As cities increasingly adopt ‘smart’ approaches to urban sustainability challenges, there is a growing need to understand how the related data-driven technologies can meaningfully reflect citizens’ lived experiences. This article presents a case study of Cotham Hill, a pedestrianised street in Bristol, UK, where citizen engagement and sensor-based monitoring were used to understand the socio-technical impacts of a low-traffic neighbourhood. The research combines environmental sensing via Smart Citizen Kits, a survey, and thematic analysis of lived experiences to examine the contested transformation of the shared urban space. We document how residents sought to evaluate the effects of pedestrianisation on their quality of life by engaging in data collection efforts. Our findings show that experiential plurality across demographics, perceptions of fairness, and temporal patterns of disturbance shaped both the response to the intervention and the potential for socially just redesign. We, therefore, propose a conceptual process model towards a ‘responsive smart city intervention’. The study contributes to debates on citizen-sensing and participatory approaches to smart city development, illustrating the value of multiple forms of knowing in revealing how interventions towards smart and sustainable city development are negotiated on the ground.

Backend data-sharing in Mobility-as-a-Service (MaaS) ecosystems involves multiple business entities—including MaaS providers, mobility operators, payment processors, and regulators—coordinating real-time services across organizational boundaries [2–4]. Privacy-Enhancing Technologies (PETs) such as Federated Learning, Secure Multi-Party Computation, and Differential Privacy offer technical solutions for safeguarding sensitive data in these workflows, but they introduce complex trade-offs in performance, cost, utility, and compliance [2, 3]. Existing PET literature often emphasizes user-facing or cloud-level applications, leaving a gap in structured decision support for inter-organizational PET selection in B2B environments [7]. This paper presents an ontology-based framework for modeling PET trade-offs in MaaS backend flows [2]. Our contributions include: (1) the formalization of PET-related trade-offs and stakeholder priorities, (2) the development of a Web Ontology Language (OWL)-based ontology supporting PET reasoning, and (3) two use cases—’booking flow’ and ’federated coordination’—illustrating how the ontology enables structured decision support across stakeholder roles and data-sharing scenarios [2].This work addresses a gap identified in prior MaaS and PET literature: the absence of structured decision-support for backend, multi-stakeholder data sharing. The proposed ontology is intended as a foundation for simulation-based and empirical validation in future studies.

Bridges are infrastructure assets which connect communities and in cases of collapse or reduced service, these vital transport links on which citizens depend for livelihoods, supplies and mobility are severed. The UK’s aging infrastructure has been referred to as an ‘asset time bomb’. There is also a concern that as climate changes, these aging bridges are increasingly going to have to withstand more extreme flood events accompanied by faster flowing water in riverine environments. These environmental factors may result in the bridge stock being more susceptible to the effects of scouring. Scour is the removal of material (usually soil) from around an object (e.g. a bridge pier) due to the action of flowing water. Scour often occurs in fast flowing river systems and in coastal environments. Scour is a complex soil-water-structure interaction process. This complexity makes the estimation of the amount of scour challenging when using empirical and semi-empirical approaches which often use only a few parameters such as: pier width; pier length; average particle size of the riverbed material; approach flow depth and velocity; critical velocity; angle of attack; pier shape and spacing. Scour being a complex process is also challenging to measure reliably in the field with some methods being suitable in some circumstances but not in others. This paper reports results of a study conducted at the University of Bristol which investigated the relative accuracy of eight scour prediction methods using some (or all) of said parameters. The influence of the measurement device used to originally measure the scour depths e.g. Ground Penetrating Radar, Fathometer, Bludworth Fathometer, Acoustic Doppler Current Profiler and Ambient bed in the field and how the accuracy within each of these sub-categories compares (for each method) to the prediction of the laboratory measurements is examined in detail in the paper. Recommendations are made for asset owners and policymakers on the relative merits of the different measurement approaches, when deploying scour monitoring systems in the field via use of a scour rating framework developed recently by the authors.

