speed Transformer model with dual vehicle trajectory data timescale operation to capture near-future actions in the context of extended driving trends. A new contribution lies in our symbiotic system which, apart from the detection of unsafe driving, also assumes the responsibility of triggering countermeasures through a real-time continuous loop with vehicle systems. Empirical results demonstrate the Multi-Speed Transformer’s performance with 97.5% in accuracy and 93% in F1-score over our balanced corpus, surpassing comparison baselines including Temporal Convolutional Networks and Random Forest classifiers by significant amounts. The performance is boosted to 98.7% in accuracy as well as 95.5% in F1-score with the symbiotic framework. They confirm the promise of leading-edge neural architectures paired with symbiotic systems in enhancing road safety in smart cities. The ability of the system to provide real-time risky driving behavior detection with mitigation offers a real-world solution for the prevention of accidents while not restricting driver autonomy, a balance between automatic intervention, and passive monitoring. Empirical evidence on the TRAF-derived corpus, which includes 18 videos and 414 labelled trajectory segments, indicates that the Multi-Speed Transformer reaches an accuracy of 97.5% and an F1-score of 93% under the balanced-training protocol, and in this configuration it consistently surpasses the considered baselines when we use the same data splits and the same evaluation metrics. Full article
(This article belongs to the Special Issue AI and Data Analysis in Smart Cities)
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31 pages, 5952 KB  
Article
Low-Cost Smart Cane for Visually Impaired People with Pathway Surface Detection and Distance Estimation Using Weighted Bounding Boxes and Depth Mapping
by Teepakorn Mungdee, Prakaidaw Ramsiri, Kanyarak Khabuankla, Pipat Khambun, Thanakrit Nupim and Ponlawat Chophuk
Information 2025, 16(8), 707; https://doi.org/10.3390/info16080707 - 19 Aug 2025
Cited by 1 | Viewed by 6713
Abstract
Visually impaired individuals are at a high risk of accidents due to sudden changes in walking surfaces and surrounding obstacles. Existing smart cane systems lack the capability to detect pathway surface transition points with accurate distance estimation and danger-level assessment. This study proposes [...] Read more.
Visually impaired individuals are at a high risk of accidents due to sudden changes in walking surfaces and surrounding obstacles. Existing smart cane systems lack the capability to detect pathway surface transition points with accurate distance estimation and danger-level assessment. This study proposes a low-cost smart cane that integrates a novel Pathway Surface Transition Point Detection (PSTPD) method with enhanced obstacle detection. The system employs dual RGB cameras, an ultrasonic sensor, and YOLO-based models to deliver real-time alerts based on object type, surface class, distance, and severity. It comprises three modules: (1) obstacle detection and classification into mild, moderate, or severe levels; (2) pathway surface detection across eight surface types with distance estimation using weighted bounding boxes and depth mapping; and (3) auditory notifications. Experimental results show a mean Average Precision (mAP@50) of 0.70 for obstacle detection and 0.92 for surface classification. The average distance estimation error was 0.3 cm for obstacles and 4.22 cm for pathway surface transition points. Additionally, the PSTPD method also demonstrated efficient processing with an average runtime of 0.6 s per instance. Full article
(This article belongs to the Special Issue AI and Data Analysis in Smart Cities)
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