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The Emerging Data–Driven Smart City of Sustainability

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Urban and Rural Development".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 24607

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Special Issue Editors

Dr. Hongzan Jiao

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Guest Editor

School of Urban Design, Wuhan University, Wuhan 430072, China
Interests: smart city; big data analysis; urban planning; land use and transportation

Dr. Wenshu Li

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Guest Editor

School of Urban Design, Wuhan University, Wuhan 430072, China
Interests: smart city; urban design; public participation; community governance

Dr. Wen Zheng

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Guest Editor

College of Data Sciences, Taiyuan University of Technology, Taiyuan 030024, China
Interests: big data technology; industrial Internet of Things; computational physics; impact dynamics
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Special Issue Information

Dear Colleagues,

Smart, sustainable cities are quintessential complex systems dynamically changing the built and human environment. ICT and big data technologies are essential to the functioning of smart cities. As part of the increasing attention on smart city sustainability, emerging data-driven smart cities have recently received attention as an important technology to address sustainability issues. Big data pose significant challenges to the sustainable development of smart cities and will require effective drive actions and adaptations in the big data revolution. However, there is a lack of responses to the practical effects and driving mechanisms of smart, sustainable cities based on data.

This Special Issue is calling for original research manuscripts that examine how the emerging data-driven smart cities are being conceived and justified in terms of development and implementation for sustainability. This Special Issue also seeks to highlight research on the driving effects and strategies of big data on urban processes and practices as a form of data-driven urbanism. We welcome work that makes use of a diverse array of theoretical and methodological approaches to examine this phenomenon. Papers selected for this Special Issue will be subject to rigorous peer review procedures with the aim of wide dissemination of research results, developments, and applications.

Prof. Dr. Hongzan Jiao
Dr. Wenshu Li
Prof. Dr. Wen Zheng
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

big data collection for smart cities
big data sensing and IoT frameworks and infrastructures
urban planning for smart cities
smart city and big data governance and management
smart city based on open data and public participation
case studies and innovative applications
data mining and machine learning for smart cities
smart city transportation big data and analytics

Published Papers (10 papers)

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Research

28 pages, 18149 KiB

Open AccessArticle

Quantitative Evaluation of Friendliness in Streets’ Pedestrian Networks Based on Complete Streets: A Case Study in Wuhan, China

by Yuanyuan Ma and Hongzan Jiao

Sustainability 2023, 15(13), 10317; https://doi.org/10.3390/su151310317 - 29 Jun 2023

Cited by 2 | Viewed by 1635

Abstract

In recent years, with the rapid growth in the number of cars, the safe and convenient street pedestrian traffic network in cities has been broken by motor vehicle lanes. The pedestrian traffic function of streets as an important urban public space has been lost, and the pedestrian friendliness of streets needs urgent improvement. However, the existing pedestrian-friendly street space assessment has not yet formed a set of full-factor quantitative evaluation systems, making the construction of pedestrian-friendly streets still in the conceptual stage and lacking practical significance. The complete streets design concept clarifies the goal of street pedestrian space construction and proposes the full elements of street pedestrian space design, which provides important support for the construction of the street pedestrian friendliness evaluation system. Based on the complete streets design concept, this study constructs a complete set of quantitative evaluation systems of street walkability from three aspects of street space: traffic, environment and function. Meanwhile, a street pedestrian usability evaluation method is proposed to further explore the actual demand of streets. Combined with the comprehensive evaluation matrix of street pedestrian friendliness and usability, the areas where the planning of street pedestrian space does not match with the actual space are explored. The case study in Wuhan found that the overall pedestrian friendliness was high in the area, but there was significant variability. The study area is dominated by streets in need of improvement, with medium demand–low friendliness, and both the pedestrian friendliness and usability of the streets need to be improved. Full article

(This article belongs to the Special Issue The Emerging Data–Driven Smart City of Sustainability)

