Track 2.2: Liveable Cities of Tomorrow: Data, Design and Digital Transformation

The presentation will explore how Digital Transformation DX is moving beyond mere technology adoption to fundamentally redefine the quality of life (Liveability) and success (Prosperity) in the 21st century. We will analyse DX not just as a set of tools (AI, IoT, 5G) but as a strategic mandate for building Human Centred Smart Cities and resilient economies. The discussion will detail specific DX applications, from optimizing urban mobility and enhancing public safety to democratizing economic opportunities for small businesses. Crucially, the presentation will emphasize the essential balance between innovation and inclusion, addressing the critical challenges of the Digital Divide and ethical governance to ensure that digital progress leads to shared prosperity for all citizens in the new digital age.

The Yunnan-Vietnam Railway, which was built over a hundred years ago under the leadership of French colonists, is a cross-border and cross-regional railway connecting Haiphong, Vietnam and Kunming, China. As an important linear industrial heritage, it is confronted with the dual challenges of fragmented cultural experience and superficial application of digital technology in the context of the deep integration of the digital economy and cultural tourism. This study focuses on the core issue of digital economy empowering the revival of cultural heritage, exploring how to utilize metaverse technology and artificial intelligence innovation models to break through the limitations of time and space, drive the upgrading of the cultural industry and the innovation of cultural tourism experience methods in heritage corridors, provide new ideas for the intelligent and inclusive planning of cross-regional heritage corridors, and activate the economic value and social vitality of linear cultural heritage. The research establishes a digital empowerment path for cultural tourism in heritage corridors through "scene design - technology empowerment - value distribution". Based on the content and spatial distribution of the heritage, a nonlinear narrative structure of "main historical axis + branch experience network" is designed. Relying on AI personalized recommendation algorithms and combining interactive technologies such as AR historical scene reproduction and VR engineering decryption, customized tourism experience services are realized, which are fully integrated with the local heritage corridor tourism transportation routes and content planning. Optimize the consumption scenarios and revenue-sharing mechanisms of the metaverse through AI prediction of tourists' behaviors, and achieve economic empowerment and sustainable social feedback of cultural heritage. The simulation planning was carried out based on scenes such as the Bisezhai Time and Space Memory Theater and the A-shaped Bridge Engineering Technology Epic, and its effectiveness in enhancing immersive experiences, stimulating community participation, prolonging tourists' stay time, and promoting diversified cultural and tourism consumption was verified through theoretical reasoning. The research emphasizes the dynamic balance between historical authenticity and digital innovation in the metaverse scenario, providing a new paradigm for the digital activation of linear cultural heritage, especially offering theoretical support and implementation paths for how cross-regional heritage corridors empowered by technology can revitalize cultural heritage through the digital economy..

Riyadh is a city that thrives at night. Most of the commercial, recreational, and social activity takes place after sunset, while daylight hours remain underutilized. This proposal explores how activating the daytime economy can enhance urban vitality, improve public health, and increase economic output-while also supporting climate adaptation in one of the world’s hottest capital cities. The project investigates policies and interventions that enable Riyadh to become a more balanced 24-hour city. It focuses on unlocking untapped economic potential by extending business operations, diversifying offerings during daytime hours, and incentivizing early engagement through various initiatives. The approach also highlights the importance of cultural shifts-promoting healthier routines aligned with natural circadian rhythms and reducing peak-hour congestion. AI and digital technologies like augmented reality and digital twins can play a central role: analysing real-time mobility and consumption patterns, identifying spatial and behavioural gaps and optimizing interventions that target specific demographics. Recognizing Riyadh’s extreme heat, the project integrates strategies to enhance outdoor thermal comfort and active mobility. These include urban design solutions such as shaded walkways, improved microclimates, and transit-oriented development. The city’s substantial investment in metro infrastructure offers an opportunity to reshape urban life around accessible, climate-resilient nodes of activity. Gamification can introduce a new layer of engagement - rewarding residents and businesses for participating in daytime activities, encouraging walking, attending events or visiting local businesses. Ultimately, activating the daytime economy is not only a means to economic diversification but also a strategy for resilience. In a rapidly transforming urban landscape, adapting the rhythms of the city to meet climatic and societal shifts is essential for long-term sustainability and quality of life. This project aims to develop a comprehensive strategy to inform both planning policies and interventions in Riyadh’s built environment. By integrating behavioural insights with spatial and environmental analysis, the strategy will guide how spaces are planned, programmed, and designed to encourage activity across more hours, especially during the day. A public survey gathered over 150 responses from Riyadh residents, revealing weekday and weekend activity patterns. It was identified that most of the respondents stay in at home during the day and almost all respondents expressed a desire to be more active during the day, if more accessible, appealing options existed. To further support this analysis, we collaborated with Lean, a Saudi company enabling the healthcare sector by leveraging AI and data analytics. We got insights into consumer behaviour, eating habits, and public health trends, which underscored the deeply ingrained evening-centric lifestyle and raised questions about how public health and consumer behaviour can be influenced to encourage earlier, healthier engagement with the city. A panel discussion and design sprint were held in collaboration with Prince Sultan University to explore opportunities for daytime activation. The panel brought together professionals from the public, private, and third sectors to translate survey findings into actionable strategies. A range of approaches such as enhancing thermal comfort, promoting active mobility, and diversifying daytime business offerings can be leveraged to enable a more balanced rhythm of activity across Riyadh and make public spaces, commercial areas, and cultural institutions more attractive and functional during daytime hours, the city can reduce pressure on night-time infrastructure, improve public health, and diversify its economy. Next, the project will identify a pilot area, such as U-Walk, KAFD, or Diriyah Gate - that offers strong potential for daytime economy activation. These sites feature a mix of commercial, cultural, and public spaces suitable for testing. Key stakeholders, including local authorities and developers, will be engaged to align goals. Through workshops and consultations, the initiative aims to move toward implementation and inform scalable planning strategies citywide.

