Special Issue "Big Data-Driven Intelligent Services in Smart Cities"

A special issue of Smart Cities (ISSN 2624-6511).

Deadline for manuscript submissions: 30 September 2019.

Special Issue Editors

Guest Editor
Prof. Zhixiang Fang

State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan, Hubei 430079, China
Website | E-Mail
Interests: space–time GIS; smart cities; spatiotemporal optimization; intelligent logistics
Guest Editor
Prof. Lee D. Han

Civil & Environmental Engineering, University of Tennessee, 319 John. D. Tickle Building, Knoxville, TN, 37996-2313, USA
E-Mail
Interests: multisource sensor data processing and mining; accuracy and precision of big data; real-time non-recurrent event detection and management; management of connected and automated vehicles; mass evacuation operations; parallel simulation of microscopic traffic network; traffic flow theory; big data-based human behavior analysis and modification; dynamic traffic modeling and simulations

Special Issue Information

Dear Colleagues,

Smart Cities initiatives have become a focal point of research and development activities for many fields, including information technology, urban planning, economics, geography, and transportation. While headway has been made in physical and virtual space infrastructure and big data analytics associated with information and sensing, significant challenges are still ahead with regards to turning all the informatics into real-time data-driven services for the human at the center, which is the purpose of the Smart City. What would “smart” and “intelligent” mean to the residents of Smart Cities and how could they benefit, significantly, from living in these cities of tomorrow?

As ubiquitous sensing and big data analytics cut across various fields, knowledge can be distilled to understand daily activities and anticipate needs at collective and individual levels. Based on what we have learned from real-time and location-specific multisource data, citizens can be served responsively and proactively to address their needs in work, school, health care, recreation, mobility, personal security, entertainment, etc. Responsiveness at this level will require major breakthroughs in data intelligence theory, behavior sensing methodology, human service framework, and so on.

We welcome papers for this Special Issue that present the work and results from cross-cutting research. The work should involve fields including, but not limited to, information technology, planning, logistics, geography, and transportation. The content and scope of the issue will be on innovating and emerging technologies that center on humans and human needs. We expect that the papers selected for this Special Issue will provide valuable and meaningful contributions toward establishing a smart services framework in theory and methodology.

Prof. Zhixiang Fang
Prof. Lee D. Han
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 papers will be 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. Smart Cities is an international peer-reviewed open access quarterly 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 1000 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 intelligence theory for city scenarios
  • Intelligent multisource data integration for information process
  • Smart sensing of city
  • dynamics and functions
  • Smart, portable, and emerging services for city life
  • Visual analytic algorithms for smart city monitoring
  • Smart logistics or deliveries in city

Published Papers (7 papers)

