Smart Cities, Volume 6, Issue 3 (June 2023) – 18 articles

With the rapid development of deep learning technology in recent years, convolutional neural networks have gained remarkable progress in SAR ship detection tasks. However, noise interference of the background and inadequate appearance features of small-scale objects still pose challenges. To tackle these issues, we propose a small ship detection algorithm for SAR images by means of a coordinate-aware mixed attention mechanism and spatial semantic joint context method. First, the coordinate-aware mixed attention mechanism innovatively combines coordinate-aware channel attention and spatial attention to achieve coordinate alignment of mixed attention features. In this way, attention with finer spatial granularity is conducive to strengthening the focusing ability on small-scale objects, thereby suppressing the background clutters accurately. In addition, the spatial semantic joint context method exploits the local and global environmental information jointly. The detailed spatial cues contained in the multi-scale local context and the generalized semantic information encoded in the global context are used to enhance the feature expression and distinctiveness of small-scale ship objects. Extensive experiments are conducted on the LS-SSDD-v1.0 and the HRSID dataset. The results with an average precision of 77.23% and 90.85% on the two datasets show the effectiveness of the proposed methods. Full article

Recently, the rapid climate change caused by increasing CO₂ emissions has become a global concern. Efficient transportation systems are necessary to reduce CO₂ emissions in cities. Taxi services are an essential part of the transportation system, both in urban areas with high demand and in rural areas with inadequate public transportation. Inefficient taxi services cause problems such as increased idle times, resulting in increased CO₂ emissions. This study proposes a taxi allocation model that minimizes taxi idle time costs for efficient taxi service operation. We also propose three heuristic algorithms to solve the proposed model. At last, we conduct a case study by using real taxi data in Nagaoka, Japan. By comparing the three algorithms, the dynamic greedy algorithm produced the best result in terms of idle time cost and CPU time. The findings indicate that by minimizing idle time costs and reducing the number of taxis, it is possible to achieve a significant 81.84% reduction in CO₂ emissions within the transportation sector. Further, in order to estimate the idle time costs the sensitivity of demand is considered. Full article

Seaports are at the forefront of global trade networks, serving as hubs for maritime logistics and the transportation of goods and people. To meet the requirements of such networks, seaport authorities are investing in advanced technologies to enhance the efficiency and reliability of port infrastructures. This can be achieved through the digitalization and automation of core systems, aimed at optimizing the management and handling of both goods and people. Furthermore, a significant effort is being made towards a green energy transition at seaports, which can be supported through marine renewable sources. This promotes energy-mix diversification and autonomy, whilst reducing the noteworthy environmental footprint of seaport activities. By analyzing these pertinent topics under the scope of a review of container-terminal case studies, and these ports’ respective contexts, this paper seeks to identify pioneering smart seaports in the fields of automation, real-time management, connectivity and accessibility control. To foster the sustainable development of seaports, from an energy perspective, the potential integration with marine renewable-energy systems is considered, as well as their capabilities for meeting, even if only partially, the energy demands of seaports. By combining these fields, we attempt to construct a holistic proposal for a “model port” representing the expected evolution towards the seaports of the future. Full article

Shared mobility is developing at a very fast pace around the world, becoming an alternative to classic forms of travel and, according to the public, providing innovative services. In recent years, these innovative services have also gained wide interest among scientists from a multicriteria point of view. However, among the topics and reviews in the literature, no review paper considering shared mobility in terms of innovation was identified. This article’s research objective was to indicate the perception of innovation in shared mobility in scientific works. The results indicate that innovations in shared mobility are a niche topic considered in few scientific works. What is more, in most cases, shared mobility services are perceived as innovative in themselves without detailed service analysis. Moreover, the issues of open innovation, which are closely related to the concept of accessible Mobility as a Service system and smart cities, are often overlooked. In addition, there was no work identified that fully referred to all areas of innovative service. The article supports researchers in the determination of further research directions in the field of shared mobility and fills the research gap in the field of knowledge about open innovation, especially in the context of the development of shared mobility services in smart cities. Full article

