Aedas (United Kingdom)

This paper studied commercial space of the Shanghai Railway Station. It summed up construction and development of history railway and railway passenger transportation. It clarified commercial activities of major railway terminal. It summarized appropriate scale and location of different business activities by the tightness of characteristics different commercial activities as well as problems and deficiencies of existing large passenger commercial space. It identified the source of the problem by several field researches on Shanghai Railway Station. This paper summarized different layout features of the passenger terminal commercial space design history and analyzed the differences and reasons. Finally this paper presented specific strategies and ideas of a large railway passenger commercial space design combined with their views of trends for railway passenger comprehensive development.

Designing simulations for urban design not only requires explicit performance criteria of planning standards but a synthesis of implicit design objectives, that we will call ‘purpose rules’, with computational approaches. The former would at most lead to automation of the existing planning processes for speed and evaluation, the latter to an understanding of perceived urban qualities and their effect on the planning of cities.In order to transform purpose rules into encoded principles we argue that the focus should not be on defining parametric constraints and quantities, but on aligning the perceptual properties of the simulations with the strategies of the stakeholders (planner/ urban designer/ architect/ developer/ community).Using projects from the Computational Design and Research group at Aedas [CDR] as examples, this chapter will discuss how an open framework of lightweight applications with simple functionality can be integrated into the design and planning process by using computational simulations as urban design heuristics.

Algorithmic design must start with the observation of physical behaviours.When applied to designing spatial configurations, correlations between occupation, experience and spatial properties as implicit performances require mapping that can be abstracted into notations for algorithmic encoding.The algorithmic descriptions of space are calibrated by physical models.Both algorithms and catalogues of physical models are weighted and compiled into a heuristic design system that enables the generation of spatial configurations based on behaviours of occupation observed in real spaces.The final designs of this Implicit Space course constitute diagrammatic building configurations designed from the inside-out through encoded occupant experiences.