Submit to Algorithms Review for Algorithms Propose a Special Issue

Journal Menu

Journal Browser

Algorithms for Smart Cities

Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Related Special Issue
Published Papers

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

Deadline for manuscript submissions: closed (20 May 2024) | Viewed by 31497

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special issue Algorithms for Smart Cities book cover image

Share This Special Issue

Special Issue Editors

Dr. Gloria Cerasela Crişan

E-Mail Website
Guest Editor

Department of Mathematics and Informatics, Faculty of Sciences, Vasile Alecsandri University of Bacău, 600115 Bacău, Romania
Interests: artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Elena Nechita

E-Mail Website
Guest Editor

Department of Mathematics and Informatics, Faculty of Sciences, Vasile Alecsandri University of Bacău, 600115 Bacău, Romania
Interests: artificial intelligence; probability theory; education
Special Issues, Collections and Topics in MDPI journals

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intelligence, city logistics, internet of things, data analytics, etc., are invited to submit their original and unpublished works to this Special Issue.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 Crişan
Prof. Dr. Elena Nechita
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. 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 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

artificial intelligence
city logistics
data analytics
e-governance
e-health
image recognition
internet of things
optimization methods
recommender systems
remote sensing
transportation networks

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Related Special Issue

Algorithms for Smart Cities (2nd Edition) in Algorithms (3 articles)

Published Papers (10 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

18 pages, 910 KiB

Open AccessArticle

Equity in Transportation Asset Management: A Proposed Framework

by Sara Arezoumand and Omar Smadi

Algorithms 2024, 17(7), 305; https://doi.org/10.3390/a17070305 - 9 Jul 2024

Viewed by 853

Abstract

Transportation asset management has historically overlooked equity considerations. However, recently, there has been a significant increase in concerns about this issue, leading to a range of research and practices aimed at achieving more equitable outcomes. Yet, addressing equity is challenging and time-consuming, given its complexity and multifaceted nature. Several factors can significantly impact the outcome of an analysis, including the definition of equity, the evaluation and quantification of its impacts, and the community classification. As a result, there can be a wide range of interpretations of what constitutes equity. Therefore, there is no single correct or incorrect approach for equity evaluation, and different perspectives, impacts, and analysis methods could be considered for this purpose. This study reviews previous research on how transportation agencies are integrating equity into transportation asset management, particularly pavement management systems. The primary objective is to investigate important equity factors for pavement management and propose a prototype framework that integrates economic, environmental, and social equity considerations into the decision-making process for pavement maintenance, rehabilitation, and reconstruction projects. The proposed framework consists of two main steps: (1) defining objectives based on the three equity dimensions, and (2) analyzing key factors for identifying underserved areas through a case study approach. The case study analyzed pavement condition and sociodemographic data for California’s Bay Area. Statistical analysis and a machine learning method revealed that areas with higher poverty rates and worse air quality tend to have poorer pavement conditions, highlighting the need to consider these factors when defining underserved areas in Bay Area and promoting equity in pavement management decision-making. The proposed framework incorporates an optimization problem to simultaneously minimize disparities in pavement conditions between underserved and other areas, reduce greenhouse gas emissions from construction and traffic disruptions, and maximize overall network pavement condition subject to budget constraints. By incorporating all three equity aspects into a quantitative decision-support framework with specific objectives, this study proposes a novel approach for transportation agencies to promote sustainable and equitable asset management practices. Full article

(This article belongs to the Special Issue Algorithms for Smart Cities)

► Show Figures

Figure 1

26 pages, 2046 KiB

Open AccessArticle

Encouraging Eco-Innovative Urban Development

by Victor Alves, Florentino Fdez-Riverola, Jorge Ribeiro, José Neves and Henrique Vicente

Algorithms 2024, 17(5), 192; https://doi.org/10.3390/a17050192 - 1 May 2024

Viewed by 1562

Abstract

This article explores the intertwining connections among artificial intelligence, machine learning, digital transformation, and computational sustainability, detailing how these elements jointly empower citizens within a smart city framework. As technological advancement accelerates, smart cities harness these innovations to improve residents’ quality of life. Artificial intelligence and machine learning act as data analysis powerhouses, making urban living more personalized, efficient, and automated, and are pivotal in managing complex urban infrastructures, anticipating societal requirements, and averting potential crises. Digital transformation transforms city operations by weaving digital technology into every facet of urban life, enhancing value delivery to citizens. Computational sustainability, a fundamental goal for smart cities, harnesses artificial intelligence, machine learning, and digital resources to forge more environmentally responsible cities, minimize ecological impact, and nurture sustainable development. The synergy of these technologies empowers residents to make well-informed choices, actively engage in their communities, and adopt sustainable lifestyles. This discussion illuminates the mechanisms and implications of these interconnections for future urban existence, ultimately focusing on empowering citizens in smart cities. Full article

