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Big Data and Smart Cities
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Big Data and Smart Cities

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "G: Energy and Buildings".

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 20994

Special Issue Editors

Prof. Dr. Reza Arghandeh

E-Mail Website
Guest Editor
Department of Computing, Mathematics and Physics, Western Norway University of Applied Sciences, Bergen, Norway
Interests: data analytics for smart grid and smart city applications
Special Issues, Collections and Topics in MDPI journals
Dr. Eren Erman Ozguven

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Resilient Infrastructure and Disaster Response (RIDER) Center, FAMU-FSU College of Engineering University, Tallahassee, FL, USA
Interests: modeling of emergency evacuation operations; emergency inventory management; simulation and modeling of transportation networks; traffic safety and accessibility; multi-modal transportation; intelligent transportation systems; smart cities and urban mobility
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart cities need to manage resources safely, sustainably, and efficiently to achieve better socioeconomic outcomes. However, the connectivity between citizens, systems, and things in the cities creates tremendous complexities when it comes to decision-making. The massive sociotechnical data from various recourses in cities can be a valuable asset to face the challenges in the operation and planning of smart cities. However, the urban data volume, heterogeneity, and spatiotemporal characteristics create difficulties in data management, fusion, analysis, and inference. Above all, a city is a conglomerate of multiple networks, such as electricity, transportation, information, water, gas, etc., which have different requirements for data handling, privacy, and analytics.

The recent advents in data mining, data archiving, machine learning, statistical inference, information theory, visualization, and computing create a new paradigm for the data-driven management of smart cities. Moreover, different levels of municipal, local, and regional government structures make interoperability and coordination a hassle for smart cities’ integrated management systems. To face the organizational challenges in smart cities, the data analysis methods need to be evolved and more attention is needed from experts in industry and academia to fill the gap.

The main goal of this Special Issue is to bring together scholars, researchers, scientists, engineers, and administrators on a common platform to develop, design, and publish new ideas and concepts to improve the field of big data and internet of things (IoT) in smart cities. The relevant topics include, but are not limited to the following:

  • Sensor networks and data mining for smart city applications;
  • Data analysis and machine learning for smart city applications;
  • Decision support systems for smart city applications;
  • Urban infrastructure networks operation and planning;
  • Urban mobility modeling and analysis in the smart city paradigm;
  • Electricity and roadway networks integration in the smart city paradigm;
  • Interoperability between different systems and domains in the city;
  • Interdependencies of energy systems and information technology in cities.

Prof. Dr. Reza Arghandeh
Assoc. Prof. Eren Erman Ozguven
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. Energies 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.

Keywords

  • Smart city
  • Smart grid
  • Monitoring systems
  • Sensor networks
  • Data mining
  • Data analysis
  • Machine learning
  • Aritifitula intelligence
  • Forecasting
  • Decision support systems
  • Multinetwork systems
  • Urban mobility
  • Electricity networks
  • Transportation networks
  • Cyberphysical systems
  • Internet of things
  • Interoperability

Published Papers (6 papers)

