Smart Cities, Volume 7, Issue 5 (October 2024) – 27 articles

The advent of Sixth Generation (6G) wireless technologies introduces challenges and opportunities for Mobile Ad Hoc Networks (MANETs) and Vehicular Ad Hoc Networks (VANETs), necessitating a reevaluation of traditional routing protocols. This paper introduces the Multi-Metric Scoring Dynamic Source Routing (MMS-DSR), a novel enhancement of the Dynamic Source Routing (DSR) protocol, designed to meet the demands of 6G-enabled MANETs and the dynamic environments of VANETs. MMS-DSR integrates advanced technologies and methodologies to enhance routing performance in dynamic scenarios. Key among these is the use of a CNN-LSTM-based beamforming algorithm, which optimizes beamforming vectors dynamically, exploiting spatial-temporal variations characteristic of 6G channels. This enables MMS-DSR to adapt beam directions in real time based on evolving network conditions, improving link reliability and throughput. Furthermore, MMS-DSR incorporates a multi-metric scoring mechanism that evaluates routes based on multiple QoS parameters, including latency, bandwidth, and reliability, enhanced by the capabilities of Massive MIMO and the IEEE 802.11ax standard. This ensures route selection is context-aware and adaptive to changing dynamics, making it effective in urban settings where vehicular and mobile nodes coexist. Additionally, the protocol uses machine learning techniques to predict future route performance, enabling proactive adjustments in routing decisions. The integration of dynamic beamforming and machine learning allows MMS-DSR to effectively handle the high mobility and variability of 6G networks, offering a robust solution for future wireless communications, particularly in smart cities. Full article

Critical infrastructure (CI) networks face diverse natural hazards, such as flooding. CI network modeling methods are used to evaluate these hazards, enabling the analysis of cascading effects, flood risk, and potential flood risk-reducing measures. However, there is a lack of linkage between analytical methods and potential multisectoral, structural, and nonstructural measures. This deficiency impedes the development of CI network (CIN) models as robust tools for active flood risk management. CI operators have significant expertise in managing and implementing flooding-related measures within their sectors. The objective of this study is to bridge the gap between the application of CIN modeling and the consideration of flood measures in three steps. The first step is conducting a literature review and CI stakeholder interviews in Central Europe on flood measures. The second step is the culmination of the findings in a comprehensive catalog detailing flood measures tailored to five CI sectors, with a generalized category spanning each phase of the disaster risk management cycle. The third step is the validation of the catalog’s utility in a proof-of-concept study along the Vicht River in Western Germany with a model-based flood risk analysis of five flood measures. The application of the flood measure catalog improves the options available for active and residual flood risk management. Additionally, the CI flood risk modeling approach presented here allows for consideration of disruption duration and recovery capability, thus linking the concept of risk and resilience. Full article

Today, historic villages represent a widespread and relevant reality of the Italian administrative structure. To preserve their value for future generations, smart city applications can contribute to implement effective monitoring and decision-making processes devoted to safeguarding their fragile ecosystem. Starting from a situational awareness model, this study proposes a method for designing human-in-the-loop cyber-physical systems that allow the design of monitoring and decision-making applications for historic villages. Both the model and the design method can be used as a reference for the realization of human-in-the-loop cyber-physical systems that consist of human beings, smart objects, edge devices, and cloud components in edge-cloud architectures. The output of the research, consisting of the graphical models for the definition of monitoring architectures and the method for the design of human-in-the-loop cyber-physical systems, was validated in the context of the village of Sant’Agata dei Goti through the implementation of a human-in-the-loop cyber-physical system for monitoring sites aiming at their management, conservation, protection, and fruition. Full article