This study aims to explore the challenges and issues in adopting cybersecurity practices in smart Saudi cities and to develop and validate a newly developed cybersecurity-based unified theory of acceptance and use of technology 3 (UTAUT3) model. The study has a twofold purpose. First, it identified the key challenges and issues in adopting smart cities in Saudi smart cities. Second, it developed a technology-based model to adopt cybersecurity practices in Saudi smart cities. Two surveys were conducted to achieve these objectives. The first survey identified challenges and gaps in adopting cybersecurity practices in smart cities, revealing concerns about weak cybersecurity platforms, privacy breaches, and the impact of IT infrastructure advancements on Saudi culture (N = 554: common public). The second survey focused on developing and validating a cybersecurity-based UTAUT3 model (N = 108: IT professionals), emphasizing nine factors: performance expectancy, effort expectancy, social influence, facilitating conditions, safety, resiliency, availability, confidentiality, and integrity of cybersecurity. The model’s validity and reliability were assessed, demonstrating its potential for understanding user behavior and adoption patterns in smart cities. The study findings provide valuable insights into the factors influencing the adoption of cybersecurity measures in smart Saudi cities, highlighting the need for targeted strategies, effective awareness programs, and collaboration between stakeholders to promote a secure and resilient digital environment. Future research may focus on refining the model, extending its applicability to other regions or countries, and investigating the impact of emerging technologies and evolving cyber threats on user behavior and cybersecurity practices. Full article

Massive human population, coupled with rapid urbanization, results in a substantial amount of garbage that requires daily collection. In urban areas, garbage often accumulates around dustbins without proper disposal at regular intervals, creating an unsanitary environment for humans, plants, and animals. This situation significantly degrades the environment. To address this problem, a Smart Waste Management System is introduced in this paper, employing machine learning techniques for air quality level classification. Furthermore, this system safeguards garbage collectors from severe health issues caused by inhaling harmful gases emitted from the waste. The proposed system not only proves cost-effective but also enhances waste management productivity by categorizing waste into three types: wet, dry, and metallic. Ultimately, by leveraging machine learning techniques, we can classify air quality levels and garbage weight into distinct categories. This system is beneficial for improving the well-being of individuals residing in close proximity to dustbins, as it enables constant monitoring and reporting of air quality to relevant city authorities. Full article

Compared with administrative cities, natural cities can be generally referred to as the areas generated based on the density of different urban facilities (e.g., point of interest, road network, etc.). To some extent, natural cities are outperformed in some related urban studies, such as urban living structure analysis. Nevertheless, traditional ways of generating natural cities are mostly limited to the planar space. Modern cities such as Hong Kong are vertical cities with high buildings, 3D road networks and land uses. Therefore, traditional nature cities could be biased when applied to 3D cities. In this work, a 3D road network in Hong Kong is adopted to extract true road intersections and generate modified natural cities to explore urban living structures. Numerous living structure units are classified into two parts: tiny and serried ones representing natural cities and vast ones representing rural areas. The classification method applies head/tail breaks, and a clustering algorithm was fitted for heavy-tailed distribution. According to the living structure theory, the living structures of the proposed natural cities and traditional natural cities based on the same road network in Hong Kong are compared. The findings show that the distribution of modified natural city regions is more reasonable compared with typical ones. The improved model will more clearly show the inherent living structure of the city and will allow an analysis of the relationship between the part and wholeness of the city. Full article