(This article belongs to the Special Issue Algorithms for Smart Cities)

► Show Figures

Figure 1

17 pages, 458 KiB

Open AccessArticle

Predicting the Gap in the Day-Ahead and Real-Time Market Prices Leveraging Exogenous Weather Data

by Nika Nizharadze, Arash Farokhi Soofi and Saeed Manshadi

Algorithms 2023, 16(11), 508; https://doi.org/10.3390/a16110508 - 4 Nov 2023

Cited by 1 | Viewed by 1795

Abstract

Predicting the price gap between the day-ahead Market (DAM) and the real-time Market (RTM) plays a vital role in the convergence bidding mechanism of Independent System Operators (ISOs) in wholesale electricity markets. This paper presents a model to predict the values of the price gap between the DAM and RTM using statistical machine learning algorithms and deep neural networks. In this paper, we seek to answer these questions: What will be the impact of predicting the DAM and RTM price gap directly on the prediction performance of learning methods? How can exogenous weather data affect the price gap prediction? In this paper, several exogenous features are collected, and the impacts of these features are examined to capture the best relations between the features and the target variable. An ensemble learning algorithm, namely the Random Forest (RF), is used to select the most important features. A Long Short-Term Memory (LSTM) network is used to capture long-term dependencies in predicting direct gap values between the markets stated. Moreover, the advantages of directly predicting the gap price rather than subtracting the price predictions of the DAM and RTM are shown. The presented results are based on the California Independent System Operator (CAISO)’s electricity market data for two years. The results show that direct gap prediction using exogenous weather features decreases the error of learning methods by

46 %

. Therefore, the presented method mitigates the prediction error of the price gap between the DAM and RTM. Thus, the convergence bidders can increase their profit, and the ISOs can tune their mechanism accordingly. Full article

(This article belongs to the Special Issue Algorithms for Smart Cities)

► Show Figures

Figure 1

19 pages, 14760 KiB

Open AccessArticle

A Plant Disease Classification Algorithm Based on Attention MobileNet V2

by Huan Wang, Shi Qiu, Huping Ye and Xiaohan Liao

Algorithms 2023, 16(9), 442; https://doi.org/10.3390/a16090442 - 13 Sep 2023

Cited by 8 | Viewed by 2542

Abstract

Plant growth is inevitably affected by diseases, and one effective method of disease detection is through the observation of leaf changes. To solve the problem of disease detection in complex backgrounds, where the distinction between plant diseases is hindered by large intra-class differences and small inter-class differences, a complete plant-disease recognition process is proposed. The process was tested through experiments and research into traditional and deep features. In the face of difficulties related to plant-disease classification in complex backgrounds, the advantages of strong interpretability of traditional features and great robustness of deep features are fully utilized, and include the following components: (1) The OSTU algorithm based on the naive Bayes model is proposed to focus on where leaves are located and remove interference from complex backgrounds. (2) A multi-dimensional feature model is introduced in an interpretable manner from the perspective of traditional features to obtain leaf characteristics. (3) A MobileNet V2 network with a dual attention mechanism is proposed to establish a model that operates in both spatial and channel dimensions at the network level to facilitate plant-disease recognition. In the Plant Village open database test, the results demonstrated an average SEN of 94%, greater than other algorithms by 12.6%. Full article

(This article belongs to the Special Issue Algorithms for Smart Cities)

► Show Figures

Figure 1

17 pages, 2589 KiB

Open AccessArticle

Learning from Imbalanced Datasets: The Bike-Sharing Inventory Problem Using Sparse Information

by Giovanni Ceccarelli, Guido Cantelmo, Marialisa Nigro and Constantinos Antoniou

Algorithms 2023, 16(7), 351; https://doi.org/10.3390/a16070351 - 22 Jul 2023

Viewed by 1528

Abstract

In bike-sharing systems, the inventory level is defined as the daily number of bicycles required to optimally meet the demand. Estimating these values is a major challenge for bike-sharing operators, as biased inventory levels lead to a reduced quality of service at best and a loss of customers and system failure at worst. This paper focuses on using machine learning (ML) classifiers, most notably random forest and gradient tree boosting, for estimating the inventory level from available features including historical data. However, while similar approaches adopted in the context of bike sharing assume the data to be well-balanced, this assumption is not met in the case of the inventory problem. Indeed, as the demand for bike sharing is sparse, datasets become biased toward low demand values, and systematic errors emerge. Thus, we propose to include a new iterative resampling procedure in the classification problem to deal with imbalanced datasets. The proposed model, tested on the real-world data of the Citi Bike operator in New York, allows to (i) provide upper-bound and lower-bound values for the bike-sharing inventory problem, accurately predicting both predominant and rare demand values; (ii) capture the main features that characterize the different demand classes; and (iii) work in a day-to-day framework. Finally, successful bike-sharing systems grow rapidly, opening new stations every year. In addition to changes in the mobility demand, an additional problem is that we cannot use historical information to predict inventory levels for new stations. Therefore, we test the capability of our model to predict inventory levels when historical data is not available, with a specific focus on stations that were not available for training. Full article