Background: In the context of sustainable development and economic transition, inefficient use of commercial office buildings is leading to growing spatial mismatch and resource waste. This study takes Nanjing’s central districts as a case to explore vacancy patterns and planning responses. It introduces a dual-index system of vacancy rate and vacancy duration, supported by multi-source data and machine learning techniques, to analyze spatial characteristics and support adaptive strategies for improving urban land use and promoting inclusive economic development. Relevance to the Congress Themes: This study directly supports the congress theme "Urban Economy and the Digital Age" by applying explainable artificial intelligence to commercial space planning. The integration of data-driven diagnostics with planning theory helps cities better understand the structural causes of vacancy and spatial inefficiency. Through a classification model built on Random Forest and interpreted using SHAP values, the study uncovers the roles of social security, market dynamics, and spatial environment in shaping commercial office vacancy. The research highlights how cities can respond to rapid digital transformation with smarter, evidence-based planning strategies. It also addresses the challenges of diversifying urban economies by linking space optimization with economic policy goals. In doing so, it demonstrates how artificial intelligence can support transparent decision-making, enhance multi-scalar collaboration, and inform flexible land use solutions that align with 24-hour city aspirations. The research contributes to global conversations on how to use new technologies to design inclusive and resilient economic systems. Contribution to Planning or Policy Making Practice: This research offers a practical framework for urban planners and policymakers to assess and respond to commercial office space vacancy using explainable machine learning. It introduces a two-dimensional typology that classifies buildings by vacancy rate and vacancy duration, providing a clear basis for spatial diagnosis and targeted intervention. Spatial analysis reveals that long-term high vacancy is not randomly distributed but follows patterns shaped by accessibility, local service quality, and economic conditions. The machine learning model identifies critical factors driving vacancy and presents threshold effects that inform tailored strategies. For example, low levels of economic activity and weak transportation links significantly increase the likelihood of prolonged vacancy. The explainable nature of the model ensures that planners can interpret the results and integrate them into practical decisions. The study recommends a series of planning responses, including promoting flexible building functions, enhancing service infrastructure, improving local accessibility, and aligning economic incentives with spatial priorities. It also encourages the use of urban data platforms and digital tools to monitor vacancy trends and evaluate policy impacts. By applying artificial intelligence to urban economic management, this research helps bridge the gap between data and policy. It contributes to the development of more inclusive and efficient urban economies, supporting sustainable growth and adaptive reuse in high-density cities.

