Smart Cities

Nowadays, modeling tools are a crucial part of best practice in the elaboration and implementation of a decarbonization plan in any organization, city, or country. The present review analyzes the different modeling tools available to assess energy systems in smart cities. It creates an updated overview of the modeling tools currently available, showing their capabilities and main potential outputs when considering the energy efficiency objective in the context of smart cities in Europe. A restricted set of 14 tools are identified which optimally fulfill the modeling mission of the energy sector, in a smart city context, for different time horizons. The selection considers the capability to include decarbonization assessments, namely, by considering the flexibility to use different external factors, energy policies, technologies, and mainly the implementation of Article 7 from the Energy Efficiency Directive and the “energy efficiency first” principle defined by the European Commission. The ELECTRE TRI method was used to implement a multi-criteria decision approach for sorting modeling tools, aiming at distributing the various alternatives by previously defined categories, and considering the performance criteria of each alternative modeling tool, the analysis suggests that the best options are the LEAP, MESSAGEix, and oemof tools. Full article

The rapid expansion of urbanization both in scale and population leads to a series of serious urban diseases, which become a huge obstacle to the healthy and sustainable development of cities. To alleviate these problems and challenges, China launched a smart city construction program in the past decade and has taken the lead in smart city construction in the world. However, there is still a lack of reflection and summary on the practice of smart cities in China. Based on the definition and concept of smart city, this paper points out the internal and external driving factors of China’s smart city development, then summarizes the four major characteristics of China’s smart city construction practice, and explores the main problems existing in the process of China’s smart city construction. Through the reflection and summary, we can facilitate development of smart cities in China, provide useful reference to urban planners and smart city practitioners in other countries and regions, and promote the healthy and sustainable development of cities. Full article

The number of drivers using parking facilities (parking demand) in downtown Pittsburgh is highly variable throughout business operating hours, which makes an efficient operation of parking facilities challenging and results in congestion around the facilities. In this study, we applied an event-based ordinary least squares (OLS) regression model to the parking data set provided from one of the parking facilities, the Theater Square Garage in downtown Pittsburgh. We demonstrated that our model achieved a high R-squared value during time periods when parking demand is highly variable. Using the model, we revealed the dynamic (time-dependent) impact of theater performances and sports events on parking demand. This dynamic information can help facility managers appropriately adjust their operating settings (e.g., the number of staff and fee structure) during surge or vacant time periods accordingly. This model is applicable to various businesses in downtown areas that have increased customer flow from theater performances and sports events, not only parking garages. Full article

Cities are becoming increasingly complex to manage, as they increase in size and must provide higher living standards for their populations. New technology-based solutions must be developed towards attending this growth and ensuring that it is socially sustainable. This paper puts forward the notion that these solutions must share some properties: they should be anthropocentric, holistic, horizontal, multi-dimensional, multi-modal, and predictive. We propose an architecture in which streaming data sources that characterize the city context are used to feed a real-time graph of the city’s assets and states, as well as to train predictive models that hint into near future states of the city. This allows human decision-makers and automated services to take decisions, both for the present and for the future. To achieve this, multiple data sources about a city were gradually connected to a message broker, that enables increasingly rich decision-support. Results show that it is possible to predict future states of a city, in aspects such as traffic, air pollution, and other ambient variables. The key innovative aspect of this work is that, as opposed to the majority of existing approaches which focus on a real-time view of the city, we also provide insights into the near-future state of the city, thus allowing city services to plan ahead and adapt accordingly. The main goal is to optimize decision-making by anticipating future states of the city and make decisions accordingly. Full article

A large number of smart city logistics projects fail to scale up, remaining a local experimental exercise. This lack of scalability is, in fact, commonly recognized as a major problem. This study aims to determine the key success factors related to the scalability of smart city logistics projects. The process of scaling up, which is articulated as expansion, roll-out, and replication, is defined as the ability of a system to improve its scale by aiming to meet the increasing volume demand. Specifically, this study investigates the scalability intended to be used as expansion and roll-out. A qualitative case study was conducted to fulfill the research purpose. The chosen case study is SMOOTh, a pilot project currently underway in the city of Gothenburg, Sweden, involving a diverse group of companies including Volvo Group and DHL. Semi-structured interviews were conducted with seven of the project’s stakeholders. Through a thematic analysis, four categories and the respective success factors were identified. These were represented by a business model, as well as technical, stakeholder and regulatory factors. The paper concludes with observations and recommendations aimed at the pilot initiatives, adding new perspectives to the upscaling debate. Full article