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24 pages, 3261 KiB

Open AccessArticle

Coupling Coordination Research on Disaster-Adapted Resilience of Modern Infrastructure System in the Middle and Lower Section of the Three Gorges Reservoir Area

by Guiyuan Li, Guo Cheng, Zhenying Wu and Xiaoxiao Liu

Sustainability 2022, 14(21), 14514; https://doi.org/10.3390/su142114514 - 04 Nov 2022

Cited by 7 | Viewed by 1281

Abstract

Rapid incremental urbanization in China has resulted in an incomplete modern infrastructure system and multiple point-like flaws. This is due to a lack of funding and poor scientific construction concepts and procedures. This also contributes to the infrastructure system’s low disaster-adapted resilience and insufficient coupling coordination of production-oriented and service-oriented infrastructure subsystems. Based on the “Robustness-Rapidity-Redundancy-Resourcefulness-Durability” (4R-D) frameworks, this study screens 53 indicators across three tiers of “production-oriented, service-oriented, intelligent” infrastructure subsystems to establish a modern infrastructure resilience evaluation system. We examined the overall infrastructure resilience and coupling coordination development among subsystems in the Three Gorges Reservoir Area (TGRA) from 2009 to 2020 using a coupling coordination degree model (CCDM). Grey relational analysis (GRA) was used to analyze the significant control aspects of infrastructure resilience and coupling coordination degree based on grey system theory. The findings show the following: (1) at the macro level the overall resilience, resilience of each subsystem, and coupling coordination among subsystems in the research region show an upward trend from 2009 to 2020, with the rise from 2018 to 2020 being the most significant; (2) at the micro level, from 2010 to 2013, there was no obvious spatial divergence and from 2014 to 2020, driven by the radiation of the two major urban agglomerations, the resilience and coupling coordination of Yiling and Wanzhou both show a trend of more substantial increase, while the rest of the counties have a small increase; and (3) at the meso level, seven factors have a more significant impact on the coupled and coordinated development of urban infrastructure than other indicators, including urbanization rate, average annual rainfall, the number of health technicians per 10,000 people, and the percentage of GDP in the tertiary industrial sector. Full article

(This article belongs to the Special Issue The Emerging Data–Driven Smart City of Sustainability)

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12 pages, 1998 KiB

Open AccessArticle

Traffic Flow Prediction with Attention Mechanism Based on TS-NAS

by Cai Zhao, Ruijing Liu, Bei Su, Lei Zhao, Zhiyong Han and Wen Zheng

Sustainability 2022, 14(19), 12232; https://doi.org/10.3390/su141912232 - 27 Sep 2022

Cited by 1 | Viewed by 1329

Abstract

The prediction of traffic flow is of great significance in the traffic field. However, because of the high uncertainty and complexity of traffic data, it is challenging that doing traffic flow prediction. Most of the existing methods have achieved good results in traffic flow prediction, but are not accurate enough to capture the dynamic temporal and spatial relationship of data by using the structural information of traffic flow. In this study, we propose a traffic flow prediction method with temporal attention mechanism and spatial attention mechanism based on neural architecture search (TS-NAS). Firstly, based on temporal and spatial attention mechanisms, we design a new attention mechanism. Secondly, we define a novel model to learn temporal flow and space flow in traffic network. Finally, the proposed method uses different modules about time, space and convolution and neural architecture search to be used for optimizing the model. We use two datasets to test the method. Experimental results show that the performance of the method is better than that of the existing method. Full article

(This article belongs to the Special Issue The Emerging Data–Driven Smart City of Sustainability)

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19 pages, 3485 KiB

Open AccessArticle

Improved Marine Predator Algorithm for Wireless Sensor Network Coverage Optimization Problem

by Qing He, Zhouxin Lan, Damin Zhang, Liu Yang and Shihang Luo

Sustainability 2022, 14(16), 9944; https://doi.org/10.3390/su14169944 - 11 Aug 2022

