Smart Cities

I conducted an integrative literature review by utilizing theoretical and methodological elements of critical realism (i.e., the distinction between ontology and epistemology) to evaluate the significance of triple-helix intermediaries. This review involved examining all published research on smart cities in “elite” ABS (Chartered Association of Business Schools) journals (4, 4*). My findings indicate that the philosophical foundations of the examined literature are predominantly grounded on “positivism”, “postmodernism”, “interpretivism”, and “pragmatism”, without delving into the ontological reinforcement of capitalist institutions through innovation creation and diffusion—a central concern of critical realism. I argue that this oversight stems from the prevailing “paradigm” within these “elite” journals, which often excludes historical and critical perspectives. In response, I propose a reoriented intermediary, the Triple-Helix Business Clinic, grounded in critical-realist assumptions. This new theoretical framework can guide practical policy development aimed at reinforcing business innovation and driving broader socioeconomic progress. Full article

Intersection sight distance (ISD) requirements, currently designed for driver-operated vehicles (DVs), will be affected once automated vehicles (AVs) enter the driving environment. This paper examines the ISD for intersections with a yield control on a minor road in a mixed DV-AV environment. Five potential conflict types with different ISD requirements are modeled as a minor-road vehicle proceeds to cross the intersection, turns right, or turns left. Furthermore, different models are developed for each conflict type depending on the vehicle types on the minor and major roads. These models, along with the intersection geometry, establish the system demand and supply models for ISD reliability analysis. A surrogate safety measure is developed and used to measure ISD non-compliance and is denoted by the probability of unresolved conflicts (PUC). The models are applied to a case study intersection, where PUC values are estimated using Monte Carlo Simulation and compared to an established target value relating to the DV-only traffic of 0.00674. The results show that AV-related traffic has higher overall PUC values than those of DV-only traffic. A corrective measure, reducing the AV speed limit on the minor-road approaches by 3 to 4 km/h, decreases the overall PUC to values below those of the target PUC. Full article

Rural public transport networks face significant challenges, often characterised by suboptimal service quality. With advancements in technology, various applications have been explored to address these issues. Autonomous Demand-Responsive Transits (ADRTs) represent a promising solution that has been investigated over recent years. Their potential to enhance the overall quality of transport systems and promote sustainable transportation is well-recognised. In our research study, we evaluated the viability of ADRTs for rural networks. Our methodology focused on two primary areas: the suitability of ADRTs (considering vehicle type, service offerings, trip purposes, demographic groups, and land use) and the broader impacts of ADRTs (including passenger performance, social impacts, and environmental impacts). Perceptions of ADRT suitability peaked for university precincts and 24/7 operations. However, they were less favoured by mobility-disadvantaged groups (disabled, seniors, and school children). We also examined demographic heterogeneity and assessed the influence of demographic factors (age, gender, education, occupation, household income level, and disability status) on the implementation of ADRTs in rural settings. The findings delineate the varied perceptions across these socio-demographic strata, underscoring the necessity for demographic-specific trials. Consequently, we advocate for the implementation of ADRT services tailored to accommodate the diverse needs of these demographic cohorts. Full article

With urbanization and population growth, waste management has become a pressing issue. Intelligent detection systems using deep learning algorithms to monitor garbage bin overflow in real time have emerged as a key solution. However, these systems often face challenges such as lack of dataset diversity and high energy consumption due to the extensive use of IoT devices. To address these challenges, we developed the Garbage Bin Status (GBS) dataset, which includes 16,771 images. Among them, 8408 images were generated using the Stable Diffusion model, depicting garbage bins under diverse weather and lighting scenarios. This enriched dataset enhances the generalization of garbage bin overflow detection models across various environmental conditions. We also created an energy-efficient model called HERD-YOLO based on Spiking Neural Networks. HERD-YOLO reduces energy consumption by 89.2% compared to artificial neural networks and outperforms the state-of-the-art EMS-YOLO in both energy efficiency and detection performance. This makes HERD-YOLO a promising solution for sustainable and efficient urban waste management, contributing to a better urban environment. Full article