Shipboard microgrids (SBMGs) are becoming increasingly popular in the power industry due to their potential for reducing fossil-fuel usage and increasing power production. However, operating SBMGs poses significant challenges due to operational and environmental constraints. To address these challenges, intelligent control, management, and protection strategies are necessary to ensure safe operation under complex and uncertain conditions. This paper provides a comprehensive review of SBMGs, including their classifications, control, management, and protection, as well as the most recent research statistics in these areas. The state-of-the-art SBMG types, propulsion systems, and power system architectures are discussed, along with a comparison of recent research contributions and issues related to control, uncertainties, management, and protection in SBMGs. In addition, a bibliometric analysis is performed to examine recent trends in SBMG research. This paper concludes with a discussion of research gaps and recommendations for further investigation in the field of SBMGs, highlighting the need for more research on the optimization of SBMGs in terms of efficiency, reliability, and cost-effectiveness, as well as the development of advanced control and protection strategies to ensure safe and stable operation. Full article

Collecting, storing, and providing access to Internet of Things (IoT) data are fundamental tasks to many smart city projects. However, developing and integrating IoT systems is still a significant barrier to entry. In this work, we share insights on the development of cloud data storage and visualization tools for IoT smart city applications using flood warning as an example application. The developed system incorporates scalable, autonomous, and inexpensive features that allow users to monitor real-time environmental conditions, and to create threshold-based alert notifications. Built in Amazon Web Services (AWS), the system leverages serverless technology for sensor data backup, a relational database for data management, and a graphical user interface (GUI) for data visualizations and alerts. A RESTful API allows for easy integration with web-based development environments, such as Jupyter notebooks, for advanced data analysis. The system can ingest data from LoRaWAN sensors deployed using The Things Network (TTN). A cost analysis can support users’ planning and decision-making when deploying the system for different use cases. A proof-of-concept demonstration of the system was built with river and weather sensors deployed in a flood prone suburban watershed in the city of Charlottesville, Virginia. Full article

Consistent and reliable information on passenger traffic is considered crucial for the efficient operation of the public transport (PT) network. The PT network is used to improve public services and thus attract more passengers. This study evaluated the passenger traffic in Odesa, Ukraine, due to the inefficient urban transport system. The main aim of this study was to make PT better by examining passenger distribution on traffic routes and specifying characteristics of PT travel influencing individual satisfaction. The metric-tabular method was used to collect data and examine the number of incoming and outgoing passengers at each bus stop. The results of the passenger and PT analysis provide valuable recommendations for optimizing future routes. It is beneficial for transport companies to implement such recommendations so that inefficient transport on the route can be reduced by either reforming the route network or choosing the optimal number of buses. According to the findings of this study, understanding PT services is the most important determinant of PT adoption. The main implications of the findings are of particular interest to policymakers who develop policies in the field of passenger transport and also to transport scientists and students. Full article

This paper presents an initiative in which QR codes on public transport are used to provide citizens with books that they can read and that will improve their general knowledge. It builds on the concept of the learning city and combines it with smart city tools. This paper aims to use a descriptive–empirical approach, including an experiment in Bucharest. This research aims to contribute to the academic world, urban sociology, public administration, and lifelong learning education. Full article

With their exponentially rising computational power, digital platforms are heralding a new era of hybrid intelligence. There has recently been much enthusiasm and hype that the Metaverse has the potential to unlock hybrid intelligence. This is premised on the idea that the Metaverse represents an applied convergence of Artificial Intelligence of Things (AIoT) and Extended Reality (XR) that intersects with urbanism in terms of the distinctive features of platform-mediated everyday life experiences in cities. However, social interaction and its resulting social organization in the Metaverse are mediated and governed by algorithms and thus submitted to—a dream of—complete logical ordering. This raises a plethora of concerns related to the systemic collection and algorithmic processing of users’ personal, brain, and biometric data, i.e., profound societal—and the hardest to predict ethical—implications. Therefore, this study analyzes and synthesizes a large body of scientific literature on the unfolding convergence of AIoT and XR technologies, neurotechnology, and nanobiotechnology in the realm of the Metaverse in order to derive a novel conceptual framework for the Metaverse as an envisioned virtual model of platform urbanism. Further, it examines the key challenges and risks of these converging technologies in relation to the Metaverse and beyond. This study employs thematic analysis and synthesis to cope with multidisciplinary literature. The analysis identifies seven themes: (1) Platformization, (2) platform urbanism, (3) virtual urbanism, (4) XR technologies, (5) AIoT technologies, (6) neurotechnology, and (7) nanobiotechnology. The synthesized evidence reveals that, while neurotechnology and nanobiotechnology have numerous benefits and promising prospects, they raise contentions and controversies stemming from their potential use to inflict harm to human users—if left unchecked—through the black box of the algorithmic mediation underpinning the Metaverse. The findings serve to steer the Metaverse to contribute to human flourishing and wellbeing by adhering to and upholding ethical principles as well as leveraging its underlying disruptive technologies in meaningful ways. They also aid scholars, practitioners, and policymakers in assessing the pros and cons of these technologies, especially their inevitable ramifications. Full article

