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Data-Driven Approach on Urban Planning and Smart Cities
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Data-Driven Approach on Urban Planning and Smart Cities

A special issue of Data (ISSN 2306-5729). This special issue belongs to the section "Spatial Data Science and Digital Earth".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 13233

Special Issue Editor

Dr. Giuseppe Ciaburro

E-Mail Website
Guest Editor
Department of Architecture and Industrial Design, Università degli Studi della Campania "Luigi Vanvitelli", Aversa, Italy
Interests: acoustics; architecture; digital signal processing; sound; audio signal processing; acoustic signal processing; acoustic analysis; acoustics and acoustic engineering; sound analysis; noise analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Current forms of urban planning have encountered considerable difficulties in giving an adequate and sustainable response to the critical issues affecting large contemporary cities. The need for new models to manage urban development is not dictated only by the need to find effective solutions to the criticalities of urban contexts, but also concerns the future role of the built environment in the new concept of smart cities. For these reasons, many cities are called upon to promote initiatives aimed at fostering innovation and increasing their attraction index in tune with the improvement in the quality of life of citizens. The availability of modern sensors for data acquisition, increasingly accessible and at low cost, has provided a new paradigm for urban planning management, providing new tools to extract knowledge and support stakeholders in decision-making processes.

This Special Issue aims to publish articles describing data collection, acquisition, processing, and management for urban planning and smart cities. Submissions on but not limited to the following hot topics are encouraged:

  • Sustainable built environment
  • Built environment monitoring
  • Smart materials for a built environment
  • Urban planning for a sustainable development
  • Smart cities design and case studies
  • Modern sensors for data acquisition
  • Internet of Things and big data analytics
  • Data acquisition methodologies
  • Virtual reality and augmented reality for built environment
  • Artificial Intelligence-based technologies for urban planning
  • Machine learning techniques for data analysis
  • Health and safety for smart cities

Dr. Giuseppe Ciaburro
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 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. Data is an international peer-reviewed open access monthly journal published by MDPI.

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Published Papers (5 papers)