(This article belongs to the Special Issue Algorithms for Smart Cities)

► Show Figures

Figure 1

21 pages, 515 KiB

Open AccessArticle

Hierarchical Modelling for CO₂ Variation Prediction for HVAC System Operation

by Ibrahim Shaer and Abdallah Shami

Algorithms 2023, 16(5), 256; https://doi.org/10.3390/a16050256 - 17 May 2023

Cited by 10 | Viewed by 2418

Abstract

Residential and industrial buildings are significant consumers of energy, which can be reduced by controlling their respective Heating, Ventilation, and Air Conditioning (HVAC) systems. Demand-based Ventilation (DCV) determines the operational times of ventilation systems that depend on indoor air quality (IAQ) conditions, including CO₂ concentration changes, and the occupants’ comfort requirements. The prediction of CO₂ concentration changes can act as a proxy estimator of occupancy changes and provide feedback about the utility of current ventilation controls. This paper proposes a Hierarchical Model for CO₂ Variation Predictions (HMCOVP) to accurately predict these variations. The proposed framework addresses two concerns in state-of-the-art implementations. First, the hierarchical structure enables fine-tuning of the produced models, facilitating their transferability to different spatial settings. Second, the formulation incorporates time dependencies, defining the relationship between different IAQ factors. Toward that goal, the HMCOVP decouples the variation prediction into two complementary steps. The first step transforms lagged versions of environmental features into image representations to predict the variations’ direction. The second step combines the first step’s result with environment-specific historical data to predict CO₂ variations. Through the HMCOVP, these predictions, which outperformed state-of-the-art approaches, help the ventilation systems in their decision-making processes, reducing energy consumption and carbon-based emissions. Full article

(This article belongs to the Special Issue Algorithms for Smart Cities)

► Show Figures

Figure 1

24 pages, 8360 KiB

Open AccessArticle

An IoT System for Social Distancing and Emergency Management in Smart Cities Using Multi-Sensor Data

by Rosario Fedele and Massimo Merenda

Algorithms 2020, 13(10), 254; https://doi.org/10.3390/a13100254 - 7 Oct 2020

Cited by 36 | Viewed by 5859

Abstract

Smart cities need technologies that can be really applied to raise the quality of life and environment. Among all the possible solutions, Internet of Things (IoT)-based Wireless Sensor Networks (WSNs) have the potentialities to satisfy multiple needs, such as offering real-time plans for emergency management (due to accidental events or inadequate asset maintenance) and managing crowds and their spatiotemporal distribution in highly populated areas (e.g., cities or parks) to face biological risks (e.g., from a virus) by using strategies such as social distancing and movement restrictions. Consequently, the objective of this study is to present an IoT system, based on an IoT-WSN and on algorithms (Neural Network, NN, and Shortest Path Finding) that are able to recognize alarms, available exits, assembly points, safest and shortest paths, and overcrowding from real-time data gathered by sensors and cameras exploiting computer vision. Subsequently, this information is sent to mobile devices using a web platform and the Near Field Communication (NFC) technology. The results refer to two different case studies (i.e., emergency and monitoring) and show that the system is able to provide customized strategies and to face different situations, and that this is also applies in the case of a connectivity shutdown. Full article

(This article belongs to the Special Issue Algorithms for Smart Cities)

► Show Figures

Figure 1

22 pages, 6084 KiB

Open AccessArticle

Automobile Fine-Grained Detection Algorithm Based on Multi-Improved YOLOv3 in Smart Streetlights

by Fan Yang, Deming Yang, Zhiming He, Yuanhua Fu and Kui Jiang

Algorithms 2020, 13(5), 114; https://doi.org/10.3390/a13050114 - 2 May 2020

Cited by 7 | Viewed by 3963

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 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

18 pages, 4372 KiB

Open AccessArticle

Multi-Level Joint Feature Learning for Person Re-Identification

by Shaojun Wu and Ling Gao

Algorithms 2020, 13(5), 111; https://doi.org/10.3390/a13050111 - 29 Apr 2020

Cited by 6 | Viewed by 4295

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 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

17 pages, 4396 KiB

Open AccessArticle

Optimal Model for Carsharing Station Location Based on Multi-Factor Constraints

by Qiuyue Sai, Jun Bi and Jinxian Chai

Algorithms 2020, 13(2), 43; https://doi.org/10.3390/a13020043 - 18 Feb 2020

Cited by 10 | Viewed by 4182

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 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

Journal Menu

Journal Browser

Algorithms for Smart Cities

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Related Special Issue

Published Papers (10 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI