Testing block subdivision algorithms on block designs

Integrated land use-transportation models predict future transportation demand taking into account how households and firms arrange themselves partly as a function of the transportation system. Recent integrated models require parcels as inputs and produce household and employment predictions at the parcel scale. Block subdivision algorithms automatically generate parcel patterns within blocks. They are useful in preparing missing input parcels to integrated models and in software designed to visualize their results. Evaluating block subdivision algorithms is done by way of generating parcels and comparing them to those in a parcel database. Realistic block subdivision improves input data and could improve spatial accuracy of model predictions, and consequently, transportation demand forecasts. Three block subdivision algorithms are evaluated on how closely they reproduce parcels of different block types found in a parcel database from Montreal, Canada. A standardized block type classification is used that consists of mutually exclusive and comprehensive categories. A statistical method is used for finding a better algorithm and the probability it will perform well for a given block type. Results suggest the Oriented Bounding Box algorithm performs better for warped grids, as well as gridiron and fragmented uniform sites. It also produces more similar parcel areas and widths. The Generalized Parcel Divider 1 algorithm performs better for gridiron and fragmented non-uniform sites. The Straight Skeleton algorithm performs better for loop and lollipop networks and also produces more similar parcel shapes.