Against the backdrop of the new urbanization strategy and innovation driven development, cities serve as the carriers of modernization, whose development can advance sustainable economic and societal development. Meanwhile, the city's construction is a critical engine for promoting the high-quality development of the urban economy. Since commercial vitality is a key indicator of urban economic development, the quantitative analysis of its vitality can provide a decision-making basis for grasping the law of urban economic development and predicting urban economic trends. As digital technology reshapes urban lifestyles, the interaction between university campuses and urban spaces increasingly impacts the 24-hour city vitality, student driven economy, and mixed use development. However, existing research mainly focuses on spatial morphology analysis at the technical level and rarely explores the dynamic effects of campus city interactions on urban vitality from the perspectives of digital transformation and lifestyle evolution. Therefore, this study breaks through conventional technical planning perspectives to focus on the university campus, a special urban spatial unit that has both closure property and opening nature. Some typical university campus spatial units in Harbin, a city with educational advantages, were selected as case studies. A quantitative index system of university campus spatial morphology across three scales was also constructed. The three scales include region, node, and land plot. The quantitative index system of the campus spatial form was constructed around the dimensions of spatial form, traffic organization, and spatial network. Furthermore, multi source data acquisition methods, including space syntax and big data analytics, were applied to quantify 9 morphology indicators at the regional scale, 13 morphology indicators at the node scale, and 8 morphology indicators at the plot scale. Integrating kernel density analysis of heterogeneous economic activities, we examine commercial vitality patterns while applying geographical detector models to assess correlations between campus spatial form and commercial vitality. This study examines how these impacts reflect contemporary urban lifestyles, providing empirical evidence for the construction of more adaptive and vibrant 24-hour urban spaces, and explores the contributions of campus city interactions to the urban economy. Results reveal that the commercial vitality distribution of different types of spatial units is quite different. Moreover, for different unit scales, the significance of the correlations between the form of spatial units and the vitality of their surrounding formats is also different. Among them, at the regional scale, except that the indicator of connectivity has no obvious effect on commercial vitality, the other indicators are significantly correlated with it, and the road network density index shows the greatest impact, which establishes critical foundations for evidence based mobility infrastructure planning in 24 hour cities. For node scale, the four indicators-choice, connectivity, building coverage ratio, and degree of enclosure-have an impact on commercial vitality. The combined effect of the four indicators significantly promotes the development of mixed use vitality, particularly beneficial for creating social and consumption spaces that meet the all day needs of students. For plot scale, the impact of various indicators on commercial vitality is not significant. Data at the regional scale indicates that campus city spatial interaction is closely related to the urban economy. Additionally, in most cases, the interaction of different morphological indicators enhances their impact on commercial vitality. Importantly, this research confirms that campus city interaction not only affects traditional economic indicators but also profoundly shapes contemporary urban lifestyles, including student-driven 24-hour economy and digital platform supported mixed consumption models. This study can provide theoretical support and practical reference for adaptingto the diverse and constantly changing urban lifestyles through campus planning, to promote campus-city interaction and high quality urban development.

As urban life accelerates and the night-time economy expands, the concept of the 24-hour city is reshaping people’s travel patterns and consumption habits. Digital service technologies like smart navigation and social media shape travelers’ decisions and awareness during daily mobility. Compact city and transit-oriented development (TOD) are key pathways for sustainable growth in high-density Asian cities. Shanghai possesses the second largest metro network in China. And its number of transfer stations and transfer intensity keep rising. Most transfer stations are surrounded by commercial complexes. Thus, understanding the new travel behavior of metro transfer commuters in Shanghai can help transform transit hubs into vibrant urban nodes. However, existing studies mainly focus on non-work stopovers of car users, overlooking public transport, especially in rail transit. Researchers have discussed the impact of socio-demographic attributes and the built environment of residence and workplace on the stopovers. But seldom studies consider the attitude of commuters and the spatial characteristic of stopover locations. Recently, metro smartcard data and mobile signaling data are increasingly used in travel research. However, due to the challenges in data access and limited granularity, these data cannot accurately capture individual transfer paths or actual behaviors. Besides, previous studies used basic regressions that cannot capture the complicated interactions between psychological and environmental factors. To address these limitations, the research incorporated factors related to the built environment and preferential policies into the Decomposed Theory of Planned Behavior (DTPB), developing an extended theoretical framework. We used Structural Equation Modeling (SEM) to explore the mechanisms shaping stopover intentions. Building on this, the study applies a Multiple Indicator Multiple Cause (MIMIC) model to examine the effects of demographic factors and usage characteristics of metro systems and commercial complexes on various psychological constructs. This study conducted an online questionnaire survey of 721 Shanghai metro users between 2024 and 2025. The results of SEM model confirm that commercial complexes surrounding transfer stations can attract metro commuters to engage in non-work stops, which has become a common phenomenon. Most transfer commuters tend to use shopping, catering, and public facilities such as restrooms during stopping. The findings reveal that stopover attitude is the decisive factor influencing commuters’ stopover intention. This attitude can be improved by enhancing the convenience of stopover behavior, increasing its perceived usefulness to commuters, improving the comfort of transfer spaces, and optimizing transfer-related policies. Meanwhile, the convenience conditions for commuters, favorable evaluations from peers and elders, as well as high accessibility, short walking distances, high density, and high diversity of commercial complexes also contribute to the intention of stopovers. Moreover, digital service platforms serve as components of perceived behavioral control and subjective norms, which significantly enhance stopover intentions. Specifically, the availability of digital tools, the visibility of complexes on navigation platforms, and the perceptibility of complexes on social media all contribute to stronger intentions. The results of MIMIC model confirm that there exists group heterogeneity in the relationships between socio-demographic attributes, usage characteristics of metro and commercial complexes, and latent psychological variables. These findings offer practical insights for local governments, metro operators, and commercial developers to create vibrant and multifunctional urban spaces around transfer stations. By integrating behavioral and spatial perspectives, the research contributes to planning approaches that align rail-based infrastructure with evolving urban lifestyles. In addition, the study underscores the role of digital service platforms both in respondent recruitment and in enhancing stopover intentions. The study provides evidence-based strategies for rail transit-commercial synergy, supporting compact and equitable urban development in the context of 24-hour cities.