Model-based systems engineering (MBSE) represents a move away from the traditional approach to systems engineering. MBSE has the potential to promote consistency, communication, clarity and maintainability within systems engineering projects. MBSE also has the potential to address one of the well-known issues of the systems engineering process—the late discovery of errors or design faults. In this article, the development of the “Spacecraft early analysis model” (SEAM) is detailed and the current version is presented. The SEAM is a model-based framework developed by the authors to define, execute and analyze spacecraft structure and behavior during preliminary design. The SEAM comprises multiple modules (Project, Requirements, Mission, System, Operations) that enable the definition of the spacecraft and its supporting systems, and enables each to be updated independently. The SEAM incorporates a novel behavioral pattern that structures the modes and functions of the spacecraft using state machines and activities. Using this pattern, the behavior itself is not prescribed, as is common in similar model-based representations of spacecraft. The user defines individual functions and modes, and the user then simulates the behavior of the spacecraft in response to a concept of operations (ConOps). In this article, the need for the SEAM, the development approach, the SEAM composition and the limitations of the SEAM are presented and discussed.

Mobility as a Service (MaaS) is an innovative approach that makes mobility more efficient, sustainable and seamless. In particular, through the integrated use of digital ticketing solutions within MaaS systems, transportation has become more accessible and user-friendly. However, existing cybersecurity measures for logistical support and ticketing systems lag behind the complexity of these large and interdependent ecosystems. These challenges threaten the reliability and sustainability of MaaS ecosystems, disrupting their functioning and putting the privacy of passenger data at risk. Therefore, identifying risks and providing preventive solutions is a critical necessity for the success of such systems. This study presents an overview of logistical support solutions for MaaS, with a specific focus on state-of-the-art digital ticketing technologies. It identifies key cyberthreats faced by MaaS systems through a comprehensive risk assessment and considers the role of Distributed Ledger Technologies (DLT), especially blockchain-based ticketing, in mitigating these risks.

The EU power market system has successfully maintained a centralized governance structure ensuring stable electricity supply and affordable prices for over two decades. However, the ongoing energy transition towards carbon neutrality has exposed critical governance limitations, leading to challenges in community projects implementation. Given that Heating and Cooling (H&C) accounts for more than 50% of the EU’s energy consumption, community H&C initiatives can drive local energy transitions and support renewable integration. This study analyzes the best practices from European community energy initiatives, supplemented by insights from the Energy Leap project. By employing a comparative analysis approach, the study proposes a technically sound and regulatory feasible governance model, alongside a robust ecosystem support framework. The proposed framework introduces new roles and new forms of partnerships between communities—private entities and consumers—taking advantage of the benefits offered by the operation of Energy Communities (ECs), enhancing community engagement and regulatory adaptability. These insights offer practical guidance and contribute to effective policymaking in support of the EU’s energy transition objectives.

Scour is a cause of the failure and often subsequent collapse of many bridge structures. Therefore, owners and operators of hydraulic structures such as bridges need to appraise the scour risk to these important infrastructure assets. To determine if scouring has affected a bridge, visual inspection data is still commonly used by bridge engineers. However, this approach has various limitations, e.g. operations cannot be undertaken safely by human divers during flood events and visual inspection may sometimes yield inaccurate data about scour extent due to e.g. refilling of scour holes overtime. Early detection of the onset of scour can ensure time for intervention and for the planning of alternative transport routes and thus some of the network service losses during and immediately after extreme weather events can be avoided. To have access to reliable data on scour, it is important to specify and deploy monitoring equipment for detecting the rate and extent of scour. In this paper, an updated framework to assist engineers with the selection of the most suitable instrument or instruments for monitoring scour around riverine bridges is presented. The existence of different types of monitoring devices and approaches requires a framework for selecting the most proper device for a specific field site or sites, as each device has its benefits and challenges with regards to, for instance: purchasing costs; ease of installation; maintenance requirements; operational resilience against debris/ice and provision of accurate data. The proposed framework was developed after an extensive review of other scour monitoring device selection frameworks. Then a pilot study was conducted at the University of Bristol to refine the framework which has now been tested by undertaking a series of interviews with practitioners. The results of the preliminary testing of the new framework are presented in detail in this paper. The paper concludes with recommendations for how rating frameworks can be used as part of the efforts to transition to ‘smarter infrastructure’.

