Digital platforms are increasingly becoming the infrastructure for accessing urban services and spaces under platformised cities. However, how this infrastructure is built spatially, and for whom, remains underexplored in planning research. Drawing on platform urbanism (Barns 2020), code/space (Kitchin & Dodge 2011) and Lefebvre's theory of the production of space, this study theorises that platformised space are politically and economically produced rather than simply technologically given. Taking Zhengzhou, China as an empirical case, the research develops three arguments. Firstly, the embedding of platforms into urban space is shaped by both market and state strategy. As the roles of platforms and the state are spatially selective, it produces a stratified digital space. Besides, platforms have reshaped urban spatial form. Some physical spaces have shrunk, while platform-related spaces have expanded, ultimately reorganizing how urban space operates. These spatial changes are rooted in the spatial arrangement by the three forces: the state, capital, and platform companies. In addition, platforms have become a new spatial form and urban infrastructure. For digital planning, this reframes platform infrastructure as representations of space, foregrounding spatial justice and unequal digital accessibility. This research argues that platform urbanisation reshapes urban space by three parties, namely state policy, capital operations and digital platform companies, through representation, production, and infrastructural embedding. This analysis contributes an analytical framework for digital planning that moves beyond technology-neutral narratives and foregrounds the power relations and inequalities.

Urban Digital Twins (DTs) are gaining prominence for their transformative potential with the advancement in artificial intelligence (AI). However, literature on applied examples of transport DTs, especially for buses, remains limited. This study conducts a literature review and thirty-eight stakeholder interviews to map the landscape for AI-enabled, transport-focused DT development in Liverpool City Region in the UK and Busan in South Korea. It identifies key actors, their roles and responsibilities, the relationship among them as well as their motivations, priorities, and standards pursued regarding topics including big data, AI, modelling frameworks, governance, feasibility, and social equity. Based on the synthesis of cross-case findings, this paper proposes a transferable framework for guiding DT development for buses, multimodal transport and beyond, with examples of input data, DT modules, conditions needed and KPIs. Furthermore, it identifies six important considerations for DT implementation and presents practical case examples from Liverpool and Busan. First two points are on governance: fostering multi-stakeholder collaboration and funding; and pursuing DT initiatives at both national and local levels. Two other points are on data: standardising online data-sharing platform with privacy safeguards and data monetisation; and overcoming reliance on private data through data agreements and citizen participation. Final two points are on DT design: adopting modular DT solutions; and matching DT modules with appropriate level of AI-enabled dynamic simulation, visualisation and automation.

Participatory planning theory evolved across several decades through a wide body of often fragmented literature. Foundational traditions, such as Davidoff’s advocacy planning, Arnstein’s ladder of citizen participation, Forester’s communicative planning, Healey’s collaborative planning, and Innes and Booher’s collaborative rationality, provide distinct though often competing interpretations of what meaningful participation consists of and the conditions that are necessary to attain it. This fragmentation produces several structural challenges as Generative AI is increasingly grounded within participatory planning practice. In this context, the discipline lacks an integrated basis for evaluating its implications. Existing research on Generative AI focuses predominantly on technical capabilities, providing limited engagement with the normative positions of participatory planning, as well as insufficient attention to questions of legitimacy and power.

This research argues that the participatory traditions, taken collectively, contain the resources required to assess Generative AI, but only if they are synthesised rather than applied individually. Applying Jabareen's (2009) conceptual framework methodology, the paper derives five evaluative dimensions from undertaking a systematic comparison of the traditions: (1) Access and Representation, (2) Influence and Power, (3) Communication and Trust, (4) Deliberation and Inclusion, and (5) Legitimacy and Outcomes. Each dimension emerges from a pattern of convergence and divergence across the five traditions and generates a distinct set of evaluative questions for Generative AI tools in participatory planning. The framework is presented as a conceptual contribution in its own right, with empirical research identified as the next step to test and validate its dimensions.