Developing an “intelligent” high-fidelity GIS-based travel demand model framework for improved network-wide traffic estimation

Abstract

The four-step travel demand model (FSTDM) is based on coarse rural traffic analysis zones (TAZs) which tends to exaggerate the intrazonal trips resulting in biased and unbalanced trip distribution over the roadway network with high estimation errors. These limitations have necessitated developing a geographic information systems (GIS)-based high-fidelity travel demand model framework (HFTDMF) capable of achieving network-wide traffic volume estimation with improved model accuracy. This requires using an all functional class roadway network and enhancing the census-based coarse TAZ structure with finer-grained spatial resolution TAZs by integrating the travel demand modeling software platform, remotely-sensed images, parcel-based digital property maps, the AZTool aggregation algorithm, and areal interpolation technique. Preliminary results from the Greater Fredericton Area (GFA) showed that increasing the GFA spatial resolution from the coarsest TAZ structure at census tract (CT) level (27 CT TAZs) to the finest TAZ structure at 4252 “fine” TAZs resulted in an improvement to modeling accuracy of R2, by 0.4092 (from 0.2490 to 0.6582) and an improvement in traffic assignment coverage by 46 percentage points (from 29% to 75%).