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Research

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Open AccessArticle
Integration of Microclimate into the Multi-Agent System Simulation in Urban Public Space
Smart Cities 2019, 2(3), 421-432; https://doi.org/10.3390/smartcities2030026
Received: 1 June 2019 / Revised: 5 August 2019 / Accepted: 6 August 2019 / Published: 15 August 2019
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Abstract
Urban public space is the main place for human outdoor activities. Simulating human behavior through the Multi-Agent System (MAS) is one of the most important methods for studying public space. However, most of the research on this topic is concerned with the way [...] Read more.
Urban public space is the main place for human outdoor activities. Simulating human behavior through the Multi-Agent System (MAS) is one of the most important methods for studying public space. However, most of the research on this topic is concerned with the way people behave and the connection to spatial layouts. It ignores the fact that in outdoor spaces, microclimate factors tend to have a more important impact on human behavior. In this study, microclimate factors were narrowed down into two main factors: outdoor thermal perception and sunshine perception. Both of these factors are integrated into the traditional MAS, together with visual perception as the influence factors of agent action. The new MAS was developed in Processing language and can dynamically and visually show agents activities in the space. Taking Gulou Square in Nanjing, China, as a case, the simulations were carried out with three typical meteorological days of spring equinox, summer solstice, and winter solstice. Through comparing the simulation results at different times, we found that the new MAS exhibits a significant impact of microclimate on human behavior. The new MAS can be used reasonably and effectively for the design and evaluation of urban public space. Full article
(This article belongs to the Special Issue Big Data-Driven Intelligent Services in Smart Cities)
Open AccessArticle
Passenger Flow Prediction of Urban Rail Transit Based on Deep Learning Methods
Smart Cities 2019, 2(3), 371-387; https://doi.org/10.3390/smartcities2030023
Received: 28 May 2019 / Revised: 12 June 2019 / Accepted: 16 July 2019 / Published: 23 July 2019
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Abstract
The rapid development of urban rail transit brings high efficiency and convenience. At the same time, the increasing passenger flow also remarkably increases the risk of emergencies such as passenger stampedes. The accurate and real-time prediction of dynamic passenger flow is of great [...] Read more.
The rapid development of urban rail transit brings high efficiency and convenience. At the same time, the increasing passenger flow also remarkably increases the risk of emergencies such as passenger stampedes. The accurate and real-time prediction of dynamic passenger flow is of great significance to the daily operation safety management, emergency prevention, and dispatch of urban rail transit systems. Two deep learning neural networks, a long short-term memory neural network (LSTM NN) and a convolutional neural network (CNN), were used to predict an urban rail transit passenger flow time series and spatiotemporal series, respectively. The experiments were carried out through the passenger flow of Beijing metro stations and lines, and the prediction results of the deep learning methods were compared with several traditional linear models including autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), and space–time autoregressive integrated moving average (STARIMA). It was shown that the LSTM NN and CNN could better capture the time or spatiotemporal features of the urban rail transit passenger flow and obtain accurate results for the long-term and short-term prediction of passenger flow. The deep learning methods also have strong data adaptability and robustness, and they are more ideal for predicting the passenger flow of stations during peaks and the passenger flow of lines during holidays. Full article
(This article belongs to the Special Issue Big Data-Driven Intelligent Services in Smart Cities)
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Open AccessArticle
West Lake Tourist: A Visual Analysis System Based on Taxi Data
Smart Cities 2019, 2(3), 345-358; https://doi.org/10.3390/smartcities2030021
Received: 15 May 2019 / Revised: 20 June 2019 / Accepted: 21 June 2019 / Published: 2 July 2019
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Abstract
Mining the mobile pattern of the urban population plays an important role in city construction, and visual analysis is a powerful technique in studying mobile patterns. In this paper, based on the taxi trajectory data in Hangzhou, we share our design for an [...] Read more.
Mining the mobile pattern of the urban population plays an important role in city construction, and visual analysis is a powerful technique in studying mobile patterns. In this paper, based on the taxi trajectory data in Hangzhou, we share our design for an interactive visual analytic system, which helps analyzers leverage their domain knowledge to gain insight into travel patterns, including travel time rules of tourists and the distribution rules of pick-up and drop-off locations. Besides, our system can present the dynamic travel process and the Point of Interest (POIs) information of the origin and the destination. A case study has been conducted, which verifies that our system can provide tools for urban managers or urban experts on the design of scenic spot open entrances and exits and travel route planning. Full article
(This article belongs to the Special Issue Big Data-Driven Intelligent Services in Smart Cities)
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Open AccessArticle
Opportunities and Challenges for the Construction of a Smart City Geo-Spatial Framework in a Small Urban Area in Central China
Smart Cities 2019, 2(2), 245-258; https://doi.org/10.3390/smartcities2020016
Received: 12 May 2019 / Revised: 10 June 2019 / Accepted: 12 June 2019 / Published: 18 June 2019
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Abstract
In 2006, China lunched its first Digital City initiative to build a national geo-spatial framework. Over the past ten years, 511 county-cities benefited from the national initiative with funding and technical resources channeled from the central government. Has the initiative achieved its goals? [...] Read more.
In 2006, China lunched its first Digital City initiative to build a national geo-spatial framework. Over the past ten years, 511 county-cities benefited from the national initiative with funding and technical resources channeled from the central government. Has the initiative achieved its goals? How has the geo-spatial framework affected local governmental administration, public services, business operation, and the daily life of citizens? What lessons can be learned from the ten-year experience of digital city development? Answering these questions is of important policy, scholarly, and practical interest. The Digital City initiative set the foundation for building smart cities that China’s central government agencies and many local municipalities are currently pursuing. A review in retrospect of China’s digital city development helps inform future Smart City investment decisions and related policy making in the nation. Lessons learned from the Chinese experience are also valuable to cities in other countries. Full article
(This article belongs to the Special Issue Big Data-Driven Intelligent Services in Smart Cities)
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Open AccessArticle
People-Centric Service Intelligence for Smart Cities
Smart Cities 2019, 2(2), 135-152; https://doi.org/10.3390/smartcities2020010
Received: 22 February 2019 / Revised: 13 April 2019 / Accepted: 15 April 2019 / Published: 22 April 2019
Cited by 1 | PDF Full-text (6520 KB) | HTML Full-text | XML Full-text
Abstract
In the era of big data, smart cities have become a promising prospect for governments, citizens, and industrials. Many ideas and their derived systems for smart cities depend on big data for achieving a goal of data intelligence. However, there is an urgent [...] Read more.
In the era of big data, smart cities have become a promising prospect for governments, citizens, and industrials. Many ideas and their derived systems for smart cities depend on big data for achieving a goal of data intelligence. However, there is an urgent transformation trend from data intelligence to service intelligence in the vision of smart cities due to the living requirements of citizens. People-centric service intelligence in smart cities has to support the realization of people’s needs within urban and social domains. This paper introduces a concept of people-centric service intelligence, defines the level of it and its challenges in the aspect of infrastructure, human dynamics, human understanding and prediction, and the human–machine interface. Then, this paper proposes the theoretical framework and technical frameworks of people-centric service intelligence, and the service intelligence schemas for future construction of smart cities. It will be helpful for governments and industries to design people-centric service intelligence for improving the quality of life, the capabilities of good sustainability, and better development. Full article
(This article belongs to the Special Issue Big Data-Driven Intelligent Services in Smart Cities)
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Review

