Smart Cities — Open Access Journal

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

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

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

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

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

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

Distributed Generations (DGs) have a productive capacity of tens of kilowatts to several megawatts, which are used to produce electrical energy at close proximity to consumers, which of the types of DGs can be named solar cells and Photovoltaics (PVs), fuel cells, micro turbines, wind power plants, and etc. If such kinds of power plants are connected to the network in optimal places, they will have several positive effects on the system, such as reducing network losses, improving the voltage profile, and increasing network reliability. The lack of optimal placement of DGs in the network will increase the costs of energy production and losses in transmission lines. Therefore, it is necessary to optimize the location of such DGs in the network so that the number of DGs, installation locations, and their capacity are determined to which the maximum reduction in network losses occurs. Besides, by applying an appropriate objective function, the evolutionary algorithm can find the optimal location of renewable units with respect to the constraints of the issue. In this paper, the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) algorithm are used to address the placement of wind and photovoltaic generators simultaneously in two states: With and without considering the effects of greenhouse gas emission. In this regard, first, an analytical method for optimal DG (wind and PV) placement is presented, then, the proposed approach is applied over a real study case, and the simulation carried out using the MATLAB program; hence, the placement problem was solved using GA and PSO and implemented in the IEEE 33-bus radial distribution system. The obtained results were compared and analyzed. The results of the simulation show the improvement of the voltage profile and the reduction of losses in the network. Full article

The concept of a smart city is becoming the leading paradigm worldwide. Consequently, a creative mix of emerging technologies and open innovation is gradually becoming the defining element of smart city evolution, changing the ways in which city administrators are organizing their services and development globally. Thus, the smart city concept is becoming extremely relevant on the agendas of policy-makers as a development strategy for enhancing the quality of life of the citizen and improving the sustainability goals of their cities. Despite of the relevance of the topic, still few studies investigate how open innovation shapes the way cities become smarter or focus on the experiences of professionals to understand the concept of a smart city and its implementation. This paper fills this gap and analyzes the processes for building effective smart cities by integrating the different perspectives of smart innovations and using the core components of smart cities according to a conceptual framework developed in previous research. In so doing, it provides useful insights for smart city stakeholders in adopting social and technological innovation to improve the global competitiveness of their cities. The empirical dataset allows examining how “smart cities” are being implemented in Manchester (UK), and in Boston, Massachusetts, and San Diego City (United States of America (USA)), including archival data and in-depth interviews with core smart city stakeholders who are involved in smart city projects and programs across the cases. Results from empirical data suggest that the conceptualization of smart cities across the cases is similar with a strong emphasis on social and technological innovation aimed at addressing municipal challenges in the core sub-systems of the cities, which include mobility, environmental sustainability, entrepreneurial development, quality of life, and social cohesion. The results also reveal benefits and challenges relating to smart innovation ecosystems across the cases and the future directions of their diffusion. Full article

Smart and sustainable cities are expected to form a cornerstone for achieving resource efficiency and sustainability worldwide. In this specific study we introduce a holistic framework for determining a repository of key performance indicators (KPIs) that are able to evaluate both business-as-usual and novel technologies and services related to smart city solutions. The framework includes six steps: (a) Clustering of the technology/service solutions into groups called Transition Tracks; (b) definition of the main groups of stakeholders; (c) definition of KPIs dimensions (or domains); (d) definition of KPIs repository per dimension; (e) definition of the scope of evaluation per KPI; and (f) threshold definition per KPI. The implementation of the proposed framework led to the development of a repository of 75 KPIs categorized in six dimensions (technical, environmental, economic, social, ICT and legal KPIs) with the corresponding levels of assessment and stakeholders’ group of interest. The proposed repository can serve as a great basis for similar projects to monitor and evaluate the performance of their solutions. Tips and guidance based on the actual implementation and lessons learned from a smart city project are provided. Full article

