Special Issue "Algorithms for Smart Cities"

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: 30 September 2020.

Special Issue Editors

Dr. Gloria Cerasela Crisan

Guest Editor
Faculty of Sciences, Vasile Alecsandri University of Bacǎu, Bacǎu 600115, Romania
Interests: artificial intelligence; transportation networks; GIS
Dr. Ha Duy Long
Website
Guest Editor
Atomic Energy and Alternative Energies Commission, Solar Technology Department, Smart Grid Laboratory, 50 avenue du Lac Léman | F-73375 Le Bourget-du-Lac, France
Interests: Energy Management; Electrical Car; Optimization; Smart Building; Smart Grid; Model Predictive Control; Micro-grid; Photovoltaic Thermal Inspection
Prof. Dr. Elena Nechita
Website
Guest Editor
Department of Mathematics, Informatics and Education Sciences, University of Bacau, Bacău 600115, Romania
Interests: Artificial Intelligence; Probability Theory; Education

Special Issue Information

Dear Colleagues,

ICT supports our society in responding to increased human pressure on Earth. Sustainable development challenges urban areas to consume resources more efficiently, to optimize operations, to boost people’s involvement in governance, and to raise the quality of life and environment. Late technological, environmental, and social changes determine the need of articulated strategies for addressing these challenges, comprehensive models of real problems, and effective ICT solutions.

The aim of this Special Issue is to address the broad range of societal issues raised by modern urban communities. The efficient use of physical infrastructure, enhancement of public health and public education, less environmental impact, better resilience of the inhabitants and also of the city structures are the expected topics of interest. The researchers and the practitioners working in artificial intelligence, city logistics, internet of things, data analytics, etc., are invited to submit their original and unpublished works to this Special Issue. Of particular interest are papers describing integrated approaches; for example, those including computer vision, optimization methods, GIS, etc.

Dr. Gloria Cerasela Crisan
Prof. Dr. Elena Nechita
Dr. Ha Duy Long
Guest Editor

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 papers will be 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. Algorithms 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 1000 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
  • city logistics
  • data analytics
  • e-governance
  • e-health
  • image recognition
  • internet of things
  • optimization methods
  • recommender systems
  • remote sensing
  • transportation networks

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Automobile Fine-Grained Detection Algorithm Based on Multi-Improved YOLOv3 in Smart Streetlights
Algorithms 2020, 13(5), 114; https://doi.org/10.3390/a13050114 - 02 May 2020
Abstract
Upgrading ordinary streetlights to smart streetlights to help monitor traffic flow is a low-cost and pragmatic option for cities. Fine-grained classification of vehicles in the sight of smart streetlights is essential for intelligent transportation and smart cities. In order to improve the classification [...] Read more.
Upgrading ordinary streetlights to smart streetlights to help monitor traffic flow is a low-cost and pragmatic option for cities. Fine-grained classification of vehicles in the sight of smart streetlights is essential for intelligent transportation and smart cities. In order to improve the classification accuracy of distant cars, we propose a reformed YOLOv3 (You Only Look Once, version 3) algorithm to realize the detection of various types of automobiles, such as SUVs, sedans, taxis, commercial vehicles, small commercial vehicles, vans, buses, trucks and pickup trucks. Based on the dataset UA-DETRAC-LITE, manually labeled data is added to improve the data balance. First, data optimization for the vehicle target is performed to improve the generalization ability and position regression loss function of the model. The experimental results show that, within the range of 67 m, and through scale optimization (i.e., by introducing multi-scale training and anchor clustering), the classification accuracies of trucks and pickup trucks are raised by 26.98% and 16.54%, respectively, and the overall accuracy is increased by 8%. Secondly, label smoothing and mixup optimization is also performed to improve the generalization ability of the model. Compared with the original YOLO algorithm, the accuracy of the proposed algorithm is improved by 16.01%. By combining the optimization of the position regression loss function of GIOU (Generalized Intersection Over Union), the overall system accuracy can reach 92.7%, which improves the performance by 21.28% compared with the original YOLOv3 algorithm. Full article
(This article belongs to the Special Issue Algorithms for Smart Cities)
Show Figures

Figure 1

Open AccessArticle
Multi-Level Joint Feature Learning for Person Re-Identification
Algorithms 2020, 13(5), 111; https://doi.org/10.3390/a13050111 - 29 Apr 2020
Abstract
In person re-identification, extracting image features is an important step when retrieving pedestrian images. Most of the current methods only extract global features or local features of pedestrian images. Some inconspicuous details are easily ignored when learning image features, which is not efficient [...] Read more.
In person re-identification, extracting image features is an important step when retrieving pedestrian images. Most of the current methods only extract global features or local features of pedestrian images. Some inconspicuous details are easily ignored when learning image features, which is not efficient or robust to for scenarios with large differences. In this paper, we propose a Multi-level Feature Fusion model that combines both global features and local features of images through deep learning networks to generate more discriminative pedestrian descriptors. Specifically, we extract local features from different depths of network by the Part-based Multi-level Net to fuse low-to-high level local features of pedestrian images. Global-Local Branches are used to extract the local features and global features at the highest level. The experiments have proved that our deep learning model based on multi-level feature fusion works well in person re-identification. The overall results outperform the state of the art with considerable margins on three widely-used datasets. For instance, we achieve 96% Rank-1 accuracy on the Market-1501 dataset and 76.1% mAP on the DukeMTMC-reID dataset, outperforming the existing works by a large margin (more than 6%). Full article
(This article belongs to the Special Issue Algorithms for Smart Cities)
Show Figures

Figure 1

Open AccessArticle
Optimal Model for Carsharing Station Location Based on Multi-Factor Constraints
Algorithms 2020, 13(2), 43; https://doi.org/10.3390/a13020043 - 18 Feb 2020
Abstract
The development of the sharing economy has made carsharing the main future development model of car rental. Carsharing network investment is enormous, but the resource allocation is limited. Therefore, the reasonable location of the carsharing station is important to the development of carsharing [...] Read more.
The development of the sharing economy has made carsharing the main future development model of car rental. Carsharing network investment is enormous, but the resource allocation is limited. Therefore, the reasonable location of the carsharing station is important to the development of carsharing companies. On the basis of the current status of carsharing development, this research considers multiple influencing factors of carsharing to meet the maximum user demand. Meanwhile, the constraint of the limited cost of the company is considered to establish a nonlinear integer programming model for station location of carsharing. A genetic algorithm is designed to solve the problem by analyzing the location model of the carsharing network. Finally, the results of a case study of Lanzhou, China show the effectiveness of the establishment and solution of the station location model. Full article
(This article belongs to the Special Issue Algorithms for Smart Cities)
Show Figures

Figure 1

Back to TopTop