Urbanisation has led to urban population growth affecting the economy and the environment, including degrading air quality via pollution. Air pollution has been linked to a variety of conditions and health risks including heart disease, stroke, asthma, Alzheimer's and neurodevelopmental disorders. However, it is difficult for a citizen to find precise air pollution data at a particular location. Smart City strategies usually stipulate that city councils should focus on delivering platforms for active citizen participation using existing technology. Existing civic data hubs such as the London Datastore, Open Data Bristol etc., provide air pollution data but lack elaborate representations for user-defined locations. Existing air quality initiatives such as the Smart Citizen platform and Sensor.Community provide more advanced graphical representations. However, they restrict themselves to showing data coming from their respective devices. The paper presents the Open City Air Quality Platform (OpenCAQP), a development that merges a wide range of data sources and air pollution parameters into a single platform. The OpenCAQP allows citizens, environmentalists, data analysts, and developers to access and visualise data. The proposed solution contributes to two key objectives: i) analysis of the air pollution data sources available in a city; ii) a replicable scalable, modular open source capability aggregating and visualising air pollution data from multiple sources. Its effectiveness has been evaluated by measuring quality, usability and increased awareness of users through a feedback questionnaire.

This paper investigates using Large Language Models (LLMs) within Model Based Systems Engineering (MBSE) as the basis for generative design tools for spacecraft. This study has developed tooling for automatically generating system architecture, functions, modes, and components from an initial requirement set. Specifically, a Python tool was developed to couple the Capella MBSE tool to an LLM in order to facilitate a rapid generative design process. The approach was tested by application to three system design tasks: a European Space Agency Earth observation mission, a CubeSat payload design, and a masters' degree level group design for an Earth observation spacecraft. For each, generated outputs were evaluated against those produced by technical designers. It was found that the generation of system modes and components was of good quality, providing high traceability and alignment against requirements, and also providing generated architectures that in some areas were more detailed than human-generated equivalents. Further development could provide spacecraft system engineers with an 'AI design assistant', as human input is still at the centre of the process and appears necessary to ensure a high-quality output.

Indoor air quality (IAQ) is a crucial aspect of the performance of the built environment that influences the health and wellbeing of occupants. A digital twin (DT) is an emerging technology that enables mirroring of real-world conditions to optimize building operations in anticipation of improvements of the occupant experience. However, existing studies and applications lack a holistic approach using digital twin technologies to quantitatively analyze the interactions between factors affecting indoor air quality. To fill this gap, two cases in a university building in Bristol were selected for in-depth study. IAQ was measured with sensors and simulated using computational fluid dynamics (CFD) in four scenarios. The results show that air temperature and humidity were not significantly affected across different scenarios, whereas CO₂ concentration varied distinctly under different conditions. In the four scenarios, the deviations of the CFD-simulated CO₂ outcomes from real-world measurements were 0.9%, 6.4%, 2.6%, and 2.8% respectively, demonstrating that the CFD simulation can predict and manage indoor air quality with high accuracy. This research reveals the extent to which IAQ factors (e.g., temperature, humidity, CO₂) are affected by window or door operation, room size and occupant distribution. Accordingly, measures for applying multi-functional sensors to monitor building conditions, simulating indoor air quality and optimizing building operations are proposed with the support of digital twin technologies.

The current Electric Vehicle (EV) adoption models target the factors impacting the adoption on a national scale. However, these models fail to explain the observed variability in EV adoption within one nation. To that end, this study investigates potential factors affecting the variability of EV adoption across a nation, taking England as a case study. The examined factors include electricity and gas consumption, photovoltaic installations, and the total number of privately registered vehicles. These factors were analysed in the form of growth rates spanning from 2015 to 2021. The Spatial Error Model was used in the spatial regression analysis of the proposed factors. The obtained model (R2 = 85.6%) indicates that the variability of EV adoption in England can be explained by economic and environmental factors, with the highest impact on the adoption variability attributed to the reduction rate in electricity consumption.