Insomnia is the most common sleep disorder worldwide. Its effects generate economic costs in the millions but could be effectively reduced using digitally provisioned cognitive behavioural therapy. However, traditional acquisition and maintenance of the necessary technical infrastructure requires high financial and personnel expenses. Sleep analysis is still mostly done in artificial settings in clinical environments. Nevertheless, innovative IT infrastructure, such as mHealth and cloud service solutions for home monitoring, are available and allow context-aware service provision following the Smart Cities paradigm. This paper aims to conceptualise a digital, cloud-based platform with context-aware data storage that supports diagnosis and therapy of non-organic insomnia. In a first step, requirements needed for a remote diagnosis, therapy, and monitoring system are identified. Then, the software architecture is drafted based on the above mentioned requirements. Lastly, an implementation concept of the software architecture is proposed through selecting and combining eleven cloud computing services. This paper shows how treatment and diagnosis of a common medical issue could be supported effectively and cost-efficiently by utilising state-of-the-art technology. The paper demonstrates the relevance of context-aware data collection and disease understanding as well as the requirements regarding health service provision in a Smart Cities context. In contrast to existing systems, we provide a cloud-based and requirement-driven reference architecture. The applied methodology can be used for the development, design, and evaluation of other remote and context-aware diagnosis and therapy systems. Considerations of additional aspects regarding cost, methods for data analytics as well as general data security and safety are discussed. Full article

This paper presents an investigation of the capacity of machine learning methods (ML) to localize leakage in water distribution systems (WDS). This issue is critical because water leakage causes economic losses, damages to the surrounding infrastructures, and soil contamination. Progress in real-time monitoring of WDS and ML has created new opportunities to develop data-based methods for water leak localization. However, the managers of WDS need recommendations for the selection of the appropriate ML methods as well their practical use for leakage localization. This paper contributes to this issue through an investigation of the capacity of ML methods to localize leakage in WDS. The campus of Lille University was used as support for this research. The paper is presented as follows: First, flow and pressure data were determined using EPANET software; then, the generated data were used to investigate the capacity of six ML methods to localize water leakage. Finally, the results of the investigations were used for leakage localization from offline water flow data. The results showed excellent performance for leakage localization by the artificial neural network, logistic regression, and random forest, but there were low performances for the unsupervised methods because of overlapping clusters. Full article

This paper presents and discusses the role of the Internet of Things (IoT) and crowdsourcing in constructing smart cities. The literature review shows an important and increasing concern of the scientific community for these three issues and their association as support for urban development. Based on an extensive literature review, the paper first presents the smart city concept, emphasizing smart city architecture and the role of data in smart city solutions. The second part presents the Internet of Things, focusing on IoT technology, the use of IoT in smart city applications, and security. Finally, the paper presents crowdsourcing with particular attention to mobile crowdsourcing and its role in smart cities. The paper shows that IoT and crowdsourcing have a crucial role in two fundamental layers of smart city applications, namely, the data collection and services layers. Since these two layers ensure the connection between the physical and digital worlds, they constitute the central pillars of smart city projects. The literature review also shows that the smart city development still requires stronger cooperation between the smart city technology-centered research, mainly based on the IoT, and the smart city citizens-centered research, mainly based on crowdsourcing. This cooperation could beneficiate in recent developments in the field of crowdsensing that combines IoT and crowdsourcing. Full article

Smart cities are a natural evolution of the concept of sustainable cities. These cities can be analyzed by social, economic, environmental, and technological biases. For this work, we chose the social and economic vision, with a special focus on the poorest and most vulnerable territories of Brazilian cities. These territories in Brazil are called slums, places of poverty but with opportunities for the development of the creative economy with its own brand. Seen by many in a simplistic way, summed up to be geographic spaces of drug circulation dominated by trafficking, Brazilian favelas have been consolidating themselves as a storehouse of innovative minds, a creative territory with multiple and complex cultures. These places today are capable of producing a positive image with potential for market exploitation. Therefore, the objective was to draw a relationship between the creative economy, branding and favelas, considering the concept of smart cities that include products and services from the slums. The present study shows the results of a survey and a bibliographic analysis based on the methodology Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and with parameters that took into account the favela, branding and the creative economy. Thus, we expect that it will be possible to point out ways to accelerate entrepreneurial actions and foster the development of these locations. Full article

