Smart Cities, Volume 7, Issue 4 (August 2024) – 8 articles

In post-war environments, property valuation encounters obstacles stemming from widespread destruction, population displacement, and complex legal frameworks. This study addresses post-war property valuation by integrating war-related considerations into the ISO 19152 Land Administration Domain Model, resulting in a valuation information model for Syria’s post-war landscape, serving as a reference for property valuation in conflict-affected areas. Additionally, property valuation is enhanced through visualization modeling, aiding the comprehension of war-related attributes amidst and following conflict. We utilize data from a field survey of 243 Condominium Units in the Harasta district, Rural Damascus Governorate. These data were collected through quantitative interviews with real estate companies and residents to uncover facts about property prices and war-related conditions. Our quantitative data are analyzed using inferential statistics of mean housing prices to assess the impact of war-related variables on property values during both wartime and post-war periods. The analysis reveals significant fluctuations in prices during wartime, with severely damaged properties experiencing notable declines (about −75%), followed by moderately damaged properties (about −60%). In the post-war phase, rehabilitated properties demonstrate price improvements (1.8% to 22.5%), while others continue to depreciate (−55% to −65%). These insights inform post-war property valuation standards, facilitating sustainable investment during the post-war recovery phase. Full article

The necessity for substantial societal transformations to meet the Sustainable Development Goals (SDGs) has become more urgent, especially in the wake of the COVID-19 pandemic. This paper examines the critical role of disruptive technologies, specifically Industry 5.0 and Society 5.0, in driving sustainable development. Our research investigation focuses on their impact on product development, healthcare innovation, pandemic response, and the development of nature-inclusive business models and smart cities. We analyze how these technologies influence SDGs 3 (Good Health and Well-Being), 4 (Quality Education), 9 (Industry, Innovation, and Infrastructure), and 11 (Sustainable Cities and Communities). By integrating these concepts into smart cities, we propose a coordinated framework to enhance the achievement of these goals. Additionally, we provide a SWOT analysis to evaluate this approach. This study aims to guide industrialists, policymakers, and researchers in leveraging technological advancements to meet the SDGs. Full article

As district heating networks age, current asset management practices, such as those relying on static life expectancies and age- and rule-based approaches, need to be replaced by data-driven asset management. As an alternative to physics-of-failure models that are typically preferred in the literature, this paper explores the application of more accessible traditional and novel machine learning-enabled reliability models for analyzing the reliability of district heating pipes and demonstrates how common data deficiencies can be accommodated by modifying the models’ likelihood expressions. The tested models comprised the Herz, Weibull, and the Neural Weibull Proportional Hazard models. An assessment of these models on data from an actual district heating network in Funen, Denmark showed that the relative youth of the network complicated the validation of the models’ distributional assumptions. However, a comparative evaluation of the models showed that there is a significant benefit in employing data-driven reliability modeling as they enable pipes to be differentiated based on the their working conditions and intrinsic features. Therefore, it is concluded that data-driven reliability models outperform current asset management practices such as age-based vulnerability ranking. Full article

Car-sharing services, which provide short-term vehicle rentals in urban centers, are rapidly expanding globally but also face numerous challenges. A significant challenge is the effective management of fleet selection to meet user expectations. Addressing this challenge, as well as methodological and literature gaps, the objective of this article is to present an original methodology that supports the evaluation of the suitability of vehicle fleets used in car-sharing systems and to identify the vehicle features preferred by users necessary for specific types of travel. The proposed methodology, which incorporates elements of transportation system modeling and concurrent analysis, was tested using a real-world case study involving a car-sharing service operator. The research focused on the commuting needs of car-sharing users for work or educational purposes. The study was conducted for a German car-sharing operator in Berlin. The research was carried out from 1 January to 30 June 2022. The findings indicate that the best vehicles for the respondents are large cars representing classes D or E, equipped with a combustion engine with a power of 63 to 149 kW, at least parking sensors, navigation, hands-free, lane assistant, heated seats, and high safety standards as indicated by Euro NCAP ratings, offered at the lowest possible rental price. The results align with market trends in Germany, which focus on the sale of at least medium-sized vehicles. This suggests a limitation of small cars in car-sharing systems, which were ideologically supposed to be a key fleet in those kinds of services. The developed methodology supports both system operators in verifying whether their fleet meets user needs and urban policymakers in effectively managing policies towards car-sharing services, including fleet composition, pricing regulations, and vehicle equipment standards. This work represents a significant step towards enhancing the efficiency of car-sharing services in the context of smart cities, where personalization and optimizing transport are crucial for sustainable development. Full article