The arrival of 5G technology is transforming the creation of smart cities by delivering unmatched speed, extremely low latency, and broad device connectivity. These developments allow for the effortless integration of IoT devices, live monitoring systems, and cutting-edge urban applications. This paper examines the impact of 5G in tackling significant urban challenges, including network overload, energy efficacy, and data security, while highlighting its revolutionary potential in improving smart city frameworks. An important emphasis is the fusion of 5G with real-time digital twins, which link physical and digital realms to enhance urban systems, refine resource management, and strengthen public safety. Even with its potential, the rollout of 5G encounters challenges such as expensive infrastructure, significant energy requirements, and limited signal reach. This research explores the present trends, current applications, and new challenges related to 5G in smart cities, providing insights into its constraints and approaches to address them. It summarizes the necessity of cooperation among stakeholders to realize 5G’s complete capabilities and to create scalable, secure, and sustainable solutions for smart cities. Full article

This manuscript investigates the integration of micromobility solutions in four Swiss contexts, with a primary focus on Lugano as the testing area. Through urban analysis, urban experiment, and social testing, the Inclusive MicroMob project introduced Genny Zero, an innovative micromobility device, to assess its impact on urban mobility. The findings highlight key factors for successful micromobility integration, including the need for an interdisciplinary approach that includes a holistic urban overview, an improved road safety analysis, and clear regulatory frameworks. This study also emphasizes the importance of participatory urban governance in addressing community needs and ensuring accessibility. Policy recommendations are provided to support the development of inclusive and sustainable micromobility systems in medium-sized cities. Full article

This article addresses the optimal network expansion problem in rural distribution systems using a mixed-integer nonlinear programming (MINLP) model that simultaneously performs route selection and conductor sizing in radial distribution systems. The proposed methodology was validated on 9- and 25-node test systems, comparing the results against approaches based on the minimum spanning tree (MST) formulation and metaheuristic approaches (the sine-cosine and tabu search algorithms). The MINLP model significantly reduced the total costs. For the nine-node system, the total cost decreased from USD 131,819.33 (MST-TSA) to USD 77,129.34 (MINLP), saving USD 54,689.99 (41.48%). Similarly, the costs of energy losses dropped from USD 111,746.73 to USD 63,764.12, a reduction of USD 47,982.61 (42.94%). In the 25-node system, the total costs fell by over 65% from USD 371,516.59 to USD 128,974.72, while the costs of energy losses decreased by USD 210,057.16 (61.06%). Despite requiring a higher initial investment in conductors, the MINLP model led to substantial long-term savings due to reduced operating costs. Unlike previous methods which separate network topology design and conductor sizing, our proposal integrates both aspects, ensuring globally optimal solutions. The results demonstrate its scalability and effectiveness for long-term distribution planning in complex power networks. The experimental implementation was carried out in Julia (v1.10.2) using JuMP (v1.21.1) and BONMIN. Full article

Amidst climate change and the energy crisis worldwide, the synergy between smart city and environmental policies has become a key path to improving the green resilience of cities. This study examines the spatial effects of carbon emission trading (CET) policy on urban energy performance under the context of artificial intelligence (AI)-empowered smart cities. Using the spatial Durbin model (SDM) and analyzing data from 262 Chinese cities covering the period 2013–2021, the results reveal that: (1) smart cities significantly benefit from the institutional support of the local CET policy, resulting in an 8.55% reduction in energy consumption in the pilot city; (2) AI advancement contributes directly to reducing energy consumption in surrounding areas by 21.84% through spatial effects, and compensates for the imbalance of regional renewable energy caused by the “siphon effect” of CET policy. This study provides empirical evidence for developing countries to build green and resilient cities. This paper proposes the need to build a national CET market, strengthen government supervision, and make reasonable use of AI technology, transforming the green and resilient model of smart cities from Chinese experience to global practice. Full article

This article explores the integration of Maintenance 4.0 into HVAC (heating, ventilation, and air conditioning) systems, highlighting its essential role within the framework of Industry 4.0. Maintenance 4.0 utilizes advanced technologies such as artificial intelligence and IoT sensing technologies. It also incorporates sophisticated data management techniques to transform maintenance strategies into HVAC and indoor ventilation systems. These innovations work together to enhance energy efficiency, air quality, and overall system performance. The paper provides an overview of various Maintenance 4.0 frameworks, discussing the role of IoT sensors in real-time monitoring of environmental conditions, equipment health, and energy consumption. It highlights how AI-driven analytics, supported by IoT data, enable predictive maintenance and fault detection. Additionally, the paper identifies key research gaps and challenges that hinder the widespread implementation of Maintenance 4.0, including issues related to data quality, model interpretability, system integration, and scalability. This paper also proposes solutions to address these challenges, such as advanced data management techniques, explainable AI models, robust system integration strategies, and user-centered design approaches. By addressing these research gaps, this paper aims to accelerate the adoption of Maintenance 4.0 in HVAC systems, contributing to more sustainable, efficient, and intelligent built environments. Full article