In the context of increasingly deteriorating global ecological conditions and rising carbon emissions from buildings, campus architecture, as the primary environment for youth learning and living, plays a crucial role in low-carbon energy-efficient design, and green environments. This paper takes the case of Yezhai Middle School in Qianshan, Anhui Province, to explore wind environment optimization and facade energy-saving strategies for mountainous campus buildings under existing building stock renovation. In the context of smart city development, integrating advanced technologies and sustainable practices into public infrastructure has become a key objective. Through wind environment simulations and facade energy retrofitting, this study reveals nonlinear increases in wind speed with building height and significant effects of ground roughness on wind speed variations. Adopting EPS panels and insulation layers in facade energy retrofitting reduces energy consumption for winter heating and summer cooling. The renovated facade effectively prevents cold air intrusion and reduces external heat gain, achieving approximately 24% energy savings. This research provides a scientific basis and practical experience for low-carbon energy retrofitting of other campus and public buildings, advancing the construction industry towards green and low-carbon development goals within the framework of smart city initiatives. Full article

This study analyses five months of continuous monitoring of different lighting warning systems at a pedestrian crosswalk through video surveillance cameras during nighttime. Three different light signalling systems were installed near a pedestrian crossing to improve the visibility and safety of vulnerable road users: in-curb LED strips, orange flashing beacons, and asymmetric enhanced LED lighting. Seven different lighting configurations of the three systems were studied and compared with standard street lighting. The speed of vehicles for each pedestrian–driver interaction was also evaluated. This was then compared to the speed that vehicles should maintain in order to stop in time and allow pedestrians to cross the road safely. In all of the conditions studied, speeds were lower than those maintained in the five-month presence of standard street lighting (42.96 km/h). The results show that in conditions with dedicated flashing LED lighting, in-curb LED strips, and orange flashing beacons, most drivers (72%) drove at a speed that allowed the vehicle to stop safely compared to standard street lighting (10%). In addition, with this lighting configuration, the majority of vehicles (85%) stopped at pedestrian crossings, while in standard street lighting conditions only 26% of the users stopped to give way to pedestrians. Full article

This paper examines how smart cities can address land subsidence and liquefaction in the context of rapid urbanization in Japan. Since the 1960s, liquefaction has been an important topic in geotechnical engineering, and extensive efforts have been made to evaluate soil resistance to liquefaction. Currently, there is a lack of machine learning applications in smart cities that specifically target geological hazards. This study aims to develop a high-performance prediction model for estimating the depth of the bearing layer, thereby improving the accuracy of geotechnical investigations. The model was developed using actual survey data from 433 points in Setagaya-ku, Tokyo, by applying two machine learning techniques: artificial neural networks (ANNs) and bagging. The results indicate that machine learning offers significant advantages in predicting the depth of the bearing layer. Furthermore, the prediction performance of ensemble learning improved by about 20% compared to ANNs. Both interdisciplinary approaches contribute to risk prediction and mitigation, thereby promoting sustainable urban development and underscoring the potential of future smart cities. Full article

The Consumer-Oriented Trusted Service Manager (CO-TSM) model has been recognised as a significant advancement in managing applications on Near Field Communication (NFC)-enabled mobile devices and multi-application smart cards. Traditional Trusted Service Manager (TSM) models, while useful, often result in market fragmentation and limit widespread adoption due to their centralised control mechanisms. The CO-TSM model addresses these issues by decentralising management and offering greater flexibility and scalability, making it more adaptable to the evolving needs of embedded systems, particularly in the context of the Internet of Things (IoT) and Radio Frequency Identification (RFID) technologies. This paper provides a comprehensive analysis of the CO-TSM model, highlighting its application in various technological domains such as smart cards, HCE-based NFC mobile phones, TEE-enabled smart home IoT devices, and RFID-based smart supply chains. By evaluating the CO-TSM model’s architecture, implementation challenges, and practical deployment scenarios, this paper demonstrates how CO-TSM can overcome the limitations of traditional TSM approaches. The case studies presented offer practical insights into the model’s adaptability and effectiveness in real-world scenarios. Through this examination, the paper aims to underscore the CO-TSM model’s role in enhancing scalability, flexibility, and user autonomy in secure embedded device management, while also identifying areas for future research and development. Full article