This paper examines business model implementations in three leading European smart cities: London, Amsterdam, and Berlin. Through a systematic literature review and comparative analysis, the study identifies and analyzes various business models employed in these urban contexts. The findings reveal a diverse array of models, including public–private partnerships, build–operate–transfer arrangements, performance-based contracts, community-centric models, innovation hubs, revenue-sharing models, outcome-based financing, and asset monetization strategies. Each city leverages a unique combination of these models to address its specific urban challenges and priorities. The study highlights the role of PPPs in large-scale infrastructure projects, BOT arrangements in transportation solutions, and performance-based contracts in driving efficiency and accountability. It also explores the benefits of community-centric models, innovation hubs, revenue-sharing models, outcome-based financing, and asset monetization strategies in enhancing the sustainability, efficiency, and livability of smart cities. The paper offers valuable insights for policymakers, urban planners, and researchers seeking to advance smart city development worldwide. Full article

In an era marked by rapid technological progress, the pivotal role of Artificial Intelligence (AI) is increasingly evident across various sectors, including local governments. These governmental bodies are progressively leveraging AI technologies to enhance service delivery to their communities, ranging from simple task automation to more complex engineering endeavours. As more local governments adopt AI, it is imperative to understand the functions, implications, and consequences of these advanced technologies. Despite the growing importance of this domain, a significant gap persists within the scholarly discourse. This study aims to bridge this void by exploring the applications of AI technologies within the context of local government service provision. Through this inquiry, it seeks to generate best practice lessons for local government and smart city initiatives. By conducting a comprehensive review of grey literature, we analysed 262 real-world AI implementations across 170 local governments worldwide. The findings underscore several key points: (a) there has been a consistent upward trajectory in the adoption of AI by local governments over the last decade; (b) local governments from China, the US, and the UK are at the forefront of AI adoption; (c) among local government AI technologies, natural language processing and robotic process automation emerge as the most prevalent ones; (d) local governments primarily deploy AI across 28 distinct services; and (e) information management, back-office work, and transportation and traffic management are leading domains in terms of AI adoption. This study enriches the existing body of knowledge by providing an overview of current AI applications within the sphere of local governance. It offers valuable insights for local government and smart city policymakers and decision-makers considering the adoption, expansion, or refinement of AI technologies in urban service provision. Additionally, it highlights the importance of using these insights to guide the successful integration and optimisation of AI in future local government and smart city projects, ensuring they meet the evolving needs of communities. Full article

As customers’ expectations continue to rise, advanced on-demand transport services face the challenge of meeting new requirements. This study addresses a specific transportation issue belonging to dial-a-ride problems, including constraints aimed at fulfilling customer needs. In order to provide more efficient on-demand transportation solutions, we propose a new hybrid evolutionary computation method. This method combines customized heuristics including two exchanged mutation operators, a crossover, and a tabu search. These optimization techniques have been empirically proven to support advanced designs and reduce operational costs, while significantly enhancing service quality. A comparative analysis with an evolutionary local search method from the literature has demonstrated the effectiveness of our approach across small-to-large-scale problems. The main results show that service providers can optimize their scheduling operations, reduce travel costs, and ensure a high level of service quality from the customer’s perspective. Full article

This study undertakes a systematic literature review, framed by eight research questions, and an exploration into the state-of-the-art concerning smart home innovations for care of older adults, ethical, security, and privacy considerations in smart home deployment, integration of technology, user interaction and experience, and smart home design and accessibility. The review evaluates the role of smart home technologies (SHTs) in enhancing the lives of older adults, focusing on their cost-effectiveness, ease of use, and overall utility. The inquiry aims to outline both the advantages these technologies offer in supporting care for older adults and the obstacles that impede their widespread adoption. Throughout the investigation, 58 studies were analyzed, selected for their relevance to the discourse on smart home applications in care for older adults. This selection came from a search of literature published between 2010 and 2023, ensuring an up-to-date understanding of the field. The findings highlight the potential of SHTs to improve various aspects of daily living for older adults, including safety, health monitoring, and social interaction. However, the research also identifies several challenges, including the high costs associated with these technologies, their complex nature, and ethical concerns surrounding privacy and autonomy. To address these challenges, the study presents recommendations to increase the accessibility and user-friendliness of SHTs for older adults. Among these, educational initiatives for older adults are emphasized as a strategy to improve technology acceptance, along with suggestions for design optimizations in wearable devices to enhance comfort and adaptability. The implications of this study are significant, offering insights for researchers, practitioners, developers, and policymakers engaged in creating and implementing smart home solutions for care of older adults. By offering an understanding of both the opportunities and barriers associated with SHTs, this research supports future efforts to create more inclusive, practical, and supportive environments for aging populations. Full article