Accurately forecasting load profiles, especially peak catching, is a challenge due to the stochastic nature of consumption. In this paper, we applied the following three models for forecasting: Long Short-Term Memory (LSTM); Support Vector Regression (SVR); and the combined model, which is a blend of SVR, Gated Recurrent Units (GRU), and Linear Regression (LR) to forecast 24 h-ahead load profiles. Household (HH), heat pump (HP), and electric vehicle (EV) loads are singular, and these were collectively considered with one-year load profiles. This study tackles the issue of accurately forecasting load profiles by evaluating LSTM, SVR, and an ensemble model for predicting energy consumption in HH, HP, and EV loads. A novel forecast correction mechanism is introduced, adjusting forecasts every eight hours to increase reliability. The findings highlight the potential of deep learning in enhancing energy demand forecasting, especially in identifying peak loads, which contributes to more stable and efficient grid operations. Visual and validation data were investigated, along with the models’ performances at different levels, such as off-peak, on-peak, and entirely. Among all models, LSTM performed slightly better in most of the factors, particularly in peak capturing. However, the blended model showed slightly better performance than LSTM for EV power load forecasting, with an on-peak mean absolute percentage error (MAPE) of 21.45%, compared to 29.24% and 22.02% for SVR and LSTM, respectively. Nevertheless, visual analysis clearly showed the strong ability of LSTM to capture peaks. This LSTM potential was also shown by the mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) during the on-peak period, with around 3–5% improvement compared to SVR and the blended model. Finally, LSTM was employed in predicting day-ahead load profiles using measured data from four grids and showed high potential in capturing peaks with MAPE values less than 10% for most of the grids. Full article

This work presents an innovative, energy-efficient IoT routing protocol that combines advanced data fusion grouping and routing strategies to effectively tackle the challenges of data management in smart cities. Our protocol employs hierarchical Data Fusion Head (DFH), relay DFHs, and marine predators algorithm, the latter of which is a reliable metaheuristic algorithm which incorporates a fitness function that optimizes parameters such as how closely the Sensor Nodes (SNs) of a data fusion group (DFG) are gathered together, the distance to the sink node, proximity to SNs within the data fusion group, the remaining energy (RE), the Average Scale of Building Occlusions (ASBO), and Primary DFH (PDFH) rotation frequency. A key innovation in our approach is the introduction of data fusion techniques to minimize redundant data transmissions and enhance data quality within DFG. By consolidating data from multiple SNs using fusion algorithms, our protocol reduces the volume of transmitted information, leading to significant energy savings. Our protocol supports both direct routing, where fused data flow straight to the sink node, and multi-hop routing, where a PDF relay is chosen based on an influential relay cost function that considers parameters such as RE, distance to the sink node, and ASBO. Given that the proposed protocol incorporates efficient failure recovery strategies, data redundancy management, and data fusion techniques, it enhances overall system resilience, thereby ensuring high protocol performance even in unforeseen circumstances. Thorough simulations and comparative analysis reveal the protocol’s superior performance across key performance metrics, namely, network lifespan, energy consumption, throughput, and average delay. When compared to the most recent and relevant protocols, including the Particle Swarm Optimization-based energy-efficient clustering protocol (PSO-EEC), linearly decreasing inertia weight PSO (LDIWPSO), Optimized Fuzzy Clustering Algorithm (OFCA), and Novel PSO-based Protocol (NPSOP), our approach achieves very promising results. Specifically, our protocol extends network lifespan by 299% over PSO-EEC, 264% over LDIWPSO, 306% over OFCA, and 249% over NPSOP. It also reduces energy consumption by 254% relative to PSO-EEC, 264% compared to LDIWPSO, 247% against OFCA, and 253% over NPSOP. The throughput improvements reach 67% over PSO-EEC, 59% over LDIWPSO, 53% over OFCA, and 50% over NPSOP. By fusing data and optimizing routing strategies, our protocol sets a new benchmark for energy-efficient IoT DFG, offering a robust solution for diverse IoT deployments. Full article