Cited by 11 | Viewed by 1421

Abstract

A wireless sensor network (WSN) is a distributed network system composed of a great many sensor nodes that rely on self-organization. The random deployment of WSNs in city planning often leads to the problem of low coverage of monitoring areas. In the construction of smart cities in particular, a large number of sensor nodes need to be deployed to maintain the reception, processing, and transmission of data throughout the city. However, the uneven distribution of nodes can cause a lot of wasted resources. To solve this problem, this paper proposes a WSN coverage optimization model based on an improved marine predator algorithm (IMPA). The algorithm introduces a dynamic inertia weight adjustment strategy in the global exploration and local exploitation stages of the standard marine predator algorithm to balance the exploration and exploitation capabilities of the algorithm and improve its solution accuracy. At the same time, the improved algorithm uses a multi-elite random leading strategy to enhance the information exchange rate between population individuals and improve the algorithm’s ability to jump out of the local optimum. The optimization experiment results of 11 benchmark test functions and part of the CEC2014 test functions show that the optimization performance of the improved algorithm is significantly better than the standard marine predator algorithm and other algorithms in the literature. Finally, the improved algorithm is applied to the WSN coverage optimization problem. The simulation results demonstrate that the IMPA has a better coverage rate than other metaheuristic algorithms and other improved algorithms in the literature for solving the WSN coverage optimization problem. Full article

(This article belongs to the Special Issue The Emerging Data–Driven Smart City of Sustainability)

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29 pages, 4848 KiB

Open AccessEditor’s ChoiceArticle

Construction of an Ecological Network Based on an Integrated Approach and Circuit Theory: A Case Study of Panzhou in Guizhou Province

by Liu Yang, Mengmeng Suo, Shunqian Gao and Hongzan Jiao

Sustainability 2022, 14(15), 9136; https://doi.org/10.3390/su14159136 - 26 Jul 2022

Cited by 5 | Viewed by 1861

Abstract

Protecting ecological security has become the backbone of social and economic development since declines in ecological quality due to an increase in human dominance over the natural environment. The establishment of ecological networks is an effective, comprehensive spatial regulation means to ensure regional ecological security. Panzhou city, as a case study, is a typical karst county and has been confronted with the pressure of ecological degradation in recent decades. In this study, an integrated approach combining ecological quality (EQ), ecosystem function importance (EFI), and morphological spatial pattern analysis (MSPA) was developed to determine the ecological sources. Ecological corridors, ecological pinch areas, and ecological barriers were extracted using circuit theory to identify the restored and conserved priority areas of ecological security patterns. The results showed that (1) the remote sensing ecological index (RSEI) and EFI exhibited typical geographical distributions, with the highest values concentrated in the northern and southern parts of the study area and the lowest values scattered in the middle part; (2) 26 patches with forestland, grassland, and waterbodies as the main land cover types were selected as the ecological sources; (3) 63 ecological corridors, composed of 45 key ecological corridors and 18 inactive ecological corridors, were extracted, accounting for 203.12 km and 163.31 km, respectively; (4) 82.76 km² of pinch areas and 320.29 km² of barriers were identified, both of which were distributed on key ecological corridors and played different roles in ecological security; and (5) 4 types of ecological security zones were established according to ecological sources, corridors, pinch areas, and barriers. This integrated approach provides a scientific method for the identification and implementation of ecological networks that can contribute to protecting regional ecological security. Our findings can serve as applicable and reasonable guidance to land administrators and policy-makers for adopting suitable territorial spatial planning, urban planning, green cities, etc. Full article

(This article belongs to the Special Issue The Emerging Data–Driven Smart City of Sustainability)

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18 pages, 4064 KiB

Open AccessArticle

Changes in and Patterns of the Tradeoffs and Synergies of Production-Living-Ecological Space: A Case Study of Longli County, Guizhou Province, China

by Shunqian Gao, Liu Yang and Hongzan Jiao

Sustainability 2022, 14(14), 8910; https://doi.org/10.3390/su14148910 - 20 Jul 2022