Public transportation has been identified as a viable solution to mitigate traffic congestion. Transit signal priority (TSP) control, which is widely used at signalized intersections, has been recognized as a practical strategy to improve the efficiency and reliability of bus operations. However, traditional TSP control may fall short of efficiency and is facing several challenges of negative externalities for non-transit users and the need to handle conflicting priority requests. Recent studies have proposed the use of reinforcement learning (RL) methods to identify efficient traffic signal control (TSC). Some of these studies on RL-based TSC have incorporated the concept of max-pressure (MP), which is a maximal weight-matching algorithm to minimize queue sizes. Nevertheless, the existing RL-based TSC methods focus on private vehicles and cannot adequately distinguish between buses and private vehicles. In prior research, RL-based control has been implemented within the context of bus rapid transit (BRT) systems. This study proposes a novel RL-based TSC strategy that leverages the MP concept and extends it to incorporate TSP control. This is the first implementation of RL-based TSP control within the mixed-traffic road network. A significant innovation of this research is the introduction of the priority factor (PF), which is designed to prioritize bus movements at signalized intersections. The proposed RL-based TSP with PF control seeks to balance the competing objectives of enhancing bus operations while mitigating adverse impacts on non-transit users. To evaluate the performance of the proposed TSP method with the PF mechanism, simulations were conducted on an arterial and a grid network under dynamic traffic conditions. The simulation results demonstrated that the proposed TSP with PF not only reduces bus travel times and resolves conflicts between priority requests but also does not make a significant negative impact on passenger car operations. Furthermore, the PF can be dynamically assigned according to the number of passengers on each bus, suggesting the potential for the proposed approach to be applied in various traffic management scenarios. Full article

In an efficient aerial package delivery scenario carried out by multiple Unmanned Aerial Vehicles (UAVs), a task allocation problem has to be formulated and solved in order to select the most suitable assignment for each delivery task. This paper presents the development methodology of an evolutionary-based optimization framework designed to tackle a specific formulation of a Drone Delivery Problem (DDP) with charging hubs. The proposed evolutionary-based optimization framework is based on a double-chromosome task encoding logic. The goal of the algorithm is to find optimal (and feasible) UAV task assignments such that (i) the tasks’ due dates are met, (ii) an energy consumption model is minimized, (iii) re-charge tasks are allocated to ensure service persistency, (iv) risk-aware flyable paths are included in the paradigm. Hard and soft constraints are defined such that the optimizer can also tackle very demanding instances of the DDP, such as tens of package delivery tasks with random temporal deadlines. Simulation results show how the algorithm’s development methodology influences the capability of the UAVs to be assigned to different tasks with different temporal constraints. Monte Carlo simulations corroborate the results for two different realistic scenarios in the city of Turin, Italy. Full article

Smart cities increasingly rely on the Internet of Things (IoT) to enhance infrastructure and public services. However, many existing IoT frameworks face challenges related to security, privacy, scalability, efficiency, and low latency. This paper introduces the Blockchain and Federated Learning for IoT (BFLIoT) framework as a solution to these issues. In the proposed method, the framework first collects real-time data, such as traffic flow and environmental conditions, then normalizes, encrypts, and securely stores it on a blockchain to ensure tamper-proof data management. In the second phase, the Data Authorization Center (DAC) uses advanced cryptographic techniques to manage secure data access and control through key generation. Additionally, edge computing devices process data locally, reducing the load on central servers, while federated learning enables distributed model training, ensuring data privacy. This approach provides a scalable, secure, efficient, and low-latency solution for IoT applications in smart cities. A comprehensive security proof demonstrates BFLIoT’s resilience against advanced cyber threats, while performance simulations validate its effectiveness, showing significant improvements in throughput, reliability, energy efficiency, and reduced delay for smart city applications. Full article

The building sector is crucial in keeping the environment healthy, mainly because of its energy and material usage. Roofs are one of the most important components to consider, as they not only shield the building from the elements but also have a big impact on the environment. The paper provides a state-of-the-art review of the life cycle assessment (LCA) application in the roofing industry. The review examines three main focus areas: (1) LCA of different roofing materials, (2) LCA of roofing systems, and (3) whole-building LCA. Key takeaways from the literature review demonstrate that there is significant variability in LCA methods and impact categories assessed across roofing studies. Only a few studies have explored the complete urban scale in LCA assessments of roofing components. Future research can include utilizing the potential of LCA at urban scales, which can offer a full understanding of the environmental impacts associated with roofing materials in urban settings. Full article

