GIS-based travel demand modeling for estimating traffic on low-class roads

Abstract

Traffic count data are useful for many purposes, but often not available for significant portions of road networks. It would be prohibitive to cover all roads with traditional sensor-based traffic monitoring system, particularly for rural, low-class roads. In cases where traffic volumes are needed but unavailable, travel demand models (TDMs) can be used to estimate such information. A literature review indicates that research work for estimating traffic volumes for low-class roads using TDM is scarce. The majority of previous research used traffic count data-based regressions. The problem of such an approach is that it relies on available traffic counts to develop, calibrate, and validate regression models. Nevertheless, few or no traffic counts are collected on low-class roads, and therefore make it inapplicable. This study implements TDMs for two regions in the province of New Brunswick, Canada to estimate traffic volumes for low-class roads. Geographical Information System-based TDMs using census data and Institute of Transportation Engineers (ITE) Quick Response Method produce forecasted traffic for a significant portion of road network previously without any traffic information and limit the average estimation errors for low-class roads to less than 40%. Available traffic data were increased by 45% in York County and 144% in the Beresford area. The traffic estimation errors are comparable to or better than those reported in the literature, and the forecast traffic volumes provide a solid foundation for identifying high-volume road segments and prioritizing funding. Study results clearly show TDM is a practical, useful, cost-effective way for estimating traffic parameters on low-class roads.