Cited by 16 | Viewed by 1502

Abstract

Production-living-ecological space (PLES) constitutes territorial space, and how to scientifically optimize PLES has become the core issue of territorial spatial planning in China. This paper constructs a spatial classification system for PLES based on merge classification. Taking Longli County, Guizhou Province, China, as an example, this paper studies the spatial patterns in 2015 and 2019, the driving factors of the changes in the spatial patterns, and the interrelationships of production space (PS), living space (LS) and ecological space (ES) and proposes a new scheme for dominant functional zoning. The results show that: (1) The high-scoring areas of PS and LS in Longli County are mainly located near the center of each town, with obvious consistency in the spatial distribution. The high-scoring areas of ES are located in the suburbs far from the towns, conflicting with PS and LS; (2) In the five-year period, PS and LS in Longli County continuously expanded. Specifically, LS expanded the most from the perspective of the rate of change, and ES shrunk continuously; (3) Socioeconomic factors are the dominant factor affecting the changes in PLES, among which the distance to town has the greatest influence; (4) Based on the correlation coefficient, PS and LS have a significant positive correlation, but they have a significant negative correlation with ES. In terms of spatial relationships, PS and LS mainly have synergistic relationships, but their relationships with ES mainly involve tradeoffs; (5) In the spatial functional areas of PLES in Longli County, the single dominant functional area is the main area, among which the ecological-dominant functional area is the largest. The results of this study provide a reference for territorial spatial planning and sustainable regional development. Full article

(This article belongs to the Special Issue The Emerging Data–Driven Smart City of Sustainability)

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26 pages, 5927 KiB

Open AccessArticle

Solar Photovoltaic Cell Parameter Identification Based on Improved Honey Badger Algorithm

by Wenjing Lei, Qing He, Liu Yang and Hongzan Jiao

Sustainability 2022, 14(14), 8897; https://doi.org/10.3390/su14148897 - 20 Jul 2022

Cited by 11 | Viewed by 1452

Abstract

Photovoltaic technology, which converts the sun’s light energy directly into electricity, can be used to make photovoltaic cells. The use of photovoltaic cells is centered on the idea of a low-carbon economy and green environmental protection, which effectively addresses the pollution problem in smart cities. Accurate identification of photovoltaic cell parameters is critical for battery life cycle and energy utilization. To accurately identify the single diode model (SDM), dual diode model (DDM), and three diode model (TDM) parameters of solar photovoltaic cells, and an improved honey badger algorithm (IHBA) is proposed in this paper. In the early stages of iteration, the IHBA uses the spiral exploration mechanism to improve the population’s global exploration ability. Furthermore, a density update factor that varies according to the quasi-cosine law is introduced to speed up the algorithm’s convergence speed and prevent the algorithm from falling into the local optimal value. Simultaneously, the pinhole imaging strategy is utilized to disturb the present optimal position to improve the algorithm’s optimization accuracy. The experimental comparison results of 18 benchmark test functions, Wilcoxon rank sum statistical test, and 30 CEC2014 test functions reveal that an IHBA shows remarkable performance in convergence speed, optimization accuracy, and robustness. Finally, the IHBA is used to identify the parameters of three kinds of commercial silicon R.T.C French solar photovoltaic cell models with a 57 mm diameter. In comparison to other algorithms, the IHBA can minimize the root mean square error (RMSE) between the measured current and estimated current at the fastest speed, demonstrating the practicality and superiority of the IHBA in tackling this problem. Full article

(This article belongs to the Special Issue The Emerging Data–Driven Smart City of Sustainability)

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16 pages, 2561 KiB

Open AccessArticle

Resilience Assessment of Urban Complex Giant Systems in Hubei Section of the Three Gorges Reservoir Area Based on Multi-Source Data

by Guiyuan Li, Guo Cheng and Zhenying Wu

Sustainability 2022, 14(14), 8423; https://doi.org/10.3390/su14148423 - 09 Jul 2022