The digital world is increasingly permeating our reality, creating a significant reflection of the processes and activities occurring in smart cities. Such activities include well-known urban events, celebrations, and those with a very local character. These widespread events have a significant influence on shaping the spirit and atmosphere of urban environments. This work presents SemConvTree, an enhanced semantic version of the ConvTree algorithm. It incorporates the semantic component of data through semi-supervised learning of a topic modeling ensemble, which consists of improved models: BERTopic, TSB-ARTM, and SBert-Zero-Shot. We also present an improved event search algorithm based on both statistical evaluations and semantic analysis of posts. This algorithm allows for fine-tuning the mechanism of discovering the required entities with the specified particularity (such as a particular topic). Experimental studies were conducted within the area of New York City. They showed an improvement in the detection of posts devoted to events (about 40% higher f1-score) due to the accurate handling of events of different scales. These results suggest the long-term potential for creating a semantic platform for the analysis and monitoring of urban events in the future. Full article

Assessing the accessibility and provision of social facilities in urban areas presents a significant challenge, particularly when direct data on facility utilization are unavailable or incomplete. To address this challenge, our study investigates the potential of trip distribution models in estimating facility utilization based on the spatial distributions of population demand and facilities’ capacities within a city. We first examine the extent to which traditional gravity-based and optimization-focused models can capture population–facilities interactions and provide a reasonable perspective on facility accessibility and provision. We then explore whether advanced deep learning techniques can produce more robust estimates of facility utilization when data are partially observed (e.g., when some of the district administrations collect and share these data). Our findings suggest that, while traditional models offer valuable insights into facility utilization, especially in the absence of direct data, their effectiveness depends on accurate assumptions about distance-related commute patterns. This limitation is addressed by our proposed novel deep learning model, incorporating supply–demand constraints, which demonstrates the ability to uncover hidden interaction patterns from partly observed data, resulting in accurate estimates of facility utilization and, thereby, more reliable provision assessments. We illustrate these findings through a case study on kindergarten accessibility in Saint Petersburg, Russia, offering urban planners a strategic toolkit for evaluating facility provision in data-limited contexts. Full article

The use of digital twins (DTs) in the electric power industry and other industries is a hot topic of research, especially concerning the potential of DTs to improve processes and management. This paper aims to present approaches to the creation of DTs and models in general. It also examines the key parameters of these models and presents the challenges that need to be addressed in the future development of this field. Our analysis of the DTs and models discussed in this paper is carried out on the basis of identified key characteristics, which serve as criteria for an evaluation and comparison that sets the basis for further investigation. A discussion of the findings shows the potential of DTs and models in different sectors. The proposed recommendations are based on this analysis, and aim to support the further development and use of DTs. Research into DTs represents a promising sector with high potential. However, several key issues and challenges need to be addressed in order to fully realize their benefits in practice. Full article

To address the growing need for advanced tools that enable urban policymakers to conduct comprehensive cost-benefit analyses of traffic management changes, the Urbanite H2020 project has developed innovative artificial intelligence methods. Among them is a robust decision support system that assists policymakers in evaluating and selecting optimal urban mobility planning modifications by combining objective and subjective criteria. Utilising open-source microscopic traffic simulation tools, accurate digital models (or “digital twins”) of four pilot cities—Bilbao, Amsterdam, Helsinki, and Messina—were created, each addressing unique mobility challenges. These challenges include reducing private vehicle access in Bilbao’s city center, analysing the impact of increased bicycle traffic and population growth in Amsterdam, constructing a mobility-enhancing tunnel in Helsinki, and improving public transport connectivity in Messina. The research introduces five key innovations: the application of a consistent open-source simulation platform across diverse urban environments, addressing integration and consistency challenges; the pioneering use of Dexi for advanced decision support in smart cities; the implementation of advanced visualisations; and the integration of the machine learning tool, Orange, with a user-friendly GUI interface. These innovations collectively make complex data analysis accessible to non-technical users. By applying multi-label machine learning techniques, the decision-making process is accelerated by three orders of magnitude, significantly enhancing urban planning efficiency. The Urbanite project’s findings offer valuable insights into both anticipated and unexpected outcomes of mobility interventions, presenting a scalable, open-source AI-based framework for urban decision-makers worldwide. Full article