Policy decisions and urban governance are being influenced by an emergence of data from internet of things (IoT), which forms the backbone of Smart Cities, giving rise to Big Data which is processed and analyzed by Artificial Intelligence models at speeds unknown to mankind decades ago. This is providing new ways of understanding how well cities perform, both in terms of economics as well as in health. However, even though cities have been increasingly digitalized, accelerated by the concept of Smart Cities, the exploration of urban health has been limited by the interpretation of sensor data from IoT devices, omitting the inclusion of data from human anatomy and the emergence of biological data in various forms. This paper advances the need for expanding the concept of Big Data beyond infrastructure to include that of urban health through human anatomy; thus, providing a more cohesive set of data, which can lead to a better knowledge as to the relationship of people with the city and how this pertains to the thematic of urban health. Coupling both data forms will be key in supplementing the contemporary notion of Big Data for the pursuit of more contextualized, resilient, and sustainable Smart Cities, rendering more liveable fabrics, as outlined in the Sustainable Development Goal (SDG) 11 and the New Urban Agenda. Full article

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

Shared autonomous vehicle systems are anticipated to offer cleaner, safer, and cheaper mobility services when autonomous vehicles are finally implemented on the roads. The evaluation of people’s intentions regarding shared autonomous vehicle services appears to be critical prior to the promotion of this emerging mobility on demand approach. Based on a stated preference survey in Nagoya, Japan, the preference for shared autonomous vehicle services as well as willingness to pay for these services were examined among 1036 respondents in order to understand the relationship between people’s socioeconomic characteristics and their preferred shared autonomous vehicle services. For this purpose, k-modes clustering technique was selected and six clusters were obtained. Six groups with respect to different interests on shared autonomous vehicle services were clustered. The result of correlation analysis and discussion of willingness to pay on services provided insightful results for the future shared autonomous vehicle services. This study not only aids in revealing the demands of customer different clusters, but also states the prospective needs of users for stakeholders from research, policymaker and industry field, who are preparing to work on promoting shared autonomous vehicle systems, and subsequently, develops an optimum transportation mode by considering both demand and services as a whole. Full article

Various technological devices have been developed to meet the ever-increasing demand of today’s cities. Cities, as we can see today, have become highly technology-oriented. However, technological advancement comes at a cost which in this case is the environment of our planet. Therefore, it is necessary to design green cities, which impose as less harm as it possibly can to the environment. One of the most important characteristics of these new cities is the way of managing their waste. Traditional waste management, which employs various sizes and shapes of trash cans at multiple places that are collected by hand after each use is space-consuming, manual, inefficient and often leads to environmental pollution. Therefore, we require a novel approach that is free from these constraints. This paper proposes a garbage management system integrating the fundamental ideas of smart waste management and ‘Internet of Things’ (IoT). The proposed system employs smart cyclic containers that rotate one by one after being filled with rubbish like ‘Merry Go Round’. This approach of waste management solves the problem of space-constraint in an innovative way. A central server monitors the whole system, which further dispatches an autonomous car to collect the waste when necessary. This IoT integrated waste management system is named ‘Internet of Garbage Bins’ (IoGB). Full article

As a new area of science and technology (S&T), big data science and analytics embodies an unprecedentedly transformative power—which is manifested not only in the form of revolutionizing science and transforming knowledge, but also in advancing social practices, catalyzing major shifts, and fostering societal transitions. Of particular relevance, it is instigating a massive change in the way both smart cities and sustainable cities are understood, studied, planned, operated, and managed to improve and maintain sustainability in the face of expanding urbanization. This relates to what has been dubbed data-driven smart sustainable urbanism, an emerging approach that is based on a computational understanding of city systems that reduces urban life to logical and algorithmic rules and procedures, as well as employs a new scientific method based on data-intensive science, while also harnessing urban big data to provide a more holistic and integrated view and synoptic intelligence of the city. This paper examines the unprecedented paradigmatic and scholarly shifts that the sciences underlying smart sustainable urbanism are undergoing in light of big data science and analytics and the underlying enabling technologies, as well as discusses how these shifts intertwine with and affect one another in the context of sustainability. I argue that data-intensive science, as a new epistemological shift, is fundamentally changing the scientific and practical foundations of urban sustainability. In specific terms, the new urban science—as underpinned by sustainability science and urban sustainability—is increasingly making cities more sustainable, resilient, efficient, and livable by rendering them more measurable, knowable, and tractable in terms of their operational functioning, management, planning, design, and development. Full article