While existing approaches that integrate IoT and blockchain technologies have demonstrated efficiency, they often exhibit limitations by focusing on a single application scenario. These proposals require specific hardware configurations, resulting reduced adaptability and manageability when transitioning to new application scenarios and updating device configurations. Though, an additional management layer could mitigate vulnerabilities, it may also introduce performance and privacy issues, particularly as the system scales up rapidly. In response, this paper introduces an architecture for Blockchain of Things (BoT) systems, providing the flexibility to accommodate multiple application scenarios within a unified hardware infrastructure. Our methodology utilizes smart contracts for system orchestration, offering an agile and cost-effective alternative. The proposed architecture is designed mainly for IoT environments, ensuring scalability without the need for centralized authority or causing network centralization and associated vulnerabilities. Moreover, it facilitates real-time application scenario transitions through smart contracts. The obtained results demonstrate the proposal's capability to scale effectively in network size, and handle varying numbers of requests within each identified scenario type. Furthermore, the results support adoption of smart contract-based scenario transitioning in BoT environments.

Quantum technologies are likely to raise significant benefits but also challenges to the continued development of smart cities. This paper looks at one of the more operationally ready of the quantum technologies, namely quantum sensing, and considers some of the opportunities and challenges facing city planners if they wish to introduce the capabilities of quantum sensing. The paper briefly outlines the evolution of quantum technologies and where quantum sensing fits in the quantum portfolio. It then considers two quantum sensing capabilities in more detail, namely the use of quantum sensing in navigation and in detection. The paper then describes the attributes of smart cities and discusses the opportunities and challenges of the introduction of these two quantum sensing capabilities, and outlines potential mitigation strategies to optimize the benefits while addressing the challenges.

Transportation systems typically follow predefined routes, leading to a cumbersome experience, especially for users relying on multiple services daily. Mobility-as-a-Service (MaaS), proposed as a solution, integrates various mobility devices and services, including bicycles, scooters, cars, taxis, bike-sharing, and more on a single digital platform, streamlining journey planning, ticketing, and personalized travel experiences. MaaS offers various transformative benefits such as a single digital platform unifying available mobility services a cost-effective, and seamless single-ticket experience for journeys. However, there are certain challenges to be addressed such as transparency, information asymmetry, and system complexity persist. To address these challenges, Blockchain technology emerges as a solution, providing decentralisation, security, and transparency. In this work, we analysed the proposals in the literature and industry, then we identified essential features and metrics for Blockchain-supported MaaS (BMaaS) systems. Lastly, we propose a conceptual design of a scalable and unifying BMaaS system, named SmartMoveChain, addressing the existing issues in the current MaaS systems.

Today’s MBSE tools and environments are highly varied and therefore present a challenge for organizations looking to implement MBSE. Furthermore, while MBSE environments are highly capable of supporting the description of design baselines, the current capabilities within these environments could be further refined for exploring alternative designs. As a result it is important to gain an understanding of the limitations of current MBSE tooling in performing the valuable activity of design space exploration, and identify a set of candidate techniques to combat these. This paper reviews the various options available to MBSE practitioners by comparing some of the most common MBSE languages, tools and methods. The possible issues that can be encountered when exploring different designs have been identified and assigned a severity rating. A set of design space exploration techniques are presented, and where possible these have been sourced from existing literature. A knowledge graph has been constructed to collect all this data into a structured format, containing all the MBSE languages, tools, methods, design space exploration-related issues and techniques, as well as the relationships between each of these. This knowledge graph, implemented as a Neo4j graph database, allowed deeper insights to be drawn from the collected information. By defining a selected MBSE environment, including language, tool and method, the knowledge graph could be used to identify the least troublesome sequence (with minimum number of related issues) to arrive at a desired design artifact, for example a set of optimized system parameters. Beside this, the knowledge graph could be used to display the relationships and clusters of MBSE languages, tools and methods, to assist organizations with selecting suitable MBSE environment elements. Future work will bring greater depth to the analysis available with the knowledge graph, for instance, differentiation between different types of design space exploration issues and techniques.

