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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 (1 September 2023) | Viewed by 23160

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Special Issue Editors

Prof. Dr. Xuan Song

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Guest Editor

Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
Interests: artificial intelligence; data mining; urban computing; intelligent system, especially in intelligent surveillance and information system designs; mobility and spatio-temporal data mining

Dr. Xiaodan Shi

E-Mail Website
Guest Editor

Center for Spatial Information Science, The University of Tokyo, Kashiwa, Japan
Interests: deep learning and its applications in sequence prediction, image processing and pattern recognition

Special Issue Information

Dear Colleagues,

The smart urban issue is an interdisciplinary field, which requires the cooperation of different technologies including IoT, machine learning and deep learning, to support cities in improving the performance of transportation, energy and health, and reaching the required sustainability levels. Modern cities generate large amounts of data from mobile phones, sensors, satellites and other digital devices every day, which contain rich information about the city and its citizens. These heterogeneous spatio-temporal urban big data, including but not limited to human flow, traffic volume, vehicle trajectories, accident records, and weather quality observation, provide intelligent solutions for alleviating traffic congestion， responding to public health problems and many other urban problems. However, it is challenging to efficiently recognize, analyze, curate, and manage the patterns and needs from urban big data. The question of how to effectively mine big data to provide efficient information for smart urban requires further investigation. The development of deep learning, including domain adaption, self/semi-supervised learning, few-shot learning, and interpretable learning, enables approaches that mine urban big data from small samples and can be generalized and adapted across domains.

The goal of this Special Issue is to feature the most recent developments and state-of-the-art multidisciplinary research across the areas of machine learning, deep learning, civil and environmental engineering, transportation science, and many others, focusing on technologies, visionary ideas, case studies, and intelligent systems to learn, recognize, manage and analyze big data to provide rich, useful information to build smarter cities.

We invite original high-quality research contributions that collect, process, manage, mine, analyze, and understand various big data to improve urban intelligence. Topics of interest include (but are not limited to) the following issues:

Big data, big data collecting and IoT frameworks for smart urban cities;
Urban big data cleaning and preparation;
Urban human mobility pattern recognition and analysis;
Vehicle trajectories’ pattern recognition and analysis;
Traffic congestion relief and analysis;
New evaluation and assessment idea and methods for urban big data;
Public health, environmental health analysis from big data for urban areas;
Social computing and networks, social behavior modeling for smart urban areas.

Prof. Dr. Xuan Song
Dr. Xiaodan Shi
Guest Editors

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Keywords

smart cities
spatio-temporal data
deep learning
urban intelligence
urban computing
machine learning
big data analysis

Published Papers (8 papers)

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Research

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17 pages, 2494 KiB

Open AccessArticle

Research on Improved Traffic Flow Prediction Network Based on CapsNet

by Bin Qiu and Yun Zhao

Sustainability 2022, 14(23), 15996; https://doi.org/10.3390/su142315996 - 30 Nov 2022

Cited by 2 | Viewed by 1038

Abstract

Traffic flow prediction is the basis and key to the realization of an intelligent transportation system. The road traffic flow prediction of city-level complex road network can be realized using traffic big data. In the traffic prediction task, the limitation of the convolutional neural network (CNN) for modeling the spatial relationship of the road network and the insufficient feature extraction of the shallow network make it impossible to accurately predict the traffic flow. In order to improve the prediction performance of the model, this paper proposes an improved capsule network (MCapsNet) based on capsule network (CapsNet). First, in the preliminary feature extraction stage, a depthwise separable convolutional block is added to expand the feature channel to enrich channel information. Subsequently, in order to strengthen the reuse of important features and suppress useless information, channel attention is used to selectively reinforce learning of extended channel information so that the network can extract a large number of high-dimensional important features and improve the ability of network feature learning and expression. At the same time, in order to alleviate the feature degradation during training and the channel collapse problem easily caused by deep convolution, a shortcut connection, and a modified linear bottleneck layer structure are added to the convolution layer. The bottleneck layer adds the depth convolution and channel attention connection to the residual block of the network. Finally, the deep local feature information extracted from the improved convolutional layer is vectorized into the form of a capsule, which can more accurately model the details of road network attributes and features and improve the model expression power and prediction performance. The network is tested on the Wenyi Road dataset and the public dataset SZ-taxi. Compared with other models, the evaluation indicators of MCapsNet are better than other models in the tests of different time periods and predictors. Compared with CapsNet, the RMSE index of MCapsNet is reduced by 10.50% in the full period of Wenyi Road, 4.66% in the peak period, 9.78% in the off-peak period, and 6.07% in the SZ-tax dataset. The experimental results verify the effectiveness of the model improvement. Full article

