Applying GIS-Based Traditional Travel Demand Model for Improved Network-Wide Traffic Estimation: New Brunswick Case Study

Abstract

Traffic volume counts are used by many Departments of Transportation in planning, traffic operations, and asset management programs. Traditionally, four-step model (FSM) is based on traffic analysis zones (TAZs) structure which conveniently uses existing census geography to take advantage of demographic data available from Statistics Canada. This coarse zone structure tends to exaggerate the intra-zone trips resulting in biased and unbalanced trip distribution over roadway network and high estimation errors. Also, estimation of traffic volumes on low-class roads is ignored in most cases. Limitations above have necessitated developing a GIS-based high-fidelity travel demand model (HFTDM) capable of achieving network-wide traffic volume estimation with improved accuracy. This will require using all functional class roadway network and spatially disaggregating census-based coarse TAZ structure into grid-based fine zones based on road density areal interpolation technique. Preliminary results from a case study developed for Beresford area in the Canadian Province of New Brunswick show that the proposed methodology is promising.