AbstractDigitalisation and the Internet of Things (IoT) help city councils improve services, increase productivity and reduce costs. City‐scale monitoring of traffic and pollution enables the development of insights into low‐air quality areas and the introduction of improvements. IoT provides a platform for the intelligent interconnection of everyday objects and has become an integral part of a citizen's life. Anyone can monitor from their fitness to the air quality of their immediate environment using everyday technologies. With caveats around privacy and accuracy, such data could even complement those collected by authorities at city‐scale, for validating or improving policies. The authors explore the hierarchies of urban sensing from citizen‐to city‐scale, how sensing at different levels may be interlinked, and the challenges of managing the urban IoT. The authors provide examples from the UK, map the data generation processes across levels of urban hierarchies and discuss the role of emerging sociotechnical urban sensing infrastructures, that is, independent, open, and transparent capabilities that facilitate stakeholder engagement and collection and curation of grassroots data. The authors discuss how such capabilities can become a conduit for the alignment of community‐ and city‐level action via an example of tracking the use of shared electric bicycles in Bristol, UK.

The definition of complexity is contentious. The lack of a univocal definition for complexity is leading to a cacophony of different views and lexicons, limiting the collective ability to handle complexity effectively. This article aims to resolve this impasse by identifying or developing a useful and acceptable definition of complexity by focusing on how this term is used in practice, and hence determine which definition resonates with the broadest possible community. To understand how the term is being used, several popular complexity-definitions have been identified and de-constructed into component definitional elements. These elements are then compared to the descriptions of complexity in a range of key community documents to infer which definitions most align with the document authors usage of the complex term. The results indicate that new emergent definitions are far more popular than the established dictionary and complexity theory definitions, supporting previous surveys. These usage insights are then analyzed to develop a definition that can unify, as much as possible, the different complexity perspectives. The proposed definition establishes that complex(ity) is when relationships between elements are weaved together such that they are not fully comprehended, leading to insufficient certainty between cause and effect. This definition was tested at a series of international workshops and accepted as the most useful definition.

This position paper argues that outcome-driven Decision Support Systems (DSS) are more appropriate than current data-driven approaches to integrate Multi-Domain Models (MDMs) in the context of sustainable urban-regional planning. This is because, for urban planning, DSS needs to provide fit-for-purpose answers to problems faced by practitioners when managing and designing city features. In fact, planners must cater for imagined scenarios, assessing viable solutions with regards to how they change in accordance with ever-changing constrains, to be able to improve cities towards becoming sustainable and resilient. The authors frame urban-regional planning as a design activity in which complex and interwoven requirements from multiple stakeholders need to be addressed by decisions involving multidomain knowledge. To this end, the paper highlights main characteristics of design activities, and then compares and contrasts data-driven strategies with outcome-driven strategies. This promotes the development of an outcome-driven DSS that better aligns with the needs of planners working with urban-regional development towards enhancing urban livability and addressing territorial inequalities. It then frames the role of data-driven strategies within outcome-driven DSS, showing how the DECIDE project embraces the creation of an outcome-driven DSS with punctual data-driven strategies.

Machine Learning security is vital for the safe operation of Autonomous Vehicles. When Autonomous Vehicles are connected and cooperating, they form a system of systems that have shared objectives. However, adversarial environments and adversarial vehicles in the system can cause security challenges for the whole system. Current research focuses on the Machine Learning security challenges from the perspective of a single vehicle. We argue that there is a need to consider these security challenges from the perspective of multiple interconnected vehicles, as a system. In this paper, we explore these challenges from the perspective of many Connected Autonomous Vehicles as a system with respect to Machine Learning security. We include attack scenarios that demonstrate the system interactions that can lead to cascading failures, which test the resilience of the system. We also outline some of the challenges in researching this perspective, where a key challenge is identifying indicators and metrics to describe the system resilience when under attack. To observe the system, experimentation via simulation is identified as a suitable environment that can capture the complex and dynamic system interactions in this security context.