Our paper was inspired by the recent Society 5.0 initiative of the Japanese Government which seeks to create a sustainable human-centric society by putting to work recent advances in technology. One of the key challenges in implementing Society 5.0 is providing trusted and secure services for everyone to use. Motivated by this challenge, this paper makes three contributions that we summarize as follows: Our first main contribution is to propose a novel blockchain and smart contract-based trust and reputation service design to reduce the uncertainty associated with buyer feedback in marketplaces that we expect to see in Society 5.0. Our second contribution is to extend Laplace’s Law of Succession in a way that provides a trust measure in a seller’s future performance in terms of their past reputation scores. Our third main contribution is to illustrate three applications of the proposed trust and reputation service. Here, we begin by discussing an application to a multi-segment marketplace, where a malicious seller may establish a stellar reputation by selling cheap items, only to use their excellent reputation score to defraud buyers in a different market segment. Next, we demonstrate how our trust and reputation service works in the context of sellers with time-varying performance due, say, to overcoming an initial learning curve. We provide a discounting scheme where older reputation scores are given less weight than more recent ones. Finally, we show how to predict trust and reputation far in the future, based on incomplete information. Extensive simulations have confirmed the accuracy of our analytical predictions. Full article

The aim of this research has been to enable the management of trustworthy relationships between stakeholders, service providers, and physical assets, which are required in critical energy and mobility cyber–physical systems (CPS) applications. The achieved novel contribution is the concept of trustworthy communities with respective experimental solutions, which are developed by relying on verifiable credentials, smart contracts, trust over IP, and an Ethereum-based distributed ledger. The provided trustworthy community solutions are validated by executing them in two practical use cases, which are called energy flexibility and hunting safety. The energy flexibility case validation considered the execution of the solutions with one simulated and two real buildings with the energy flexibility aggregation platform, which was able to trade the flexibilities in an energy flexibility marketplace. The provided solutions were executed with a hunting safety smartphone application for a hunter and the smartwatch of a person moving around in the forest. The evaluations indicate that conceptual solutions for trustworthy communities fulfill the purpose and contribute toward making energy flexibility trading and hunting safety possible and trustworthy enough for participants. A trustworthy community solution is required to make value sharing and usage of critical energy resources and their flexibilities feasible and secure enough for their owners as part of the energy flexibility community. Sharing the presence and location in mobile conditions requires a trustworthy community solution because of security and privacy reasons, but it can also save lives in real-life elk hunting cases. During the evaluations, the need for further studies related to performance, scalability, community applications, verifiable credentials with wallets, sharing of values and incentives, authorized trust networks, dynamic trust situations, time-sensitive behavior, autonomous operations with smart contracts through security assessment, and applicability have been detected. Full article

Digital Twin (DT) technology is a pivotal innovation within the built environment industry, facilitating digital transformation through advanced data integration and analytics. DTs have demonstrated significant benefits in building design, construction, and asset management, including optimising lifecycle energy use, enhancing operational efficiency, enabling predictive maintenance, and improving user adaptability. By integrating real-time data from IoT sensors with advanced analytics, DTs provide dynamic and actionable insights for better decision-making and resource management. Despite these promising benefits, several challenges impede the widespread adoption of DT technology, such as technological integration, data consistency, organisational adaptation, and cybersecurity concerns. Addressing these challenges requires interdisciplinary collaboration, standardisation of data formats, and the development of universal design and development platforms for DTs. This paper provides a comprehensive review of DT definitions, applications, capabilities, and challenges within the Architecture, Engineering, and Construction (AEC) industries. This paper provides important insights for researchers and professionals, helping them gain a more comprehensive and detailed view of DT. The findings also demonstrate the significant impact that DTs can have on this sector, contributing to advancing DT implementations and promoting sustainable and efficient building management practices. Ultimately, DT technology is set to revolutionise the AEC industries by enabling autonomous, data-driven decision-making and optimising building operations for enhanced productivity and performance. Full article