Energy-efficient renovation of the existing building stock is essential for achieving the ambitious sustainability goals set by the European Commission for 2030. However, implementing sustainable renovation has proven challenging, as numerous studies have concluded. Multi-family buildings are a significant part of Sweden’s building stock and require renovations to meet energy efficiency standards. This study aims to provide an overview of sustainable renovation practices in Sweden’s multi-family buildings. A semi-open structured questionnaire was developed to examine the adoption of these practices, with data collected from 11 housing companies. The responses reveal that Swedish housing companies are well aware of the three key aspects of sustainability and actively consider them in their renovation projects. Notably, specific energy use and investment costs are the most commonly used methods for evaluating the environmental and economic aspects, respectively. However, there is a lack of a common method for assessing the social aspects of renovation projects. Additionally, this study highlights the need for standardized decision-making tools in multi-family building renovations. Full article

Social media is crucial for gathering public feedback on government services, particularly in the traffic sector. While Aspect-Based Sentiment Analysis (ABSA) offers a means to extract actionable insights from user posts, analyzing Arabic content poses unique challenges. Existing Arabic ABSA approaches heavily rely on supervised learning and manual annotation, limiting scalability. To tackle these challenges, we suggest an integrated framework combining unsupervised BERTopic-based Aspect Category Detection with distance supervision using a fine-tuned CAMeLBERT model for sentiment classification. This is further complemented by transformer-based summarization through a fine-tuned AraBART model. Key contributions of this paper include: (1) the first comprehensive Arabic traffic services dataset containing 461,844 tweets, enabling future research in this previously unexplored domain; (2) a novel unsupervised approach for Arabic ABSA that eliminates the need for large-scale manual annotation, using FastText custom embeddings and BERTopic to achieve superior topic clustering; (3) a pioneering integration of aspect detection, sentiment analysis, and abstractive summarization that provides a complete pipeline for analyzing Arabic traffic service feedback; (4) state-of-the-art performance metrics across all tasks, achieving 92% accuracy in ABSA and a ROUGE-L score of 0.79 for summarization, establishing new benchmarks for Arabic NLP in the traffic domain. The framework significantly enhances smart city traffic management by enabling automated processing of citizen feedback, supporting data-driven decision-making, and allowing authorities to monitor public sentiment, identify emerging issues, and allocate resources based on citizen needs, ultimately improving urban mobility and service responsiveness. Full article

The global demand for transportation systems is growing due to the rise in passenger and cargo traffic, predicted to reach twice the current level by 2050. Although this growth signifies social and economic progress, its impact on energy consumption and greenhouse gas emissions cannot be overlooked. Developments in the transportation industry must align with advancements in emerging energy production systems. In this regards, UNSDG 7 advocates for “affordable and clean energy”, leading to a global shift towards the electrification of transport systems, sourcing energy from a mix of renewable and non-renewable resources. This paper proposes an integrated hybrid renewable energy system with grid connectivity to meet the electrical and thermal loads of a tourist complex, including an electric vehicle charging station. The analysis was carried on in nine locations with different weather conditions, with various components such as wind turbines, photovoltaic systems, diesel generators, boilers, converters, thermal load controllers, and battery energy storage systems. The proposed model also considers the effects of seasonal variations on electricity generation and charging connectivity. Sensitivity analysis has been carried on investigating the impact of variables on the techno-economic parameters of the hybrid system. The obtained results led to interesting conclusions. Full article

The rapid integration of Internet of Things (IoT) technologies in smart cities enhances urban management, yet public acceptance remains crucial for successful deployment. This study examined gender-based differences in IoT acceptance through a survey of 288 respondents from Warsaw and Plock, analyzed using structural equation modeling (SEM). The results revealed that women demonstrated significantly higher trust in IoT (+0.93, p < 0.001), greater perceived safety (+0.24, p = 0.013), and stronger support for environmental IoT applications (+0.48, p = 0.007) than men. While perceived usefulness was the strongest predictor of IoT acceptance for men (β = 0.523, p < 0.001), safety (β = 0.286, p = 0.001) and environmental awareness (β = 0.507, p < 0.001) drove acceptance among women. These findings highlight the need for gender-sensitive urban technology policies, emphasizing safety and sustainability to foster inclusive smart city development. The research results can be used by city authorities to learn about the requirements and concerns of residents to design a city that meets all residents’ requirements and better communicates IoT technology. Furthermore, the study underscores the importance of targeted education and awareness campaigns to address privacy concerns and promote broader adoption of IoT-driven solutions in urban environments. Full article