(This article belongs to the Special Issue Big Data, Information and AI for Smart Urban)

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17 pages, 481 KiB

Open AccessEditor’s ChoiceArticle

Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting

by Lingyu Zhang, Xu Geng, Zhiwei Qin, Hongjun Wang, Xiao Wang, Ying Zhang, Jian Liang, Guobin Wu, Xuan Song and Yunhai Wang

Sustainability 2022, 14(19), 12397; https://doi.org/10.3390/su141912397 - 29 Sep 2022

Cited by 5 | Viewed by 2155

Abstract

Graph convolution network-based approaches have been recently used to model region-wise relationships in region-level prediction problems in urban computing. Each relationship represents a kind of spatial dependency, such as region-wise distance or functional similarity. To incorporate multiple relationships into a spatial feature extraction, we define the problem as a multi-modal machine learning problem on multi-graph convolution networks. Leveraging the advantage of multi-modal machine learning, we propose to develop modality interaction mechanisms for this problem in order to reduce the generalization error by reinforcing the learning of multi-modal coordinated representations. In this work, we propose two interaction techniques for handling features in lower layers and higher layers, respectively. In lower layers, we propose grouped GCN to combine the graph connectivity from different modalities for a more complete spatial feature extraction. In higher layers, we adapt multi-linear relationship networks to GCN by exploring the dimension transformation and freezing part of the covariance structure. The adapted approach, called multi-linear relationship GCN, learns more generalized features to overcome the train–test divergence induced by time shifting. We evaluated our model on a ride-hailing demand forecasting problem using two real-world datasets. The proposed technique outperforms state-of-the art baselines in terms of prediction accuracy, training efficiency, interpretability and model robustness. Full article

(This article belongs to the Special Issue Big Data, Information and AI for Smart Urban)

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14 pages, 2839 KiB

Open AccessArticle

A Hybrid Deep Learning Model for Short-Term Traffic Flow Pre-Diction Considering Spatiotemporal Features

by Shenghan Zhou, Chaofan Wei, Chaofei Song, Yu Fu, Rui Luo, Wenbing Chang and Linchao Yang

Sustainability 2022, 14(16), 10039; https://doi.org/10.3390/su141610039 - 13 Aug 2022

Cited by 6 | Viewed by 1446

Abstract

Traffic flow prediction is one of the basic, key problems with developing an intelligent transportation system since accurate and timely traffic flow prediction can provide information support and decision support for traffic control and guidance. However, due to the complex characteristics of traffic information, it is still a challenging task. This paper proposes a novel hybrid deep learning model for short-term traffic flow prediction by considering the inherent features of traffic data. The proposed model consists of three components: the recent, daily and weekly components. The recent component is integrated with an improved graph convolutional network (GCN) and bi-directional LSTM (Bi-LSTM). It is designed to capture spatiotemporal features. The remaining two components are built by multi-layer Bi-LSTM. They are developed to extract the periodic features. The proposed model focus on the important information by using an attention mechanism. We tested the performance of our model with a real-world traffic dataset and the experimental results indicate that our model has better prediction performance than those developed previously. Full article

(This article belongs to the Special Issue Big Data, Information and AI for Smart Urban)

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14 pages, 2800 KiB

Open AccessArticle

A Comparative Study of Machine Learning and Spatial Interpolation Methods for Predicting House Prices

by Jeonghyeon Kim, Youngho Lee, Myeong-Hun Lee and Seong-Yun Hong

Sustainability 2022, 14(15), 9056; https://doi.org/10.3390/su14159056 - 24 Jul 2022

Cited by 12 | Viewed by 3663

Abstract

As the volume of spatial data has rapidly increased over the last several decades, there is a growing concern about missing and incomplete observations that may result in biased conclusions. Several recent studies have reported that machine learning techniques can more efficiently address this limitation in emerging data sets than conventional interpolation approaches, such as inverse distance weighting and kriging. However, most existing studies focus on data from environmental sciences; so, further evaluations are required to assess their strengths and limitations for socioeconomic data, such as house price data. In this study, we conducted a comparative analysis of four commonly used methods: neural networks, random forests, inverse distance weighting, and kriging. We applied these methods to the real estate transaction data of Seoul, South Korea, and demonstrated how the values of the houses at which no transactions are recorded could be predicted. Our empirical analysis suggested that the neural networks and random forests can provide a more accurate estimation than the interpolation counterparts. Of the two machine learning techniques, the results from a random forest model were slightly better than those from a neural network model. However, the neural network appeared to be more sensitive to the amount of training data, implying that it has the potential to outperform the other methods when there are sufficient data available for training. Full article