This paper introduces a novel methodology for the real-time mapping of mobility restrictions, utilizing spatial crowdsourcing and Telegram as a traffic event data source. This approach is efficient in regions suffering from limitations in traditional data-capturing devices. The methodology employs ArcGIS Online (AGOL) for data collection, storage, and analysis, and develops a 3W (what, where, when) model for analyzing mined Arabic text from Telegram. Data quality validation methods, including spatial clustering, cross-referencing, and ground-truth methods, support the reliability of this approach. Applied to the Palestinian territory, the proposed methodology ensures the accurate, timely, and comprehensive mapping of traffic events, including checkpoints, road gates, settler violence, and traffic congestion. The validation results indicate that using spatial crowdsourcing to report restrictions yields promising validation rates ranging from 67% to 100%. Additionally, the developed methodology utilizing Telegram achieves a precision value of 73%. These results demonstrate that this methodology constitutes a promising solution, enhancing traffic management and informed decision-making, and providing a scalable model for regions with limited traditional data collection infrastructure. Full article

The Internet of Things (IoT) presents immense opportunities for driving Industry 4.0 forward. However, in scenarios involving networked control automation, ensuring high reliability and predictable latency is vital for timely responses. To meet these demands, the contemporary wireless protocol time-slotted channel hopping (TSCH), also referred to as IEEE 802.15.4-2015, relies on precise transmission schedules to prevent collisions and achieve consistent end-to-end traffic flow. In the realm of diverse IoT applications, this study introduces a new traffic-aware autonomous multi-objective scheduling function called OPTIMAOrchestra. This function integrates slotframe and channel management, adapts to varying network sizes, supports mobility, and reduces collision risks. The effectiveness of two versions of OPTIMAOrchestra is extensively evaluated through multi-run experiments, each spanning up to 3600 s. It involves networks ranging from small-scale setups to large-scale deployments with 111 nodes. Homogeneous and heterogeneous network topologies are considered in static and mobile environments, where the nodes within a network send packets to the server with the same and different application packet intervals. The results demonstrate that OPTIMAOrchestra_ch4 achieves a current consumption of 0.72 mA while maintaining 100% reliability and 0.86 mA with a 100% packet delivery ratio in static networks. Both proposed Orchestra variants in mobile networks achieve 100% reliability, with current consumption recorded at 6.36 mA. Minimum latencies of 0.073 and 0.02 s are observed in static and mobile environments, respectively. On average, a collision rate of 5% is recorded for TSCH and RPL communication, with a minimum of 0% collision rate observed in the TSCH broadcast in mobile networks. Overall, the proposed OPTIMAOrchestra scheduler outperforms existing schedulers regarding network efficiency, time, and usability, significantly improving reliability while maintaining a balanced latency–energy trade-off. Full article

LoRa technology is a recent technology belonging to the Low Power and Wide Area Networks (LPWANs), which offers distinct advantages for wireless communications and possesses unique features. Among others, it can be used for localization procedures offering minimal energy consumption and quite long-range transmissions. However, the exact capabilities of LoRa localization performance are yet to be employed thoroughly. This article examines the efficiency of the LoRa technology in localization tasks using Time Difference of Arrival (TDoA) measurements. An extensive and concrete experimental study was conducted in a real-world setup on the University of Patras campus, employing both real-world data and simulations to assess the precision of geodetic coordinate determination. Through our experiments, we implemented advanced localization algorithms, including Social Learning Particle Swarm Optimization (PSO), Least Squares, and Chan techniques. The results are quite interesting and highlight the conditions and parameters that result in accurate LoRa-based localization in real-world scenarios in smart cities. In our context, we were able to achieve state-of-the-art localization results reporting localization errors as low as 300 m in a quite complex 8 km × 6 km real-world environment. Full article

