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Smart Cities and Smart Traffic: Sensors, IoT, and Intelligence
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Smart Cities and Smart Traffic: Sensors, IoT, and Intelligence

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 45294

Special Issue Editors

Prof. Dr. Enrique Alba

E-Mail Website
Guest Editor
ITIS Software, Universidad de Málaga, E.T.S.I. Informática (3-2-12), B. Louis Pasteur s/n, 29071 Málaga, Spain
Interests: smart cities; holistic vision of city applications; smart sensors and actuators; software quality for smart city; intelligent systems; security by design; complex optimization; parallel computing systems; smart mobility; prediction; machine learning; data science; artificial intelligence; big data; app and web services for complex services; metaheuristics; bioinspired techniques; multiobjective; dynamic algorithms; decentralized techniques; parallel algorithms; HPC; hybrid algorithms; theory; synergies between theory and practice
Special Issues, Collections and Topics in MDPI journals
Dr. Zakaria Abdelmoiz Dahi

E-Mail Website
Guest Editor
Associate Professor and Researcher at the Department of Fundamental Computer Science and Its Applications, Faculty of New Technologies of Information and Communication, Constantine 2 University, 67-A nouvelle ville Ali Mendjeli, Constantine city 25000, Algeria
Interests: real applications; artificial intelligence; quantum computing; soft computing; optimization problem solving

Special Issue Information

Dear Colleagues,

Today’s global evolution has made it possible to return technology to its original purpose, which is serving in the best way possible human beings, especially in the currently most widespread societal structure: the city. High-performance and cutting-edge technologies are literally everywhere and intensively used to assist and ease each aspect of the citizen’s life, thus giving birth to one of today’s most challenging, promising, and cross-disciplinary research topics: the smart city.

Smart cities are strongly related to and correlated with the profile of their citizens (e.g., behavior, need, preferences, etc.). Regardless of the application purpose (e.g., traffic regulation, economy, energy, health, etc.), a key component of smart cities is gathering inhabitants’ data via sensors of many types and their integration into all kinds of devices (e.g., cars, mobile phones, buildings, clothes, etc.). Data and their intelligent management are the backbone supporting the smart city concept and its realization.

In this Special Issue, we welcome applications focusing on data acquisition, use, and service delivery for the materialization of the smart city concept with an emphasis on the smart traffic axis (e.g., jam avoidance, safety, pollution reduction, city organization, etc.). We encourage works making an original and out-of-the-box use of deep learning, artificial intelligence, city microsimulation, bio-inspired algorithms, special hardware, etc.

We welcome both research and review article submissions. If you are willing to contribute to this Special Issue, we would very much appreciate receiving via email the tentative title and abstract of your contribution.

Prof. Dr. Enrique Alba
Dr. Zakaria Abdelmoiz Dahi
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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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.

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

Published Papers (4 papers)