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18 pages, 3814 KiB  
Article
Development of a Machine-Learning-Based Novel Framework for Travel Time Distribution Determination Using Probe Vehicle Data
by Gurmesh Sihag, Praveen Kumar and Manoranjan Parida
Data 2023, 8(3), 60; https://doi.org/10.3390/data8030060 - 14 Mar 2023
Cited by 1 | Viewed by 2092
Abstract
Investigating travel time variability is critical for pre-trip planning, reliable route selection, traffic management, and the development of control strategies to mitigate traffic congestion problems cost-effectively. Hence, a large number of studies are available in the literature which determine the most suitable distribution [...] Read more.
Investigating travel time variability is critical for pre-trip planning, reliable route selection, traffic management, and the development of control strategies to mitigate traffic congestion problems cost-effectively. Hence, a large number of studies are available in the literature which determine the most suitable distribution to fit the travel time data, but these studies recommend different distributions for the travel time data, and there is a disagreement on the best distribution option for fitting to the travel time data. The present study proposes a novel framework to determine the best distribution to represent the travel time data obtained from probe vehicles by using the modern machine learning technique. This study employs vast travel time data collected by fitting GPS tracking units on the probe vehicles and offers a comprehensive investigation of travel time distribution in different scenarios generated due to spatiotemporal variation of the travel time. The study also considers the effect of weather and uses the three most commonly used non-parametric goodness-of-fit tests (namely, Kolmogorov–Smirnov test, Anderson–Darling test, and chi-squared test) to fit and rank a comprehensive set of around 60 unimodal statistical distributions. The framework proposed in the study can determine the travel time distribution with 91% accuracy. Additionally, the distribution determined by the framework has an acceptance rate of 98.4%, which is better than the acceptance rates of the distributions recommended in existing studies. Because of its robustness and applicability in many different traffic situations, the proposed framework can also be used in developing countries with heterogeneous disordered traffic conditions to evaluate the road network’s performance in terms of travel time reliability. Full article
(This article belongs to the Special Issue Data-Driven Approach on Urban Planning and Smart Cities)
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20 pages, 2084 KiB  
Article
Toward a Spatially Segregated Urban Growth? Austerity, Poverty, and the Demographic Decline of Metropolitan Greece
by Kostas Rontos, Enrico Maria Mosconi, Mattia Gianvincenzi, Simona Moretti and Luca Salvati
Data 2023, 8(3), 53; https://doi.org/10.3390/data8030053 - 1 Mar 2023
Cited by 1 | Viewed by 2147
Abstract
Metropolitan decline in southern Europe was documented in few cases, being less intensively investigated than in other regions of the continent. Likely for the first time in recent history, the aftermath of the 2007 recession was a time period associated with economic and [...] Read more.
Metropolitan decline in southern Europe was documented in few cases, being less intensively investigated than in other regions of the continent. Likely for the first time in recent history, the aftermath of the 2007 recession was a time period associated with economic and demographic decline in Mediterranean Europe. However, the impacts and consequences of the great crisis were occasionally verified and quantified, both in strictly urban contexts and in the surrounding rural areas. By exploiting official statistics, our study delineates sequential stages of demographic growth and decline in a large metropolitan region (Athens, Greece) as a response to economic expansion and stagnation. Having important implications for the extent and spatial direction of metropolitan cycles, the Athens’ case—taken as an example of urban cycles in Mediterranean Europe—indicates a possibly new dimension of urban shrinkage, with spatially varying population growth and decline along a geographical gradient of income and wealth. Heterogeneous dynamics led to a leapfrog urban expansion decoupled from agglomeration and scale, the factors most likely shaping long-term metropolitan expansion in advanced economies. Demographic decline in urban contexts was associated with multidimensional socioeconomic processes resulting in spatially complex demographic outcomes that require appropriate, and possibly more specific, regulation policies. By shedding further light on recession-driven metropolitan decline in advanced economies, the present study contributes to re-thinking short-term development mechanisms and medium-term demographic scenarios in Mediterranean Europe. Full article
(This article belongs to the Special Issue Data-Driven Approach on Urban Planning and Smart Cities)
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19 pages, 9596 KiB  
Communication
Basic Input Data for Audiences’ Geotargeting by Destinations’ Partial Accessibility: Notes from Slovakia
by Csaba Sidor, Branislav Kršák and Ľubomír Štrba
Data 2023, 8(2), 24; https://doi.org/10.3390/data8020024 - 19 Jan 2023
Viewed by 1772
Abstract
The presented notes focus partially on two of the basic elements (accessibility and image) of any managed tourism destination from the perspective of basic ETL processes over open and third-party data. The specific case aims to investigate the usability of open government data [...] Read more.
The presented notes focus partially on two of the basic elements (accessibility and image) of any managed tourism destination from the perspective of basic ETL processes over open and third-party data. The specific case aims to investigate the usability of open government data on occupancy in combination with third-party data on online audiences’ engagement for DMOs’ potential seasonal geotargeting via utilizing Openrouteservice’s APIs. For the pilot case, a Slovak (Central Europe) destination’s data on occupancy, and the DMO’s website and social media engagement by origin were used to determine potential audiences’ accessibility by car. Testing of the pilot results on a sample of foreign markets indicates that by a partial mix of the means of transportation, the vast majority of audiences are within a 4 h long incoming trip. Although the preliminary tests indicate a linear correlation between the destination’s occupancy and online audiences’ share accessibility by car, for further extrapolation, the list of missing input remains long. The main addition to the field of tourism and destination management may be the partial reusability of developed techniques for data extraction, and transformation for further data overlays, which may save some time. Full article
