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Sustainability
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Data-driven Decision Support for Urban Management: Trends and Challenges
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Data-driven Decision Support for Urban Management: Trends and Challenges

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Management".

Deadline for manuscript submissions: closed (1 March 2021) | Viewed by 6398

Special Issue Editors

Prof. Dr. Sidharta Gautama

E-Mail Website
Guest Editor
Department of Industrial Systems Engineering and Product Design, Ghent University, 9000 Gent, Belgium
Interests: intelligent systems; smart cities; industry 4.0; data quality; decision support
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ivana Semanjski

E-Mail Website
Guest Editor
Department of Industrial Systems Engineering and Product Design, Ghent University, 9000 Ghent, Belgium
Interests: big data; smart cities; data quality; GPS; city logistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With almost 70% of the world’s population expected to live in urban areas by 2050, cities increasingly face complex challenges to keep the urban environment accessible, attractive, healthy and sustainable. Administrations need to manage urban space, resources and activity in line with the needs of citizens and local economy. At the same time, modern urban services are rapidly changing with new models like autonomous vehicles, co-housing, same day delivery entering the market and fundamentally changing traditional patterns of behaviour.

Modern data sources can help to give detailed and current insight in different aspects of a city like transport and mobility, energy, environment, tourism. Emerging sources like massive location traces, IoT sensors, smart cards and social media offer the possibility of capturing the pulse of a city in realtime and complement traditional sources that are often estimated and delayed in time. They give insight with a detailed spatial, time and user granularity. Many cities have started to collect and explore data like live traffic data, citizen science mobile data campaigns and cell phone-based pedestrian monitoring. Data collection is however only a first step. There are big challenges in using this data for policy support in an efficient and reliable way. The support can range from city dashboards that monitor urban indicators for operational support (e.g. traffic management, environment) up to data-driven prediction models that predict short-term events (e.g. traffic jams) or long-term trends (e.g. energy consumption). Depending its use, challenges lie in the quality and reliability of the data, inter-operability and standardization for data integration, the transformation and mining of raw data into policy-relevant information, the integration of data into urban decision support systems, privacy and governance of urban data. In this special issue, we invite papers that explore the use of modern data sources in urban decision making. Suggested topics include, but are not limited to:

  • Reviews on the state of art in urban data sources and policy support;
  • Applications in data-driven urban decision support;
  • Methods for quality assessment of urban big data;
  • Interoperability and standardization of governmental and commercial (open) data and services;
  • Data mining for urban policy indicators and information;
  • Data-driven predictive modelling (e.g. neural networks, agent-based simulation) for urban management.

Prof. Dr. Sidharta Gautama
Prof. Dr. Ivana Semanjski
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. Sustainability 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 2400 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

  • Data-driven decision support
  • Predictive modelling
  • Data mining
  • Data governance
  • Urban management

Published Papers (2 papers)

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Research

16 pages, 6322 KiB  
Article
Traffic State Estimation and Classification on Citywide Scale Using Speed Transition Matrices
by Leo Tišljarić, Tonči Carić, Borna Abramović and Tomislav Fratrović
Sustainability 2020, 12(18), 7278; https://doi.org/10.3390/su12187278 - 4 Sep 2020
Cited by 27 | Viewed by 3082
Abstract
The rising need for mobility, especially in large urban centers, consequently results in congestion, which leads to increased travel times and pollution. Advanced traffic management systems are being developed to take the advantage of increased mobility positive effects and minimize the negative ones. [...] Read more.
The rising need for mobility, especially in large urban centers, consequently results in congestion, which leads to increased travel times and pollution. Advanced traffic management systems are being developed to take the advantage of increased mobility positive effects and minimize the negative ones. The first step dealing with congestion in urban areas is the detection of congested areas and the estimation of the congestion level. This paper presents a a method for a traffic state estimation on a citywide scale using the novel traffic data representation, named Speed Transition Matrix (STM). The proposed method uses traffic data to extract the STMs and to estimate the traffic state based on the Center Of Mass (COM) computation for every STM. The COM-based approach enables the simplification of the clustering process and provides increased interpretability of the resulting clusters. Using the proposed method, traffic data is analyzed, and the traffic state is estimated for the most relevant road segments in the City of Zagreb, which is the capital and the largest city in Croatia. The traffic state classification results are validated using the cross-validation method and the domain knowledge data with the resulting accuracy of 97% and 91%, respectively. The results indicate the possible application of the proposed method for the traffic state estimation on macro- and micro-locations in the city area. In the end, the application of STMs for traffic state estimation, traffic management, and anomaly detection is discussed. Full article
(This article belongs to the Special Issue Data-driven Decision Support for Urban Management: Trends and Challenges)
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18 pages, 4126 KiB  
Article
Rethinking the Identification of Urban Centers from the Perspective of Function Distribution: A Framework Based on Point-of-Interest Data
by Lu Yu, Tao Yu, Yongxiang Wu and Guangdong Wu
Sustainability 2020, 12(4), 1543; https://doi.org/10.3390/su12041543 - 19 Feb 2020
Cited by 16 | Viewed by 2904
Abstract
Urban spatial structure has a significant impact on the sustainable development of cities. An important step of urban spatial structure analysis is the identification of urban centers. From the perspective of urban function distribution, this study developed a theoretical framework of three layers [...] Read more.
Urban spatial structure has a significant impact on the sustainable development of cities. An important step of urban spatial structure analysis is the identification of urban centers. From the perspective of urban function distribution, this study developed a theoretical framework of three layers for urban center identification. In the first layer, point-of-interest data were collected from geospatial databases and utilized to capture the spatial distribution of urban functions. In the second layer, the density-based spatial clustering of application with noise (DBSCAN) algorithm was employed to group points of interest into urban centers according to their inter-distances and urban functions. In the third layer, the spatial distribution of the identified urban centers was visualized by the ArcGIS platform. This framework was applied in the urban center analysis of Beijing. The results showed that Beijing is in the process of transitioning from monocentric to polycentric with urban functions distributed unevenly throughout the city. To facilitate this transition, strategies such as the construction of super-large communities, the development of branch campuses and higher-education parks, and the enhancement of modern culture industries should be considered for the 11 new towns of Beijing. Full article
(This article belongs to the Special Issue Data-driven Decision Support for Urban Management: Trends and Challenges)
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