Nowadays, the growth of traffic congestion and emissions has led to the emergence of an innovative and sustainable transportation service, called dynamic vanpooling. The main aim of this study is to identify factors affecting the travel behavior of passengers due to the introduction of dynamic vanpooling in the transportation system. A web-based mode choice survey was designed and implemented for this scope. The stated-preference experiments offered respondents binary hypothetical scenarios with an ordered choice between dynamic vanpool and the conventional modes of transport, private car and public transportation. In-vehicle travel time, total travel cost and walking and waiting time or searching time for parking varies across the choice scenarios. An ordered probit model, a multinomial logit model and two binary logit models were specified. The model estimation results indicate that respondents who are aged between 26 and 35 years old, commute with PT or are members of bike-sharing services were significantly more likely to choose dynamic vanpool or PT than private car. Moreover, respondents who are worried about climate change and are willing to spend more for environmentally friendly products are significantly more likely to use dynamic vanpool in comparison with private cars. Finally, to indicate the model estimation results for dynamic vanpool, the value of in-vehicle travel time is found to be 12.2€ per hour (13.4€ for Munich subsample). Full article

Floods are one of the most fatal and devastating disasters, instigating an immense loss of human lives and damage to property, infrastructure, and agricultural lands. To cater to this, there is a need to develop and implement real-time flood management systems that could instantly detect flooded regions to initiate relief activities as early as possible. Current imaging systems, relying on satellites, have demonstrated low accuracy and delayed response, making them unreliable and impractical to be used in emergency responses to natural disasters such as flooding. This research employs Unmanned Aerial Vehicles (UAVs) to develop an automated imaging system that can identify inundated areas from aerial images. The Haar cascade classifier was explored in the case study to detect landmarks such as roads and buildings from the aerial images captured by UAVs and identify flooded areas. The extracted landmarks are added to the training dataset that is used to train a deep learning algorithm. Experimental results show that buildings and roads can be detected from the images with 91% and 94% accuracy, respectively. The overall accuracy of 91% is recorded in classifying flooded and non-flooded regions from the input case study images. The system has shown promising results on test images belonging to both pre- and post-flood classes. The flood relief and rescue workers can quickly locate flooded regions and rescue stranded people using this system. Such real-time flood inundation systems will help transform the disaster management systems in line with modern smart cities initiatives. Full article

A smart city is an urbanization region that collects data using several digital and physical devices. The information collected from such devices is used efficiently to manage revenues, resources, and assets, etc., while the information obtained from such devices is utilized to boost performance throughout the city. Cloud-based Internet of Things (IoT) applications could help smart cities that contain information gathered from citizens, devices, homes, and other things. This information is processed and analyzed to monitor and manage transportation networks, electric utilities, resources management, water supply systems, waste management, crime detection, security mechanisms, proficiency, digital library, healthcare facilities, and other opportunities. A cloud service provider offers public cloud services that can update the IoT environment, enabling third-party activities to embed IoT data within electronic devices executing on the IoT. In this paper, the author explored cloud-based IoT applications and their roles in smart cities. Full article

One of the important aspects of realizing smart cities is developing smart homes/buildings and, from the energy perspective, designing and implementing an efficient smart home area energy management system (HAEMS) is vital. To be effective, the HAEMS should include various electrical appliances as well as local distributed/renewable energy resources and energy storage systems, with the whole system as a microgrid. However, the collecting and processing of the data associated with these appliances/resources are challenging in terms of the required sensors/communication infrastructure and computational burden. Thanks to the internet-of-things and cloud computing technologies, the physical requirements for handling the data have been provided; however, they demand suitable optimization/management schemes. In this article, a HAEMS is developed using cloud services to increase the accuracy and speed of the data processing. A management protocol is proposed that provides an optimal schedule for a day-ahead operation of the electrical equipment of smart residential homes under welfare indicators. The proposed system comprises three layers: (1) sensors associated with the home appliances and generation/storage units, (2) local fog nodes, and (3) a cloud where the information is processed bilaterally with HAEMS and the hourly optimal operation of appliances/generation/storage units is planned. The neural network and genetic algorithm (GA) are used as part of the HAEMS program. The neural network is used to predict the amount of workload corresponding to users’ requests. Improving the load factor and the economic efficiency are considered as the objective function that is optimized using GA. Numerical studies are performed in the MATLAB platform and the results are compared with a conventional method. Full article