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Open AccessReview
Review on the Application of Artificial Intelligence in Smart Homes
Smart Cities 2019, 2(3), 402-420; https://doi.org/10.3390/smartcities2030025
Received: 31 May 2019 / Revised: 27 July 2019 / Accepted: 31 July 2019 / Published: 2 August 2019
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Abstract
Smart home and artificial intelligence technologies are developing rapidly, and various smart home products associated with artificial intelligence (AI) improved the quality of living for occupants. Although some studies discussed the application of artificial intelligence in smart homes, few publications fully considered the [...] Read more.
Smart home and artificial intelligence technologies are developing rapidly, and various smart home products associated with artificial intelligence (AI) improved the quality of living for occupants. Although some studies discussed the application of artificial intelligence in smart homes, few publications fully considered the integration of literature and products. In this paper, we aim to answer the research questions of “what is the trend of smart home technology and products” and “what is the relationship between literature and products in smart homes with AI”. Literature reviews and product reviews are given to define the functions and roles of artificial intelligence in smart homes. We determined the application status of artificial intelligence in smart home products and how it is utilized in our house so that we could understand how artificial intelligence is used to make smart homes. Furthermore, our results revealed that there is a delay between literature and products, and smart home intelligent interactions will become more and more popular. Full article
(This article belongs to the Special Issue Big Data-Driven Intelligent Services in Smart Cities)
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Other

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Open AccessLetter
Design of Remote Expert Evaluation System in Remote Mountainous Area Based on Cloud Service Platform
Smart Cities 2019, 2(3), 359-370; https://doi.org/10.3390/smartcities2030022
Received: 27 May 2019 / Revised: 11 July 2019 / Accepted: 12 July 2019 / Published: 22 July 2019
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Abstract
Faced with limited technical strength and resource protection requirements in remote mountainous areas, a remote expert evaluation system was designed based on a cloud service platform. On the basis of the analysis of system users, the remote expert evaluation system was structured, and [...] Read more.
Faced with limited technical strength and resource protection requirements in remote mountainous areas, a remote expert evaluation system was designed based on a cloud service platform. On the basis of the analysis of system users, the remote expert evaluation system was structured, and then the cloud platform service architecture and the system functions were designed. This cloud platform construction could enhance the informatization level of the planning review process and improve the efficiency and the scientific nature of the review process. Full article
(This article belongs to the Special Issue Big Data-Driven Intelligent Services in Smart Cities)
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