Smartphones are an integral cog of the IoT environment and a fundamental bloc of any related security solution, given that IoT mobile applications allow users not only to get information, but also to influence the environment. This paper presents a methodological instrument that can contribute to implementing and evaluating security measures in mobile applications by means of an automated analysis tool. A clear process for linking policy and high-level security guidelines and measures to concrete source code elements is depicted, as well as an automated way of testing a set of mobile applications against them. In addition, the obtained results highlight the current state of authentication measures’ implementation in IoT mobile applications; at the same time, it is important to note that the proposed approach is generic enough to accommodate other security principles as well. Full article

Mauritius, a small island developing state (SIDS), has an approved government-issued smart city framework geared at facilitating the development of smart cities through an application of Internet of things. In an attempt to move away from privately-operated urban developments in the form of newly built smart cities, an alternate framework has been proposed by Allam and Newman to redefine this timely concept for existing cities with the main dimensions being metabolism, culture, and governance. This new framework focuses on liveability and sustainability that builds on the cultural and historical dimensions of existing cities. This study adds to the redefined smart city paradigm by proposing a new pillar in the form of smart tourism for the city of Port Louis, Mauritius. This paper reviews models of smart tourism and explores how this can be grafted to the Allam and Newman’s smart city model. The findings of this study seek to inform policy makers on alternate and the more relevant economic potential of smart tourism for the city of Port Louis. Full article

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

Through the Internet of things (IoT), as promoted by smart cities, there is an emergence of big data accentuating the use of artificial intelligence through various components of urban planning, management, and design. One such system is that of artificial neural networks (ANNs), a component of machine learning that boasts similitude with brain neurological networks and its functioning. However, the development of ANN was done in singular fashion, whereby processes are rendered in sequence in a unidimensional perspective, contrasting with the functions of the brain to which ANN boasts similitude, and in particular to the concept of neuroplasticity which encourages unique complex interactions in self-learning fashion, thereby encouraging more inclusive urban processes and render urban coherence. This paper takes inspiration from Christopher Alexander’s Nature of Order and dwells in the concept of complexity theory; it also proposes a theoretical model of how ANN can be rendered with the same plastic properties as brain neurological networks with multidimensional interactivity in the context of smart cities through the use of big data and its emerging complex networks. By doing so, this model caters to the creation of stronger, richer, and more complex patterns that support Alexander’s concept of “wholeness” through the connection of overlapping networks. This paper is aimed toward engineers with interdisciplinary interest looking at creating more complex and intricate ANN models, and toward urban planners and urban theorists working on the emerging contemporary concept of smart cities. Full article

Spatial Data Infrastructures (SDIs) are frequently used to exchange 2D & 3D data, in areas such as city planning, disaster management, urban navigation and many more. City Geography Mark-up Language (CityGML), an Open Geospatial Consortium (OGC) standard has been developed for the storage and exchange of 3D city models. Due to its encoding in XML based format, the data transfer efficiency is reduced which leads to data storage issues. The use of CityGML for analysis purposes is limited due to its inefficiency in terms of file size and bandwidth consumption. This paper introduces XML based compression technique and elaborates how data efficiency can be achieved with the use of schema-aware encoder. We particularly present CityGML Schema Aware Compressor (CitySAC), which is a compression approach for CityGML data transaction within SDI framework. Our test results show that the encoding system produces smaller file size in comparison with existing state-of-the-art compression methods. The encoding process significantly reduces the file size up to 7–10% of the original data. Full article