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Research

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20 pages, 6326 KiB  
Article
A Machine Learning Framework for Balancing Training Sets of Sensor Sequential Data Streams
by Budi Darma Setiawan, Uwe Serdült and Victor Kryssanov
Sensors 2021, 21(20), 6892; https://doi.org/10.3390/s21206892 - 18 Oct 2021
Cited by 9 | Viewed by 3283
Abstract
The recent explosive growth in the number of smart technologies relying on data collected from sensors and processed with machine learning classifiers made the training data imbalance problem more visible than ever before. Class-imbalanced sets used to train models of various events of [...] Read more.
The recent explosive growth in the number of smart technologies relying on data collected from sensors and processed with machine learning classifiers made the training data imbalance problem more visible than ever before. Class-imbalanced sets used to train models of various events of interest are among the main reasons for a smart technology to work incorrectly or even to completely fail. This paper presents an attempt to resolve the imbalance problem in sensor sequential (time-series) data through training data augmentation. An Unrolled Generative Adversarial Networks (Unrolled GAN)-powered framework is developed and successfully used to balance the training data of smartphone accelerometer and gyroscope sensors in different contexts of road surface monitoring. Experiments with other sensor data from an open data collection are also conducted. It is demonstrated that the proposed approach allows for improving the classification performance in the case of heavily imbalanced data (the F1 score increased from 0.69 to 0.72, p<0.01, in the presented case study). However, the effect is negligible in the case of slightly imbalanced or inadequate training sets. The latter determines the limitations of this study that would be resolved in future work aimed at incorporating mechanisms for assessing the training data quality into the proposed framework and improving its computational efficiency. Full article
(This article belongs to the Special Issue Smart Cities and Smart Traffic: Sensors, IoT, and Intelligence)
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34 pages, 4837 KiB  
Article
Towards Smart City Governance. Case Study: Improving the Interpretation of Quantitative Traffic Measurement Data through Citizen Participation
by David Fonseca, Monica Sanchez-Sepulveda, Silvia Necchi and Enric Peña
Sensors 2021, 21(16), 5321; https://doi.org/10.3390/s21165321 - 6 Aug 2021
Cited by 14 | Viewed by 5844
Abstract
Citizens play a core role in sustainable cities as users of the services delivered by cities and as active participants in initiatives aimed at making cities more sustainable. This paper considers the role of citizens as information providers and discusses the conditions under [...] Read more.
Citizens play a core role in sustainable cities as users of the services delivered by cities and as active participants in initiatives aimed at making cities more sustainable. This paper considers the role of citizens as information providers and discusses the conditions under which citizens can participate in the development of sustainable cities. The objective of this study is to document the sustainability of an urban transit system and evaluate its compliance, with citizen participation as a major contributor. The methodology used is intensive field visits, interviews, and a mixed analysis of Sant Andreu de Palomar District in Barcelona city. The circulating vehicles are quantitatively monitored, qualitative problems are detected, and the typology of vehicles and other aspects identified and detailed in the study are indicated. All this information is contrasted with that of the technological sensors in the sectors. The results indicate that vehicles in the current pattern of urban density planned under incorrect sensor operation influence sustainable behavior through agglomerative clustering. This paper provides recommendations for future urban sustainability assessment research, including the employment of mixed-methods research, among other strategies. This article is intended to assist policymakers and traffic engineers in evaluating the sustainability of urban transportation infrastructure projects considering citizens as sensors. Full article
(This article belongs to the Special Issue Smart Cities and Smart Traffic: Sensors, IoT, and Intelligence)
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12 pages, 7043 KiB  
Communication
How to Improve Urban Intelligent Traffic? A Case Study Using Traffic Signal Timing Optimization Model Based on Swarm Intelligence Algorithm
by Xiancheng Fu, Hengqiang Gao, Hongjuan Cai, Zhihao Wang and Weiming Chen
Sensors 2021, 21(8), 2631; https://doi.org/10.3390/s21082631 - 8 Apr 2021
Cited by 14 | Viewed by 3867
Abstract
Traffic congestion is a major problem in today’s society, and the intersection, as an important hub of urban traffic, is one of the most common places to produce traffic congestion. To alleviate the phenomenon of congestion at urban traffic intersections and relieve the [...] Read more.
Traffic congestion is a major problem in today’s society, and the intersection, as an important hub of urban traffic, is one of the most common places to produce traffic congestion. To alleviate the phenomenon of congestion at urban traffic intersections and relieve the traffic pressure at intersections, this paper takes the traffic flow at intersections as the research object and adopts the swarm intelligent algorithm to establish an optimization model of intersection traffic signal timing, which takes the average delay time of vehicles, the average number of stops of vehicles and the traffic capacity as the evaluation indexes. This model adjusts the intersection traffic signal timing intelligence according to the real-time traffic flow and carries out simulation experiments with MATLAB. Compared with the traditional timing schemes, the average delay time of vehicles is reduced by 10.25%, the average number of stops of vehicles is reduced by 24.55%, and the total traffic capacity of the intersection is increased by 3.56%, which verifies that the scheme proposed in this paper is effective in relieving traffic congestion. Full article
(This article belongs to the Special Issue Smart Cities and Smart Traffic: Sensors, IoT, and Intelligence)
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Review

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41 pages, 3879 KiB  
Review
Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges
by Sara Paiva, Mohd Abdul Ahad, Gautami Tripathi, Noushaba Feroz and Gabriella Casalino
Sensors 2021, 21(6), 2143; https://doi.org/10.3390/s21062143 - 18 Mar 2021
Cited by 211 | Viewed by 29998
Abstract
The increasing population across the globe makes it essential to link smart and sustainable city planning with the logistics of transporting people and goods, which will significantly contribute to how societies will face mobility in the coming years. The concept of smart mobility [...] Read more.
The increasing population across the globe makes it essential to link smart and sustainable city planning with the logistics of transporting people and goods, which will significantly contribute to how societies will face mobility in the coming years. The concept of smart mobility emerged with the popularity of smart cities and is aligned with the sustainable development goals defined by the United Nations. A reduction in traffic congestion and new route optimizations with reduced ecological footprint are some of the essential factors of smart mobility; however, other aspects must also be taken into account, such as the promotion of active mobility and inclusive mobility, encouraging the use of other types of environmentally friendly fuels and engagement with citizens. The Internet of Things (IoT), Artificial Intelligence (AI), Blockchain and Big Data technology will serve as the main entry points and fundamental pillars to promote the rise of new innovative solutions that will change the current paradigm for cities and their citizens. Mobility-as-a-service, traffic flow optimization, the optimization of logistics and autonomous vehicles are some of the services and applications that will encompass several changes in the coming years with the transition of existing cities into smart cities. This paper provides an extensive review of the current trends and solutions presented in the scope of smart mobility and enabling technologies that support it. An overview of how smart mobility fits into smart cities is provided by characterizing its main attributes and the key benefits of using smart mobility in a smart city ecosystem. Further, this paper highlights other various opportunities and challenges related to smart mobility. Lastly, the major services and applications that are expected to arise in the coming years within smart mobility are explored with the prospective future trends and scope. Full article
(This article belongs to the Special Issue Smart Cities and Smart Traffic: Sensors, IoT, and Intelligence)
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