Smart cities are required to be effectively and efficiently managed in order to ensure the desired level of sustainability and quality of life for all inhabitants. This is a particularly difficult challenge in crisis situations of considerable scale and intensity (for example, the COVID-19 pandemic, armed conflicts, social tensions). For this reason, the aim of this article is to identify the attitudes and perceptions of risk by city administration employees combined with an assessment of their impact on the consequences of risk (the implementation of internal and external threats). The analyses used the results of a survey conducted on a representative sample of 399 Polish municipal offices, as well as descriptive statistics and structural equation modeling. The obtained results show that: (1) employees of municipal offices negatively perceive risk and are aware of its destructive impact on the organization, but are reluctant to report the risks; (2) individual and collective measures are taken in offices to protect against risks, but employees are not always encouraged to report potential sources of risk (rarely in the form of an informal conversation and even more rarely in a systemic form); (3) for the most part, employees are aware that internal and external risks have a negative impact on the operation of municipal offices; (4) the consequences of risks are more strongly influenced by employees’ perceptions of risk than by individual, team, and systemic attitudes toward risk. The added value of the research presented in this article comes from diagnosing the behavioral aspects of urban risk management and assessing the impact of attitudes toward risks and risk perceptions (internal and external) in a broad, representative range. Full article

Dynamic pricing is frequently used in online marketplaces, ticket sales, and booking systems. The commercial principles of dynamic pricing systems are often kept secret; however, their application causes complex changes in human behavior. Thus, a scientific tool is needed to evaluate and predict the impact of dynamic pricing strategies. Publications in the field lack a common quality evaluation methodology, public data, and source code, making them difficult to reproduce. In this paper, a data-driven method, DPRank, for evaluating dynamic pricing systems is proposed. DPRank first builds a surrogate price elasticity of demand model using public data generated by a hidden dynamic pricing model, and then applies the surrogate model to build an exposed dynamic pricing model. The hidden and exposed dynamic pricing models were then systematically compared in terms of quality using a Monte Carlo simulation in terms of a company’s revenue. The effectiveness of the proposed method was tested on the dataset collected from the website of a Russian railway passenger carrier company. Depending on the train type, the quality difference between the hidden and exposed models can vary by several dozen percent on average, indicating the potential for improving the existing (hidden) company’s dynamic pricing model. Full article

Floods generated by rain cause significant economic and human losses. The campus of the Escuela Superior Politécnica del Litoral (ESPOL) has a drainage system that conducts stormwater to two discharge points outside the campus. The system works effectively at the macro-drainage level. However, a very crowded area is deficient at the micro-drainage level, which has registered flooding and the proliferation of vectors that affect people’s health. This work aimed to design a masterplan for stormwater sewerage by analyzing the existing situation and applying technical criteria that allow the establishment of solutions and strategies to control floods at the university campus. The methodology consisted of: (i) data collection and processing for the stormwater drainage system diagnosis; (ii) a design proposal for micro-drainage and (iii) a SWOT analysis to propose improvement strategies in water management. The resulting flows for return periods of 5 years, 10 years, and 25 years are 9.67 m³/s, 11.85 m³/s, and 15.85 m³/s, respectively. In the latter, as the most critical area (presence of flooding), the implementation of a trapezoidal channel 80.20 m long, with a capacity of 1.00 m³/s, for a return period of 25 years was proposed. The stormwater masterplan will contribute to the execution of activities within the campus and prevent accidents and the proliferation of diseases, constituting a water-management model that can be replicated locally, regionally, and internationally. Full article

