City Science required a speed model to populate gaps in the network where speed data isn’t available (e.g. during certain low-volume time periods or along quieter routes). The aim was not to develop a full 4-stage model to examine these effects, but to create a mechanism to forecast the relevant information with minimal time and complexity.
City Science built a network from the ITN, turning it into a graph of nodes and edges. This was linked to the City Science database of Geotagged / GIS-linked predictor variables. A series of additional variables were created based on the properties of network mathematics. The team then selected a range of non-demand predictor variables and applied the Gradient Boosting Tree algorithm in Python.