(This article belongs to the Special Issue Data-Driven Approach on Urban Planning and Smart Cities)
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19 pages, 1486 KiB  
Article
Identifying and Classifying Urban Data Sources for Machine Learning-Based Sustainable Urban Planning and Decision Support Systems Development
by Stéphane C. K. Tékouabou, Jérôme Chenal, Rida Azmi, Hamza Toulni, El Bachir Diop and Anastasija Nikiforova
Data 2022, 7(12), 170; https://doi.org/10.3390/data7120170 - 28 Nov 2022
Cited by 5 | Viewed by 3763
Abstract
With the increase in the amount and variety of data that are constantly produced, collected, and exchanged between systems, the efficiency and accuracy of solutions/services that use data as input may suffer if an inappropriate or inaccurate technique, method, or tool is chosen [...] Read more.
With the increase in the amount and variety of data that are constantly produced, collected, and exchanged between systems, the efficiency and accuracy of solutions/services that use data as input may suffer if an inappropriate or inaccurate technique, method, or tool is chosen to deal with them. This paper presents a global overview of urban data sources and structures used to train machine learning (ML) algorithms integrated into urban planning decision support systems (DSS). It contributes to a common understanding of choosing the right urban data for a given urban planning issue, i.e., their type, source and structure, for more efficient use in training ML models. For the purpose of this study, we conduct a systematic literature review (SLR) of all relevant peer-reviewed studies available in the Scopus database. More precisely, 248 papers were found to be relevant with their further analysis using a text-mining approach to determine (a) the main urban data sources used for ML modeling, (b) the most popular approaches used in relevant urban planning and urban problem-solving studies and their relationship to the type of data source used, and (c) the problems commonly encountered in their use. After classifying them, we identified the strengths and weaknesses of data sources depending on several predefined factors. We found that the data mainly come from two main categories of sources, namely (1) sensors and (2) statistical surveys, including social network data. They can be classified as (a) opportunistic or (b) non-opportunistic depending on the process of data acquisition, collection, and storage. Data sources are closely correlated with their structure and potential urban planning issues to be addressed. Almost all urban data have an indexed structure and, in particular, either attribute tables for statistical survey data and data from simple sensors (e.g., climate and pollution sensors) or vectors, mostly obtained from satellite images after large-scale spatio-temporal analysis. The paper also provides a discussion of the potential opportunities, emerging issues, and challenges that urban data sources face and should overcome to better catalyze intelligent/smart planning. This should contribute to the general understanding of the data, their sources and the challenges to be faced and overcome by those seeking data and integrating them into smart applications and urban-planning processes. Full article
(This article belongs to the Special Issue Data-Driven Approach on Urban Planning and Smart Cities)
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10 pages, 2740 KiB  
Data Descriptor
Methodology for the Surveillance the Voltage Supply in Public Buildings Using the ITIC Curve and Python Programming
by Javier Fernández-Morales, Juan-José González-de-la-Rosa, José-María Sierra-Fernández, Olivia Florencias-Oliveros, Paula Remigio-Carmona, Manuel-Jesús Espinosa-Gavira, Agustín Agüera-Pérez and José-Carlos Palomares-Salas
Data 2022, 7(11), 162; https://doi.org/10.3390/data7110162 - 17 Nov 2022
Viewed by 2109
Abstract
This paper proposes an easy-to-implement method for detecting and assessing two of the most frequent PQ (Power Quality) problems: voltage sags and swells. These can affect sensitive equipment such as computers, programmable logic controllers, contactors, etc. Therefore, it is of great interest to [...] Read more.
This paper proposes an easy-to-implement method for detecting and assessing two of the most frequent PQ (Power Quality) problems: voltage sags and swells. These can affect sensitive equipment such as computers, programmable logic controllers, contactors, etc. Therefore, it is of great interest to implement it in any laboratory, not only for protection reasons but also as a safeguard for claims against the supply company. Thanks to the actual context, in which it is possible to manage big volumes of data, connect multiple devices with IoT (Internet of Things), etc., it is feasible and of great interest to monitor the voltage at specific points of the network. This makes it possible to detect voltage sags and swells and diagnose which points are more prone to this type of problems. For the detection of sags and swells, a program written in Python is in charge of crawling all the files in the database and target those RMS values that fall outside the established limits. Compared to LabVIEW, which might have been the most logical alternative, being the acquisition hardware from the same company (National Instruments), Python has a higher computational performance and is also free of charge, unlike LabVIEW. Thanks to the libraries available in Python, it allows a hardware control close to what is possible using LabVIEW. Implemented in MATLAB, the ITIC (Information Technology Industry Council) power acceptability curve reflects the impact of these power quality disturbances in electrical power systems. The results showed that the combined action of Python and MATLAB performed well on a conventional desktop computer. Full article
(This article belongs to the Special Issue Data-Driven Approach on Urban Planning and Smart Cities)
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