Today, the problem of predicting population migration is essential in the concept of smart cities for the proper development planning of certain regions of the country, as well as their financing and landscaping. In dealing with population migration in small settlements whose population is below 100,000, data collection is challenging. In countries where data collection is not well developed, most of the available data in open access are presented as part of textual reports issued by authorities in municipal districts. Therefore, the creation of a more or less adequate dataset requires significant efforts, and despite these efforts, the outcome is far from ideal. However, for large cities, there are typically aggregated databases maintained by authorities. We used them to find out what factors had an impact on the number of people who arrived or departed the city. Then, we reviewed several dozens of documents to mine the data of small settlements. These data were not sufficient to solve machine learning tasks, but they were used as the basis for creating a synthetic sample for model fitting. We found that a combination of two models, each trained on synthetic data, performed better. A binary classifier predicted the migration direction and a regressor estimateed the number of migrants. Lastly, the model fitted with synthetics was applied to the other set of real data, and we obtained good results, which are presented in this paper. Full article

The Metaverse of Things (MoT) is an advanced technology that has the potential to revolutionise urban living in the present era. This article explores the advantages, uses, and transformative outcomes of the MoT in smart cities. It encompasses sustainability, urban planning, citizen participation, infrastructure management, and more. MoT integrates the Internet of Things (IoT) with metaverse technologies. The ultimate objective is to develop virtual environments that are highly interactive, interconnected, and immersive while maintaining a high level of fidelity to reality. The IoT utilises virtual reality (VR), augmented reality (AR), and other digital technologies to gather data, facilitate communication, and automate certain processes, thereby enhancing several elements of urban living. The IoT will bring about a profound transformation in the way cities gather and utilise data to enhance services and optimise efficiency. Cities that can efficiently distribute this data can enhance public safety, optimise energy usage, regulate traffic, and manage waste properly. MoT apps that utilise immersive technologies and the IoT can be used to generate more intelligent and captivating cityscapes. The implementation of the MoT can greatly enhance the quality of life for residents of smart cities through improvements in transportation, healthcare, education, and community engagement. This study’s author examined how smart cities utilise the MoT to enhance the daily experiences of their inhabitants. This study examines the technical structure, possible advantages, and difficulties of implementing the MoT in urban settings, aiming to enhance the resilience, responsiveness, and adaptability of cities. The findings emphasise the importance of robust legislative frameworks, stringent security requirements, and well-developed infrastructure to facilitate the extensive use of MoT technology. These factors are crucial for establishing a highly interconnected and efficient urban environment. Full article

Road traffic crashes (RTCs) are a global public health issue, with traditional analysis methods often hindered by delays and incomplete data. Leveraging social media for real-time traffic safety analysis offers a promising alternative, yet effective frameworks for this integration are scarce. This study introduces a novel multitask learning (MTL) framework utilizing large language models (LLMs) to analyze RTC-related tweets from Australia. We collected 26,226 traffic-related tweets from May 2022 to May 2023. Using GPT-3.5, we extracted fifteen distinct features categorized into six classification tasks and nine information retrieval tasks. These features were then used to fine-tune GPT-2 for language modeling, which outperformed baseline models, including GPT-4o mini in zero-shot mode and XGBoost, across most tasks. Unlike traditional single-task classifiers that may miss critical details, our MTL approach simultaneously classifies RTC-related tweets and extracts detailed information in natural language. Our fine-tunedGPT-2 model achieved an average accuracy of 85% across the six classification tasks, surpassing the baseline GPT-4o mini model’s 64% and XGBoost’s 83.5%. In information retrieval tasks, our fine-tuned GPT-2 model achieved a BLEU-4 score of 0.22, a ROUGE-I score of 0.78, and a WER of 0.30, significantly outperforming the baseline GPT-4 mini model’s BLEU-4 score of 0.0674, ROUGE-I score of 0.2992, and WER of 2.0715. These results demonstrate the efficacy of our fine-tuned GPT-2 model in enhancing both classification and information retrieval, offering valuable insights for data-driven decision-making to improve road safety. This study is the first to explicitly apply social media data and LLMs within an MTL framework to enhance traffic safety. Full article

