Traffic counts are usually collected using sensor-based monitoring tools at limited locations in a network. An extensive literature review from this paper revealed that using traditional factor approach, regression-based models, and artificial neural network models failed to present network-wide traffic/truck volume estimation because they rely on traffic counts for model development and they all have inherent weaknesses. Moreover, their traffic estimates have high estimation errors. Traditionally, four-step model (FSM) is based on traffic analysis zones (TAZs) structure which conveniently uses existing census geography to take advantage of socioeconomic data available from Statistics Canada. However, the coarse zone structure used in such models tends to exaggerate the intrazonal trips resulting in biased and unbalanced trip distribution over roadway network and high estimation errors. Also, their purpose is to guide infrastructure development and therefore, they are not appropriate as a tool for estimating traffic at network wide, including low-class roads. This paper develops a high-fidelity travel demand model (HFTDM) capable of achieving network-wide traffic volume estimation with improved accuracy. This will require using all functional class roadways and spatially disaggregating census-based coarse TAZ structure into fine zones. A case study using an areal interpolation technique, which is based on fine-scale grids, road density and a detailed road network was developed for the Beresford/Bathurst area in the province of New Brunswick. Finally, a few conclusions and recommendations regarding this paper are given. For the covering abstract of this conference see ITRD number E220308.
Abstract