a) Background In the digital age, the rise of the 24-hour city and its dynamic urban economy demands a new paradigm of governance. Planners are now required to make real-time decisions by understanding the complex interplay between energy consumption, social activities, built environment, and other multimodal data. However, a critical barrier persists: the vast, heterogeneous data from urban sensors and systems lacks semantic compatibility, creating analytical blind spots. This fragmentation prevents accurate cross-domain impact assessment, hindering the development of inclusive and proactive urban planning. b) Research objective, central question or statement or problem addressed This research addresses this critical gap to enhance urban economy management in the digital age. We ask: Can a novel framework, combining knowledge graph with Graph Neural Network (GNN), transform siloed urban data into a unified framework for real-time decision-making? Specifically, we investigate whether this semantic approach enables planners to accurately predict cascading effects across urban systems, allowing them to anticipate unintended consequences and optimize for system-wide performance, thereby fostering a more resilient and equitable city. c) Research and data collection methods Our methodology introduces a semantic integration pipeline to support 24-hour city governance. We use Nanjing as our case study, a megacity balancing historic preservation with smart city transformation in China. Our process has three stages. First, we develop an urban ontology to create a common language for disparate data sources, including street view imagery, remote sensing, GIS, and energy consumption data. This allows isolated datasets to be meaningfully interconnected. Second, we construct a semantically-enriched urban knowledge graph that maps out the complex web of relationships within the city, mirroring how real-world urban systems interact. Finally, we implement a semantic-aware Graph Neural Network to reason over this graph. This AI model can predict how a policy change in one domain, such as a new transportation link, will ripple through other urban systems to impact local business activity and energy use, directly supporting data driven planning for a vibrant and efficient 24-hour city. d) Main findings and their significance for theory or practice The framework's cross-modal predictive capability was validated in some downstream applications; for instance, it successfully revealed a complete, previously hidden impact chain from urban micro-design to macro-economic performance. Specifically, by analyzing the quality of the built environment reflected in street view imagery, the model directly links these visual attributes to downstream impacts like commercial energy consumption and economic vitality indicators, thereby quantifying the tangible contribution of urban design to the 24-hour city economy. It is precisely by capturing these deep, non-linear connections that our AI model achieves a significant 15-40% improvement in cross-domain prediction accuracy compared to conventional statistical or single-modal machine learning models. For theory, this work establishes semantic integration as a prerequisite for urban system analysis, not merely an enhancement. It challenges the prevailing paradigm of domain-specific modeling, which often leads to fragmented urban strategies, and provides a new theoretical lens to view cities as an interconnected network, making it possible to trace how a decision in one sector creates ripple effects in another. For practice, this provides a dynamic decision-support tool that transforms urban planning from reactive problem-solving to proactive system optimization. It gives planners the ability to model and anticipate the cascading impacts of interventions—like adding a new road—across the urban economic system before they are implemented. This foresight is indispensable for cultivating a vibrant, inclusive, and resilient 24-hour city economy in the digital age.