sDNA is now at the core of adapting Flyvberg‘s Reference Class Forecasting megaproject outcomes appraisal – using explainable AI to appraise new town, CBD, road and rail infrastructures:
A SEM-ANN adaptation of reference class forecasting for the assessment of large-scale urban planning vision, Land Use Policy, Zhou, Y., Zhang, L., and Chiaradia* AJF.
Key items of interest are:
- the need to appraise early front-end visioning scenarios based on relatively sparse data
- infrastructure projects appraisal needs to be able to incorporate network impacts which are notoriously difficult to identify and model.
sDNA contributes analyses of existing and proposed scenarios of urban rail network (MTR), road network and pedestrian network to a structural equation model (SEM) to determine significant variables which are fed into an artificial neural net (ANN) giving a form of explainable AI. Pedestrian network is here proxied via road network. The case study is a megaproject in Hong Kong HKSAR, China.