Cited by 6 | Viewed by 1743

Abstract

Due to a lack of guidance in urban systems thinking, China’s rapid urbanization has intensified the interactions and coercive effects between the various urban space subsystems. As a result, “urban diseases” such as environmental pollution, frequent earthquakes, and unbalanced urban–rural development have spread. As a complex giant system, the exploration of urban resilience enhancement is critical to ensuring the joint spatial development of cities and towns. Based on the PSR model, this study screens 38 indicators in five levels of the natural-material-economic-social-intelligent regulation subsystem of the Three Gorges Reservoir Area urban giant system, and constructs a multi-source data resilience assessment framework. Likewise, it employs the Geodetector model to investigate the key factors impacting the resilience mechanism. The results demonstrate that: (1) between 2011 and 2020, the overall resilience in the Hubei section of the Three Gorges Reservoir Area increased from low to high and the coupled characterization of the “pressure-state-response” increased at different rates, with the state layer increasing the most; (2) the frequency of geological hazards, urbanization rate, and total number of early warning and monitoring of geological hazards are the key factors that contribute to changes in spatial resilience; (3) enhanced resilience is the result of the synergistic effects of different driving factors. Our model is used to assess the resilience of the urban system, assisting decision-makers in planning strategies to respond to urban system problems effectively and improve urban resilience. Full article

(This article belongs to the Special Issue The Emerging Data–Driven Smart City of Sustainability)

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27 pages, 1121 KiB

Open AccessArticle

An Improved Equilibrium Optimizer for Solving Optimal Power Flow Problem

by Zhouxin Lan, Qing He, Hongzan Jiao and Liu Yang

Sustainability 2022, 14(9), 4992; https://doi.org/10.3390/su14094992 - 21 Apr 2022

Cited by 8 | Viewed by 1526

Abstract

With the rapid development of the economy, the quality of power systems has assumed an increasingly prominent influence on people’s daily lives. In this paper, an improved equilibrium optimizer (IEO) is proposed to solve the optimal power flow (OPF) problem. The algorithm uses the chaotic equilibrium pool to enhance the information interaction between individuals. In addition, a nonlinear dynamic generation mechanism is introduced to balance the global search and local development capabilities. At the same time, the improved algorithm uses the golden sine strategy to update the individual position and enhance the ability of the algorithm to jump out of local optimums. Sixteen benchmark test functions, Wilcoxon rank sum test and 30 CEC2014 complex test function optimization results show that the improved algorithm has better global searching ability than the basic equilibrium optimizer, as well as faster convergence and a more accurate solution than other improved equilibrium optimizers and metaheuristic algorithms. Finally, the improved algorithm is applied to the standard IEEE 30-bus test systems for different objectives. The obtained results demonstrate that the improved algorithm has better solutions than other algorithms in the literature for solving the optimal power flow problem. Full article

(This article belongs to the Special Issue The Emerging Data–Driven Smart City of Sustainability)

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14 pages, 3788 KiB

Open AccessArticle

Fire-YOLO: A Small Target Object Detection Method for Fire Inspection

by Lei Zhao, Luqian Zhi, Cai Zhao and Wen Zheng

Sustainability 2022, 14(9), 4930; https://doi.org/10.3390/su14094930 - 20 Apr 2022

Cited by 53 | Viewed by 9152

Abstract

For the detection of small targets, fire-like and smoke-like targets in forest fire images, as well as fire detection under different natural lights, an improved Fire-YOLO deep learning algorithm is proposed. The Fire-YOLO detection model expands the feature extraction network from three dimensions, which enhances feature propagation of fire small targets identification, improves network performance, and reduces model parameters. Furthermore, through the promotion of the feature pyramid, the top-performing prediction box is obtained. Fire-YOLO attains excellent results compared to state-of-the-art object detection networks, notably in the detection of small targets of fire and smoke. Overall, the Fire-YOLO detection model can effectively deal with the inspection of small fire targets, as well as fire-like and smoke-like objects. When the input image size is 416 × 416 resolution, the average detection time is 0.04 s per frame, which can provide real-time forest fire detection. Moreover, the algorithm proposed in this paper can also be applied to small target detection under other complicated situations. Full article

(This article belongs to the Special Issue The Emerging Data–Driven Smart City of Sustainability)

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