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Research

33 pages, 6995 KiB  
Article
The Analysis of the Factors Determining the Choice of Park and Ride Facility Using a Multinomial Logit Model
by Elżbieta Macioszek and Agata Kurek
Energies 2021, 14(1), 203; https://doi.org/10.3390/en14010203 - 02 Jan 2021
Cited by 29 | Viewed by 2874
Abstract
The basic function of the Park and Ride (P&R) facility is to allow users to leave their vehicle on the outskirts of the city and to continue their journey to the city center using means of public transport, e.g., bus, tram, trolleybus, subway, [...] Read more.
The basic function of the Park and Ride (P&R) facility is to allow users to leave their vehicle on the outskirts of the city and to continue their journey to the city center using means of public transport, e.g., bus, tram, trolleybus, subway, train, or bike. In the first part of the paper, an analysis of the selected factors related to the functioning of P&R facilities in Warsaw (Poland) was performed. The main purpose of this paper was to identify and quantify the influence factors determining the choice of P&R facility during a journey. This analysis was performed for three hypothetical journey scenarios. A list of potential factors determining the choice of P&R facility during travel was compiled after conducting previous research in this area and studying the worldwide scientific literature on the subject. The structural parameters of the multinomial logit model were estimated based on the data from the survey conducted in Warsaw. The results of the analyses indicate that the decision to choose a hypothetical journey scenario depends on many factors, but primarily on the level of education, the number of years of having a driving license, age, the number of kilometers traveled during the year, and the performed activity. Full article
(This article belongs to the Special Issue Big Data and Smart Cities)
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17 pages, 4927 KiB  
Article
Analyzing COVID-19 Impacts on Vehicle Travels and Daily Nitrogen Dioxide (NO2) Levels among Florida Counties
by Alican Karaer, Nozhan Balafkan, Michele Gazzea, Reza Arghandeh and Eren Erman Ozguven
Energies 2020, 13(22), 6044; https://doi.org/10.3390/en13226044 - 19 Nov 2020
Cited by 16 | Viewed by 3689
Abstract
The COVID-19 outbreak and ensuing social distancing behaviors resulted in substantial reduction on traffic, making this a unique experiment on observing the air quality. Such an experiment is also supplemental to the smart city concept as it can help to identify whether there [...] Read more.
The COVID-19 outbreak and ensuing social distancing behaviors resulted in substantial reduction on traffic, making this a unique experiment on observing the air quality. Such an experiment is also supplemental to the smart city concept as it can help to identify whether there is a delay on air quality improvement during or after a sharp decline on traffic and to determine what, if any, factors are contributing to that time lag. As such, this study investigates the immediate impacts of COVID-19 causing abrupt declines on traffic and NO2 concentration in all Florida Counties through March 2020. Daily tropospheric NO2 concentrations were extracted from the Sentinel-5 Precursor satellite and vehicle mile traveled (VMT) estimates were acquired from cell phone mobility records. It is observed that overall impacts of the COVID-19 response in Florida have started in the first half of the March 2020, two weeks earlier than the official stay-at-home orders, and resulted in 54.07% and 59.68% decrease by the end of the month on NO2 and VMT, respectively. Further, a cross-correlation based dependency analysis was conducted to analyze the similarities and associated time lag between 7-day moving averages of VMT and NO2 concentrations of the 67 counties. Although such reduction is unprecedented for both data sets, results indicate a strong correlation and this correlation increases with the identification of a time lag between VMT and NO2 concentration. Majority of the counties have no time lag between VMT and NO2 concentration; however, a cluster of South Florida counties presents earlier decrease on NO2 concentration compare to VMT, which indicates that the air quality improvements in those counties are not traffic related. Investigation on the socioeconomic factors indicates that population density and income level have no significant impact on the time lag between traffic and air quality improvements in light of COVID-19. Full article
(This article belongs to the Special Issue Big Data and Smart Cities)
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26 pages, 10567 KiB  
Article
The Use of a Park and Ride System—A Case Study Based on the City of Cracow (Poland)
by Elżbieta Macioszek and Agata Kurek
Energies 2020, 13(13), 3473; https://doi.org/10.3390/en13133473 - 05 Jul 2020
Cited by 36 | Viewed by 4989
Abstract
The park and ride (P&R) parking type is usually located near peripheral public transport stops. These parking places are dedicated to people who, after leaving their vehicle in the parking, continue their journey to the city center using some form of public transport [...] Read more.
The park and ride (P&R) parking type is usually located near peripheral public transport stops. These parking places are dedicated to people who, after leaving their vehicle in the parking, continue their journey to the city center using some form of public transport such as bus, metro, rail or tram systems. This article aims to examine the features associated with P&R parking locations in use in Cracow (Poland). The analysis included the number of entries and exits to and from parking during particular periods of the day, week and year, parking time of vehicles, and parking space use. A parking peak hour factor was also calculated, which expresses the crowding degree of vehicle entries/exits in/out parking during a particular period. In addition, the paper presents an analysis of factors determining users to P&R parking use. In the modeling process, logit models were used, which, as stated after analyzing the literature on the subject, were already used in various countries around the world to describe the behavior of P&R parking users. However, so far, such research relating to Polish conditions has been not published in the available literature. The obtained results allowed to state that the most important factors determining the likelihood of using P&R parking in Cracow are age, number of years having a driving license, monthly income (gross), and an average number of trips made during a day. Other variables, which not included in the study, can influence the P&R parking use. However, the presented results are the basis for conducting furtherer, more in-depth analyses based on a larger number of independent variables that may determine the P&R parking use. Full article
