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Smart Cities, Volume 2, Issue 3 (September 2019)

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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 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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Open AccessArticle
A Conflict-Detecting and Early-Warning System for Multi-Plan Integration in Small Cities and Towns Based on Cloud Service Platform
Smart Cities 2019, 2(3), 388-401; https://doi.org/10.3390/smartcities2030024
Received: 27 May 2019 / Revised: 27 June 2019 / Accepted: 22 July 2019 / Published: 1 August 2019
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Abstract
Multi-plan integration (MPI) is a major effort initiated by China’s State Council for the purpose of streamlining development plans made by various public agencies in provincial and city governments. Small cities and towns are facing challenges to achieve MPI goals due to lack [...] Read more.
Multi-plan integration (MPI) is a major effort initiated by China’s State Council for the purpose of streamlining development plans made by various public agencies in provincial and city governments. Small cities and towns are facing challenges to achieve MPI goals due to lack of technological infrastructure and professional expertise. This article presents a system to assist small cities and towns to carry out their MPI tasks. The system, named conflict-detecting and early-warning for MPI (CDEW4MPI) is developed based on a cloud service platform. CDEW4MPI enables small cities and towns in remote locations to detect inconsistency and conflicts among multiple plans. The system includes two modules. One is conflict-detecting, which identifies spatial conflicts in boundary designation among different plans from different agencies. The other is early-warning, which analyzes and reports potential encroachment of proposed local plans to urban growth boundary, the baseline for ecological protection, and the farmland under permanent preservation. CDEW4MPI was implemented as a demo project in Shennongjia Forestry District, a municipality in the western mountainous region of Hubei Province, China. The paper presents the design of CDEW4MPI and its implementation in Shennongjia and draws lessons from the Shennongjia case for broad interests in smart management of spatial resources. Full article
(This article belongs to the Special Issue Smart Cities and Data-driven Innovative Solutions)
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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 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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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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