Generating millions of transport scenarios from a large scale digital twin

Abstract In the development of city planning since the middle of the last century, there have been severe difficulties in generating more than a handful of plans to test. This is because we have largely lacked computational methods to generate a systematic evaluation of their performance. City plans have been too big in terms of the number of components comprising their form to test and too small in terms of numbers of alternative plans with which to make meaningful comparisons. However, we have almost reached the point with the advent of big data and massive computer memories where we can break through these barriers and begin to test an order of magnitude greater numbers of plans than anything possible hitherto. In this paper, we demonstrate how we are able to compare a multitude of very simple alternatives, by increasing the speeds of travel on the shortest links which define complex transportation networks, exploring whether or not small changes in large networks generate greater carbon reductions than bigger changes in those same networks. We are able to show that in many cases, this is the case: small changes lead to better plans reflecting lower carbon reduction than big changes. We argue that once we are able explore a wide range of alternatives, we are likely to come across many counterintuitive and counterfactual results that need to be considered where more than one plan is proposed and tested.