Against the widespread assumption that data are the oil of the 21st century, this article offers an alternative conceptual framework, interpretation, and pathway around data and smart city nexus to subvert surveillance capitalism in light of emerging and further promising practical cases. This article illustrates an open debate in data governance and the data justice field related to current trends and challenges in smart cities, resulting in a new approach advocated for and recently coined by the UN-Habitat programme ‘People-Centred Smart Cities’. Particularly, this feature article sheds light on two intertwined notions that articulate the technopolitical dimension of the ‘People-Centred Smart Cities’ approach: data co-operatives and data sovereignty. Data co-operatives are emerging as a way to share and own data through peer-to-peer (p2p) repositories and data sovereignty is being claimed as a digital right for communities/citizens. Consequently, this feature article aims to open up new research avenues around ‘People-Centred Smart Cities’ approach: First, it elucidates how data co-operatives through data sovereignty could be articulated as long as co-developed with communities connected to the long history and analysis of the various forms of co-operatives (technopolitical dimension). Second, it prospectively anticipates the city–regional dimension encompassing data colonialism and data devolution. Full article

Over the years, the digitization of all aspects of life in modern societies is considered an acquired advantage. However, like the terrestrial world, the digital world is not perfect and many dangers and threats are present. In the present work, we conduct a systematic review on the methods of network detection and cyber attacks that can take place in a critical infrastructure. As is shown, the implementation of a system that learns from the system behavior (machine learning), on multiple levels and spots any diversity, is one of the most effective solutions. Full article

Many of us have experienced this when driving in the city, we had to “turn for hours” to find a parking space. To attenuate this drawback, this paper proposes a collaborative parking search strategy based on the reality of a drivers network in which, depending on his/her role at a given time, each one can help the others to find public on-street parking spots. To highlight a situation of parking spot scarcity, we assume the extreme case where there is no free space and the only solution to park is to swap with a parked car that leaves its location. Under this assumption, agent-based modelling and simulations allow to compare the ReadyPark solution with the classical approach that consists, by luck, in taking the place of a nearby car that is leaving its spot. Full article

This paper aims to examine the determinants of smart-city commitment across individuals from Bahía Blanca, Argentina. Literature has identified different factors explaining citizens’ commitment to smart cities, such as education, age, labor condition, and other more subjective factors, such as trust and awareness about the smart-city concept. A mediator factor of smart commitment is e-readiness or digital readiness, that is, the level of preparedness to properly exploit internet opportunities such as e-government and e-commerce. To achieve this goal, we used a survey conducted on 97 citizens (followers of the Moderniza Bahía Facebook) from the city of Bahía Blanca, Argentina. By estimating a structural equation model, we found that higher levels of ICT use are associated with higher levels of smart-city commitment and that higher awareness of the smart-city concept is related to higher levels of smart-city commitment. Sociodemographic factors such as age and labor condition also explain ICT use. Full article

Millions of users post comments to TripAdvisor daily, together with a numeric evaluation of their experience using a rating scale of between 1 and 5 stars. At the same time, inspectors dispatched by national and local authorities visit restaurant premises regularly to audit hygiene standards, safe food practices, and overall cleanliness. The purpose of our study is to analyze the use of online-generated reviews (OGRs) as a tool to complement official restaurant inspection procedures. Our case study-based approach, with the help of a Python-based scraping library, consists of collecting OGR data from TripAdvisor and comparing them to extant restaurants’ health inspection reports. Our findings reveal that a correlation does exist between OGRs and national health system scorings. In other words, OGRs were found to provide valid indicators of restaurant quality based on inspection ratings and can thus contribute to the prevention of foodborne illness among citizens in real time. The originality of the paper resides in the use of big data and social network data as a an easily accessible, zero-cost, and complementary tool in disease prevention systems. Incorporated in restaurant management dashboards, it will aid in determining what action plans are necessary to improve quality and customer experience on the premises. Full article

Based on the constant need for safety and operational cost optimization, the air-cargo industry is continually evolving in the context of Industry 4.0. Used wisely, data can help the industry to provide critical resilience that will allow authorities to take proper measures/actions in response to unexpected disasters and secure societal protection. The “INTELLICONT” project combines state-of-the-art technologies blended with novel solutions to improve the loading/unloading time, the structural status awareness, and the safety and security of the air-cargo related operations (prior to, during, and after the flight), as well as to enhance their capabilities related to the execution of their duties. The suggested system is contextually aligned and harmonized with the existing international and EU regulations. In the present work, the remote monitoring and control system for intelligent aircraft cargo containers have been presented from the software perspective. The intelligent containers integrate three types of sensors, Structural Health Monitoring, fire suppression, and locking status indication. The focus has been given to the design and development of a Human Machine Interface (HMI) capable to visualize all related data for better and safer control of the aircraft cargo. It is shown that the system can contribute to making the air transportations safer, environmentally friendlier, faster and with the lowest possible cost. Full article