(This article belongs to the Special Issue Big Data, Information and AI for Smart Urban)

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22 pages, 74076 KiB

Open AccessArticle

Wind Environment Simulation and Optimisation Strategies for Block Spatial Forms in Cold Low Mountainous Areas—A Case Study of Changchun, China

by Hongyu Zhao, Xue Jiang, Yujie Cao, Haina Zhang, Shinan Zhen, Runze Jia and Shichao Zhang

Sustainability 2022, 14(11), 6643; https://doi.org/10.3390/su14116643 - 28 May 2022

Cited by 1 | Viewed by 1803

Abstract

Low mountainous areas provide high-quality ecological environments, offering a high urban development value globally. However, cold low mountainous areas are greatly affected by wind environments. Therefore, this study investigates a simulated block wind environment in a typical city in a cold low mountainous area. As opposed to previous work, we put forward the block spatial modes quantitatively for cold low mountainous areas. Computational fluid dynamics (CFD) technology is used to simulate the wind environment of building blocks, including point-type high-rise buildings and row-type multi-story buildings. We propose a new targeted wind environment measurement system developed using PHOENICS 2018 and a spatial combination model using urban information sensing for sustainable development. By comparing the average wind speed (W_AS) and calm wind area ratio (S_CA) under different simulation conditions, we were able find that when the building form, slope direction, and slope were constant, W_AS was inversely proportional to S_CA, following the order of south slope > west slope > southwest slope > southeast slope. Second, proper selection of 1:2 and 1:3 ratios for point-type high-rise buildings (HPT) can provide good ventilation for cold low mountainous areas. In addition, continuous high-rise buildings should be avoided. These strategies have been applied in practice in the spatial design of the Lianhuashan tourist resort in Changchun. Possible optimization strategies for planners and governments could include promoting pedestrian spatial environments in these special areas. Moreover, this research is significant for the collection and mining of data-based wind information in cold low mountainous areas, thereby providing scientific quantitative evaluation methods and spatial organisation optimisation guidelines. Full article

(This article belongs to the Special Issue Big Data, Information and AI for Smart Urban)

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15 pages, 4023 KiB

Open AccessArticle

Combining Canopy Coverage and Plant Height from UAV-Based RGB Images to Estimate Spraying Volume on Potato

by Jingxin Xie, Zhongrui Zhou, Hongduo Zhang, Liang Zhang and Ming Li

Sustainability 2022, 14(11), 6473; https://doi.org/10.3390/su14116473 - 25 May 2022

Cited by 4 | Viewed by 1799

Abstract

Canopy coverage and plant height are the main crop canopy parameters, which can obviously reflect the growth status of crops on the field. The ability to identify canopy coverage and plant height quickly is critical for farmers or breeders to arrange their working schedule. In precision agriculture, choosing the opportunity and amount of farm inputs is the critical part, which will improve the yield and decrease the cost. The potato canopy coverage and plant height were quickly extracted, which could be used to estimate the spraying volume using the evaluation model obtained by indoor tests. The vegetation index approach was used to extract potato canopy coverage, and the color point cloud data method at different height rates was formed to estimate the plant height of potato at different growth stages. The original data were collected using a low-cost UAV, which was mounted on a high-resolution RGB camera. Then, the Structure from Motion (SFM) algorithm was used to extract the 3D point cloud from ordered images that could form a digital orthophoto model (DOM) and sparse point cloud. The results show that the vegetation index-based method could accurately estimate canopy coverage. Among EXG, EXR, RGBVI, GLI, and CIVE, EXG achieved the best adaptability in different test plots. Point cloud data could be used to estimate plant height, but when the potato coverage rate was low, potato canopy point cloud data underwent rarefaction; in the vigorous growth period, the estimated value was substantially connected with the measured value (R² = 0.94). The relationship between the coverage area of spraying on potato canopy and canopy coverage was measured indoors to form the model. The results revealed that the model could estimate the dose accurately (R² = 0.878). Therefore, combining agronomic factors with data extracted from the UAV RGB image had the ability to predict the field spraying volume. Full article