Because of the competitive economy, organizations today seek to rationalize, innovate, and adapt to changing environments and circumstances as part of business process improvement efforts. The strength of blockchain technology lies in its usage as an apt technology to enhance the efficiency and effectiveness of business processes; furthermore, it prevents the use of erroneous or obsolete data and allows sharing of confidential data securely. The use of superior technology in the execution and automation of business processes brings opportunities to rethink the specific process itself as well. Business processes modeling and verification are essential to control and assure organizational evolution, therefore, the aim of this paper is three-fold: firstly, to provide business process management patterns in finance, based on blockchain, specifically for the loan-application process in the banking industry and claim process in the insurance industry that could be used and customized by companies; secondly, to critically analyze challenges and opportunities from the introduction of such approach for companies, and thirdly, to outline how companies can implement the loan business process as a web service. Partner companies (a bank and an insurance company) formulated the potential requirements for M2P along with the application of blockchain technology. An experimental design framework was established that gave the necessary services to model the requirements, check the models, and operationalize the models. The applied research methodologies are as follows: design science research paradigm and software case study, model-to-programming (M2P) of business processes, and utilization of patterns of workflow and blockchain. Full article

The implementation of bike-sharing systems (BSSs) is expected to lead to modifications in the travel habits of transport users, one of which is the choice of travel mode. Therefore, this research focuses on the identification of factors influencing the shift of private car users to BSSs based on stated preference survey data from the city of Alexandroupolis, Greece. A binary logit model is employed for this purpose. The estimation results indicate the impacts of gender, income, travel time, travel cost and safety-related aspects on the mode shift, through which behavioural insights are derived. For example, car users are found to be twice as sensitive to the cost of BSSs than to that of car. Similarly, they are highly sensitive to BSS travel time. Based on the behavioural findings, policy measures are suggested under the following categories: (i) finance, (ii) regulation, (iii) infrastructure, (iv) campaigns and (v) customer targeting. In addition, a secondary objective of this research is to obtain insights from the comparison of the specified logit model with a machine learning approach, as the latter is slowly gaining prominence in the field of transport. For the comparison, a random forest classifier is also developed. This comparison shows a coherence between the two approaches, although a discrepancy in the feature importance for gender and travel time is observed. A deeper exploration of this discrepancy highlights the hurdles that often occur when using mathematically more powerful models, such as the random forest classifier. Full article

Building materials can enable the recycling of sewage sludge from tannery wastewater treatment by infiltration/percolation over coal and clay waste. The process avoids energy-intensive operations and yields a stable and environmentally friendly product. The sludge under study is mainly composed of SiO₂, CaO, Al₂O₃, and Fe₂O₃, which is convenient to replace the mortar in cement. Different mortars were made by substituting a variable amount of sludge, from 0 to 30%, into the standard cement. The microstructure and mechanical properties of the mortar specimens were characterized after curing for 7 days and 28 days. The best properties were obtained with 15% sludge. Above 15%, the strength decreases at an early stage, as confirmed by SEM and XRD analysis, with more voids and ettringites at larger sludge content. The leaching tests of the mortar confirm that the cumulative values of heavy metals are far below the Deutsch regulatory limits (NEN 7043), justifying retention of the metals in the matrix. Radiological assessment of the sludge mortars also confirms their safety with the values of naturally occurring radioactive materials, surface radon exhalation and annual effective dose far below the required limits. The study suggests that 15% sludge can be used to sustainably replace cement and meet building safety requirement standards. Full article