The wisdom of ‘smart’ development increasingly shapes urban sustainability in Europe and beyond. Yet, the ‘smart city’ paradigm has been critiqued for favouring technological solutions and business interests over social inclusion and urban innovation. Despite the rhetoric of ‘citizen-centred approaches’ and ‘user-generated data’, the level of stakeholder engagement and public empowerment is still in question. It is unclear how smart city initiatives are developing common visions according to the principles of sustainable urban development. This paper examines how data governance in particular is framed in the new smart city agenda that is focused on sustainability. The challenges and opportunities of data governance in sustainability-driven smart city initiatives are articulated within a conceptual Framework on Sustainable Smart City Data Governance. Drawing on three cases from European countries and a stakeholder survey, the paper shows how governance of data can underpin urban smart and sustainable development solutions. The paper presents insights and lessons from this multi-case study, and discusses risks, challenges, and future research.

Indoor environmental quality (IEQ) affects occupants’ satisfaction, health, productivity, comfort, and well-being. IoT developments enable better monitoring of IEQ parameters; however, integrating the various types of heterogeneous data from both the IoT and BIM domains is cumbersome and capital intensive, and therefore, limits the potential of smart buildings. Semantic web technologies can reduce heterogeneity issues, which is necessary to facilitate complex IEQ models. An ontology integrating data related to a building’s topology and its static and dynamic properties is still lacking. The outline of this research is twofold. First, a systematic literature review was conducted to find state-of-the-art semantic web technologies related to building topology, static properties, and dynamic properties from the IoT and BIM domains. By graphically reviewing various ontologies, their valuable patterns, commonalities, and best practices were revealed. Secondly, those results were used to develop a new ontology that integrates topological building information with static and dynamic properties. This Building Performance Ontology (BOP) provides a generic upper-level description of properties and two lower-level ontologies representing observations and actuation. The ontology results in intuitive queries and is both horizontally and vertically extensible. Multiple levels of detail are introduced to ensure practical applicability and efficient patterns based on the data modeler’s needs. BOP opens up a new range of research opportunities in the IEQ domain.

With the rapid rural urbanization and new rural construction in China, tremendous changes are occurring in rural built environments and rural household vehicle ownership. However, few studies have examined the relationship between rural built environments and rural household vehicle ownership. In this study, a questionnaire survey of 374 rural households was conducted and the built environment data of seven typical villages in rural Sichuan were collected using Geographic Information System (GIS) technology and on-site measurement. This study aimed to investigate the relationship between the rural built environment and rural household vehicle ownership in China through a multinomial logit (MNL) model. Results show that household structure attributes have the most significant relationship with vehicle ownership, followed by rural built environment attributes and the respondents’ driving skills. In the process of urbanization, with increases in building density, road density, and destination accessibility, an increase in high-carbon vehicle ownership is an inevitable trend among rural households. However, low-carbon-oriented rural planning can effectively control the increase in high-carbon vehicle ownership. For example, the distance between rural households and important destinations, such as hospitals, schools, and markets, should be shortened and rural residents should be encouraged to learn to ride bicycles. Moreover, rural residents riding motorcycles can effectively reduce household car ownership.

For autonomous vehicles (AV), the ability to share information about their surroundings is crucial. With Level 4 and 5 autonomy in sight, solving the challenge of organization and efficient storing of data, coming from these connected platforms, becomes paramount. Research done up to now has been mostly focused on communication and network layers of V2X (Vehicle-to-Everything) data sharing. However, there is a gap when it comes to the data layer. Limited attention has been paid to the ontology development in the automotive domain. More specifically, the way to integrate sensor data and geospatial data efficiently is missing. Therefore, we proposed to develop a new Connected Traffic Data Ontology (CTDO) on the foundations of Sensor, Observation, Sample, and Actuator (SOSA) ontology, to provide a more suitable ontology for large volumes of time-sensitive data coming from multi-sensory platforms, like connected vehicles, as the first step in closing the existing research gap. Additionally, as this research aims to further extend the CTDO in the future, a possible way to map to the CTDO with ontologies that represent road infrastructure has been presented. Finally, new CTDO ontology was benchmarked against SOSA, and better memory performance and query execution speeds have been confirmed.

This study presents a latent class competing risks model to examine the influence of socio-demographics and life course events on car transaction behaviour. The types of car transaction and interval times between car transactions events are incorporated in a competing risk model. To capture unobserved behavioural heterogeneity across the population, the model classifies households into different segments. Results estimated based on retrospective survey data show significant heterogeneity exist in household car ownership decisions. The covariates are found to have different effects on car ownership decisions between different classes. Households in the class labelled “Young households without a car” are more sensitive to life course events related to household composition. Households labelled as “middle-aged and aged households with car(s)” are more sensitive to life course events related to job and house locations.