The incorporation of Artificial Intelligence (AI) models into various optimization systems is on the rise. However, addressing complex urban and environmental management challenges often demands deep expertise in domain science and informatics. This expertise is essential for deriving data and simulation-driven insights that support informed decision-making. In this context, we investigate the potential of leveraging the pre-trained Large Language Models (LLMs) to create knowledge representations for supporting operations research. By adopting ChatGPT-4 API as the reasoning core, we outline an applied workflow that encompasses natural language processing, Methontology-based prompt tuning, and Generative Pre-trained Transformer (GPT), to automate the construction of scenario-based ontologies using existing research articles and technical manuals of urban datasets and simulations. From these ontologies, knowledge graphs can be derived using widely adopted formats and protocols, guiding various tasks towards data-informed decision support. The performance of our methodology is evaluated through a comparative analysis that contrasts our AI-generated ontology with the widely recognized pizza ontology, commonly used in tutorials for popular ontology software. We conclude with a real-world case study on optimizing the complex system of multi-modal freight transportation. Our approach advances urban decision support systems by enhancing data and metadata modeling, improving data integration and simulation coupling, and guiding the development of decision support strategies and essential software components. Full article

Commercially available crowdsourced connected vehicle (CV) trajectory data have recently been used to provide stakeholders with actionable and scalable roadway mobility infrastructure performance measures. Transportation agencies and automotive original equipment manufacturers (OEMs) share a common vision of ensuring the privacy of motorists that anonymously provide their journey information. As this market has evolved, the fleet mix has changed, and some OEMs have introduced additional fuzzification of CV data around 0.5 miles of frequently visited locations. This study compared the estimated Indiana market penetration rates (MPRs) between historic non-fuzzified CV datasets from 2020 to 2023 and a 5–11 May 2024, CV dataset with fuzzified records and a reduced fleet. At selected permanent interstate and non-interstate count stations, overall CV MPRs decreased by 0.5% and 0.3% compared to 2023, respectively. However, the trend in previous years was upward. Additionally, this paper evaluated the impact on data characteristics at freeways and intersections between the 5–11 May 2024, fuzzified CV dataset and a non-fuzzified 7–13 May 2023, CV dataset. The analysis found that the total number of GPS samples decreased 10% statewide. Of the evaluated 54,284 0.1-mile Indiana freeway, US Route, and State Route segments, the number of CV samples increased for 33.8% and decreased for 65.9%. This study also evaluated 26,291 movements at 3289 intersections and found that the number of available trajectories increased for 28.3% and decreased for 70.4%. This paper concludes that data representativeness is enough to derive most relevant mobility performance measures. However, since the change in available trajectories is not uniformly distributed among intersection movements, an unintended sample bias may be introduced when computing performance measures. This may affect signal retiming or capital investment opportunity identification algorithms. Full article

Smart city/IoT frameworks are becoming more complex for the needs regarding multi-tenancy, data streams, real-time event-driven processing, data, and visual analytics. The infrastructures also need to support multiple organizations and optimizations in terms of data, processes/services, and tools cross-exploited by multiple applications and developers. In this paper, we addressed these needs to provide platform operators and developers effective models and tools to: (i) identify the causes of problems and dysfunctions at their inception; (ii) identify references to data, processes, and APIs to add/develop new scenarios in the infrastructure, minimizing effort; (iii) monitor resources and the work performed by developers to exploit the complex multi-application platform. To this end, we developed a semantic unified knowledge model, UKM, and a number of tools for its implementation and exploitation. The UKM, with its inferences, allows to browse and extract information from complex relationships among entities. The proposed solution has been designed, implemented, and validated in the context of the open source Snap4City.org platform and applied in many geographical areas with 18 organizations, 40 cities, thousands of operators and developers, and free trials to keep platform complexity under control, as in the interconnected scenarios of the Herit-Data Interreg Project, which is a lighthouse project of the European Commission. Full article