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Information
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Big Data Analytics in Smart Cities
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Big Data Analytics in Smart Cities

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 2545

Special Issue Editors

Dr. Tahir Cetin Akinci

E-Mail Website
Guest Editor
Winston Chung Global Energy Center (WCGEC), University of California Riverside, Riverside, CA 92521, USA
Interests: deep learning; machine learning; signal processing; data analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mustafa Ilhan Akbas

E-Mail Website
Guest Editor
Electrical Engineering and Computer Science, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA
Interests: computer engineering; cyber–physical systems; software defined networks
Special Issues, Collections and Topics in MDPI journals
Dr. Maurizio Vincini

E-Mail Website
Guest Editor
Department of Information Engineering, University of Modena and Reggio Emilia, 41121 Modena, Italy
Interests: Big Data integration; information process; application integration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of Big Data analytics into the fabric of urban environments is directing cities towards advanced technologies to enhance efficiency, sustainability, and overall quality of life in the continually evolving urbanization landscape. As our cities become "smart", harnessing the power of extensive datasets becomes increasingly imperative for informed decision making and effective resource management.

Big Data analytics in Smart Cities represents a paradigm shift in how urban planners, policymakers, and technology experts approach the challenges of modern urban living. Cities, leveraging the data richness generated by various urban systems, unlock invaluable insights, promising to optimize city functions, alleviate challenges, and ultimately create more livable, resilient, and sustainable urban areas.

In the context of Smart Cities, Big Data analytics has acquired a versatile, in-depth understanding. It shapes the transformative potential of data for tomorrow’s cities, influencing real-time data usage for traffic management, predictive analytics for resource allocation, and more, spanning areas such as security, energy transmission, regional governance, and beyond. Technology, data, and urban intelligence engage in a complex interplay that shapes the cities of the future.

We invite original research articles and reviews to be submitted to this Special Issue. Research areas may include (but are not limited to) the following:

  • Autonomous/electric vehicles
  • Electric vehicle charging station location planning
  • Smart City applications
  • Statistics and mathematics
  • Crowdsensing
  • Game theory
  • Agent based modeling
  • Engineering/traffic Systems
  • Computer and computational sciences
  • Electrical, electronic, and automation systems
  • Aviation and drone technologies
  • Energy systems
  • Social media

Dr. Tahir Cetin Akinci
Dr. Mustafa Akbas
Dr. Maurizio Vincini
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 100 words) can be sent to the Editorial Office for announcement on this website.

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 1600 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

  • smart cities
  • Big Data
  • AI-based data processing
  • transportation
  • energy
  • environment
  • health
  • social media

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

Published Papers (2 papers)

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Research

23 pages, 788 KiB  
Article
Complexity Evaluation of Test Scenarios for Autonomous Vehicle Safety Validation Using Information Theory
by Maja Issler, Quentin Goss and Mustafa İlhan Akbaş
Information 2024, 15(12), 772; https://doi.org/10.3390/info15120772 - 3 Dec 2024
Viewed by 711
Abstract
The validation of autonomous vehicles remains a vexing challenge for the automotive industry’s goal of fully autonomous driving. The systematic hierarchization of the test scenarios would provide valuable insights for the development, testing, and verification of autonomous vehicles, enabling nuanced performance evaluations based [...] Read more.
The validation of autonomous vehicles remains a vexing challenge for the automotive industry’s goal of fully autonomous driving. The systematic hierarchization of the test scenarios would provide valuable insights for the development, testing, and verification of autonomous vehicles, enabling nuanced performance evaluations based on scenario complexity. In this paper, an information entropy-based quantification method is proposed to evaluate the complexity of autonomous vehicle validation scenarios. The proposed method addresses the dynamic uncertainties within driving scenarios in a comprehensive way which includes the unpredictability of dynamic agents such as autonomous vehicles, human-driven vehicles, and pedestrians. The numerical complexity calculation of the approach and the ranking of the scenarios are presented through sample scenarios. To automate processes and assist with the calculations, a novel software tool with a user-friendly interface is developed. The performance of the approach is also evaluated through six example driving scenarios, then through extensive simulation using an open-source microscopic traffic simulator. The performance evaluation results confirm the numerical classification and demonstrate the method’s adaptability to diverse scenarios with a comparison of complexity calculation ranking to the ratio of collision, near collision, and normal operation tests observed during simulation testing. The proposed quantification method contributes to the improvement of autonomous vehicle validation procedures by addressing the multifaceted nature of scenario complexities. Beyond advancing the field of validation, the approach also aligns with the broad and active drive of the industry for the widespread deployment of fully autonomous driving. Full article
(This article belongs to the Special Issue Big Data Analytics in Smart Cities)
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19 pages, 5361 KiB  
Article
Research on Resident Behavioral Activities Based on Social Media Data: A Case Study of Four Typical Communities in Beijing
by Zhiyuan Ou, Bingqing Wang, Bin Meng, Changsheng Shi and Dongsheng Zhan
Information 2024, 15(7), 392; https://doi.org/10.3390/info15070392 - 5 Jul 2024
Viewed by 954
Abstract
With the support of big data mining techniques, utilizing social media data containing location information and rich semantic text information can construct large-scale daily activity OD flows for urban populations, providing new data resources and research perspectives for studying urban spatiotemporal structures. This [...] Read more.
With the support of big data mining techniques, utilizing social media data containing location information and rich semantic text information can construct large-scale daily activity OD flows for urban populations, providing new data resources and research perspectives for studying urban spatiotemporal structures. This paper employs the ST-DBSCAN algorithm to identify the residential locations of Weibo users in four communities and then uses the BERT model for activity-type classification of Weibo texts. Combined with the TF-IDF method, the results are analyzed from three aspects: temporal features, spatial features, and semantic features. The research findings indicate: ① Spatially, residents’ daily activities are mainly centered around their residential locations, but there are significant differences in the radius and direction of activity among residents of different communities; ② In the temporal dimension, the activity intensities of residents from different communities exhibit uniformity during different time periods on weekdays and weekends; ③ Based on semantic analysis, the differences in activities and venue choices among residents of different communities are deeply influenced by the comprehensive characteristics of the communities. This study explores methods for OD information mining based on social media data, which is of great significance for expanding the mining methods of residents’ spatiotemporal behavior characteristics and enriching research on the configuration of public service facilities based on community residents’ activity spaces and facility demands. Full article
(This article belongs to the Special Issue Big Data Analytics in Smart Cities)
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