(This article belongs to the Special Issue Big Data, Information and AI for Smart Urban)

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27 pages, 5009 KiB

Open AccessEditor’s ChoiceArticle

Understanding the Urban Environment from Satellite Images with New Classification Method—Focusing on Formality and Informality

by Qianwei Cheng, Moinul Zaber, AKM Mahbubur Rahman, Haoran Zhang, Zhiling Guo, Akiko Okabe and Ryosuke Shibasaki

Sustainability 2022, 14(7), 4336; https://doi.org/10.3390/su14074336 - 06 Apr 2022

Cited by 4 | Viewed by 3933

Abstract

Urbanization plays a critical role in changing the urban environment. Most developed countries have almost completed urbanization. However, with more and more people moving to cities, the urban environment in developing countries is undergoing significant changes. Sustainable development cannot be achieved without significant changes in building, managing, and responding to changes in the urban environment. The classified measurement and analysis of the urban environment in developing countries and the real-time understanding of the evolution and characteristics of the urban environment are of great significance for decision-makers to manage and plan cities more effectively and maintain the sustainability of the urban environment. Hence, a method readily applicable for the state-of-the-art computational analysis can help conceive the rapidly changing urban socio-environmental dynamics that can make the policy-making process even more informative and help monitor the changes almost in real-time. Based on easily accessible data from Google Earth, this work develops and proposes a new urban environment classification method focusing on formality and informality. Firstly, the method gives a new model to scrutinize the urban environment based on the buildings and their surroundings. Secondly, the method is suited for the state-of-the-art machine learning processes that make it applicable and scalable for forecasting, analytics, or computational modeling. The paper first demonstrates the model and its applicability based on the urban environment in the developing world. The method divides the urban environment into 16 categories under four classes. Then it is used to draw the urban environment classes maps of the following emerging cities: Nairobi in Kenya, Mumbai in India, Guangzhou in China, Jakarta in Indonesia, Cairo in Egypt, and Lima in Chile. Then, we discuss the characteristics of different urban environments and the differences between the same class in different cities. We also demonstrate the agility of the proposed method by showing how this classification method can be easily augmented with other data such as population per square kilometer to aid the decision-making process. This mapping should help urban designers who are working on analyzing formality and informality in the developing world. Moreover, from the application point of view, this will provide training data sets for future deep learning algorithms and automate them, help establish databases, and significantly reduce the cost of acquiring data for urban environments that change over time. The method can become a necessary tool for decision-makers to plan sustainable urban spaces in the future to design and manage cities more effectively. Full article

(This article belongs to the Special Issue Big Data, Information and AI for Smart Urban)

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Review

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32 pages, 2350 KiB

Open AccessEditor’s ChoiceReview

Urban Computing for Sustainable Smart Cities: Recent Advances, Taxonomy, and Open Research Challenges

by Ibrahim Abaker Targio Hashem, Raja Sher Afgun Usmani, Mubarak S. Almutairi, Ashraf Osman Ibrahim, Abubakar Zakari, Faiz Alotaibi, Saadat Mehmood Alhashmi and Haruna Chiroma

Sustainability 2023, 15(5), 3916; https://doi.org/10.3390/su15053916 - 21 Feb 2023

Cited by 13 | Viewed by 5971

Abstract

The recent proliferation of ubiquitous computing technologies has led to the emergence of urban computing that aims to provide intelligent services to inhabitants of smart cities. Urban computing deals with enormous amounts of data collected from sensors and other sources in a smart city. In this article, we investigated and highlighted the role of urban computing in sustainable smart cities. In addition, a taxonomy was conceived that categorized the existing studies based on urban data, approaches, applications, enabling technologies, and implications. In this context, recent developments were elucidated. To cope with the engendered challenges of smart cities, we outlined some crucial use cases of urban computing. Furthermore, prominent use cases of urban computing in sustainable smart cities (e.g., planning in smart cities, the environment in smart cities, energy consumption in smart cities, transportation in smart cities, government policy in smart cities, and business processes in smart cities) for smart urbanization were also elaborated. Finally, several research challenges (such as cognitive cybersecurity, air quality, the data sparsity problem, data movement, 5G technologies, scaling via the analysis and harvesting of energy, and knowledge versus privacy) and their possible solutions in a new perspective were discussed explicitly. Full article

(This article belongs to the Special Issue Big Data, Information and AI for Smart Urban)

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