(This article belongs to the Special Issue Big Data and Smart Cities)
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23 pages, 5464 KiB  
Article
Passenger Travel Patterns and Behavior Analysis of Long-Term Staying in Subway System by Massive Smart Card Data
by Gang Xue, Daqing Gong, Jianhai Zhang, Peng Zhang and Qimin Tai
Energies 2020, 13(10), 2670; https://doi.org/10.3390/en13102670 - 25 May 2020
Cited by 5 | Viewed by 2696
Abstract
Due to the massive congestion in ground transportation in Beijing, underground rail transit has gradually become the main mode of travel for residents of large urban areas. Because the average daily traffic of the Beijing subway is over 12 million passengers, ensuring the [...] Read more.
Due to the massive congestion in ground transportation in Beijing, underground rail transit has gradually become the main mode of travel for residents of large urban areas. Because the average daily traffic of the Beijing subway is over 12 million passengers, ensuring the safety of underground rail transit is particularly important. Big data shows that more than 4000 passengers participate in Long-term Stay in the Subway every day. However, the behaviors of these passengers have not been characterized. This paper proposes a method for identifying the Long-term Staying in Subway System (LSSS) in the subway based on the shortest path and analyze its travel mode. In combination with the past research of scholars, we try to quantify the suspected behavior with a database of assumed suspected behavior records. Finally, we extract the spatial-temporal travel characteristics of passengers and we propose a SAE-DNN algorithm to identify suspected anomalies; the accuracy of the training set can reach 95.7%, and the accuracy of the test set can also reach 93.5%, which provides a reference for the subway operators and the public security system. Full article
(This article belongs to the Special Issue Big Data and Smart Cities)
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15 pages, 595 KiB  
Article
The Road toward Smart Cities: A Study of Citizens’ Acceptance of Mobile Applications for City Services
by Jinghui (Jove) Hou, Laura Arpan, Yijie Wu, Richard Feiock, Eren Ozguven and Reza Arghandeh
Energies 2020, 13(10), 2496; https://doi.org/10.3390/en13102496 - 15 May 2020
Cited by 24 | Viewed by 3460
Abstract
Many local governments have started using smartphone applications to more effectively inform and communicate with citizens. This trend is of interest, as cities can only be smart if they are responsive to their citizens. In this paper, the intention to use such a [...] Read more.
Many local governments have started using smartphone applications to more effectively inform and communicate with citizens. This trend is of interest, as cities can only be smart if they are responsive to their citizens. In this paper, the intention to use such a mobile application among adult residents (n = 420) of a mid-sized city in the southeastern United States was examined using hierarchical linear regression analysis. The regression model that was tested indicated significant predictors of the intention to use the app in order to report municipal problems, such as power outages, and to request services for one’s home or community, including: Performance expectancy (e.g., citizens’ beliefs that the app would be efficient, helpful, convenient), effort expectancy (citizens’ beliefs about difficulty of using the app), social influence, perceived cost (e.g., privacy loss, storage space, unwanted notifications), and prior use of city apps. Consistent with current research on technology adoption, performance expectancy had the strongest influence on app-use intentions. Additionally, citizens’ trust in their city government’s ability to effectively manage an app was a weak, positive predictor of app-use intentions; general trust in the city government did not predict app-use intentions. Implications for city governments and city app developers are discussed. Full article
(This article belongs to the Special Issue Big Data and Smart Cities)
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28 pages, 5983 KiB  
Article
Development of Algorithms for Effective Resource Allocation among Highway–Rail Grade Crossings: A Case Study for the State of Florida
by Masoud Kavoosi, Maxim A. Dulebenets, Junayed Pasha, Olumide F. Abioye, Ren Moses, John Sobanjo and Eren E. Ozguven
Energies 2020, 13(6), 1419; https://doi.org/10.3390/en13061419 - 18 Mar 2020
Cited by 13 | Viewed by 2055
Abstract
Smart cities directly rely on a variety of elements, including water, gas, electricity, buildings, services, transportation networks, and others. Lack of properly designed transportation networks may cause different economic and safety concerns. Highway–rail grade crossings are known to be a hazardous point in [...] Read more.
Smart cities directly rely on a variety of elements, including water, gas, electricity, buildings, services, transportation networks, and others. Lack of properly designed transportation networks may cause different economic and safety concerns. Highway–rail grade crossings are known to be a hazardous point in the transportation network, considering a remarkable number of accidents recorded annually between highway users and trains, and even solely between highway users at highway–rail grade crossings. Hence, safety improvement at highway–rail grade crossings is a challenging issue for smart city authorities, given limitations in monetary resources. In this study, two optimization models are developed for resource allocation among highway–rail grade crossings to minimize the overall hazard and the overall hazard severity, taking into account the available budget limitations. The optimization models are solved by CPLEX to the global optimality. Moreover, some heuristic algorithms are proposed as well. A case study focusing on the public highway–rail grade crossings in the State of Florida is performed to evaluate the effectiveness of the developed optimization models and the solution methodologies. In terms of the computational time, all the solution approaches are found to be effective decision support tools from the practical standpoint. Moreover, the results demonstrate that some of the developed heuristic algorithms can provide near-optimal solutions. Therefore, the smart city authorities can utilize the proposed heuristics as decision support tools for effective resource allocation among highway–rail grade crossings. Full article
(This article belongs to the Special Issue Big Data and Smart Cities)
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