This paper investigates the application of recurrent neural networks, specifically Long Short-Term Memory (LSTM) models, for pressure forecasting in urban water supply systems. The objective of this study was to evaluate the effectiveness of LSTM models for pressure prediction tasks. To acquire real-time pressure data, an information system based on Internet of Things (IoT) technology using the MQTT protocol was proposed. The paper presents a data pre-processing algorithm for model training, as well as an analysis of the influence of various architectural parameters, such as the number of LSTM layers, the utilization of Dropout layers for regularization, and the number of neurons in Dense (fully connected) layers. The impact of seasonal factors, including month, day of the week, and time of day, on the pressure forecast quality was also investigated. The results obtained demonstrate that the optimal model consists of two LSTM layers, one Dropout layer, and one Dense layer. The incorporation of seasonal parameters improved prediction accuracy. The model training time increased significantly with the number of layers and neurons, but this did not always result in improved forecast accuracy. The results showed that the optimally tuned LSTM model can achieve high accuracy and outperform traditional methods such as the Holt–Winters model. This study confirms the effectiveness of using LSTM for forecasting in the water supply field and highlights the importance of pre-optimizing the model parameters to achieve the best forecasting results. Full article

Understanding and predicting urban vitality—the intensity and diversity of human activities in urban spaces—is crucial for sustainable urban development. However, existing studies often rely on discrete sampling points and single metrics, limiting their ability to capture the continuous spatial distribution of urban vibrancy. This study introduces the UVPN (urban vitality prediction network), a novel deep-learning architecture designed to generate high-resolution predictions of static and dynamic vitality at regional scales. The architecture integrates two key innovations: a SE (squeeze-and-excitation) block for adaptive feature recalibration and an RCA (residual connection with coordinate attention) bottleneck for position-aware feature learning. Applied to New York City, UVPN leverages diverse urban morphological features such as streetscape attributes and land use patterns to predict continuous vitality distributions. The model outperforms existing architectures, achieving reductions of 34.03% and 38.66% in mean squared error for population density and pedestrian flow predictions, respectively. Feature importance analysis reveals that road networks predominantly influence population density, while streetscape features strongly affect pedestrian flows, with built density and points of interest contributing to both dimensions. By advancing urban vitality prediction, UVPN provides a robust framework for evidence-based urban planning, supporting the creation of more sustainable, functional, and livable cities. Full article

Understanding the interplay between traffic optimization parameters and their alignment with sensors, control algorithms, and Sustainable Development Goals (SDGs) is essential for improving urban traffic management. The appropriate selection of parameters in urban traffic management is crucial to optimize vehicular flow and meet the Sustainable Development Goals (SDGs). To find out which parameters are most commonly used and appropriate, a comprehensive study was conducted, the results of which are presented in this article. This study uses a three-phase approach: qualitative exploration, systematic literature review, and multiple-dimensional analysis. This study’s contributions include a practical five-level framework for traffic optimization addressing congestion problems, the identification of 19 commonly used traffic control parameters, the analysis of their implementations in recent intelligent traffic control systems, and a proposal of trends to orient these parameters towards efficiency and compliance with the SDGs. The results lay the groundwork for creating new parameters or modifying existing parameters so that the parameters are oriented not only towards efficiency in control algorithms or user experience but also towards meeting the SDGs. Full article

The increasing complexity of urban logistics in smart cities requires innovative solutions that leverage real-time data, predictive analytics, and adaptive learning to enhance efficiency. This study presents a predictive analytics framework integrating digital twin technology, IoT-enabled logistics data, and cybernetic feedback loops to improve last-mile delivery accuracy, congestion management, and sustainability in smart cities. Grounded in Systems Theory and Cybernetic Theory, the framework models urban logistics as an interconnected network, where real-time IoT data enable dynamic routing, demand forecasting, and self-regulating logistics operations. By incorporating machine learning-driven predictive analytics, the study demonstrates how AI-powered logistics optimization can enhance urban freight mobility. The cybernetic feedback mechanism further improves adaptive decision-making and operational resilience, allowing logistics networks to respond dynamically to changing urban conditions. The findings provide valuable insights for logistics managers, smart city policymakers, and urban planners, highlighting how AI-driven logistics strategies can reduce congestion, enhance sustainability, and optimize delivery performance. The study also contributes to logistics and smart city research by integrating digital twins with adaptive analytics, addressing gaps in dynamic, feedback-driven logistics models. Full article