Day-of-Week, Month, and Seasonal Demand Variations: Comparing Flow Estimates Across New Travel Data Sources

Abstract

Transportation planners and engineers are increasingly interested in incorporating demand variations into travel models. Regression models are used to predict and compare variations in permanent traffic recorder (PTR) counts along Texas highways to vehicle-kilometers traveled (VKT) inferred from INRIX’s probe-vehicle data across days of the year. Results suggest INRIX data do not illuminate month-of-year variations in network use, due to random or unexpected shifts in sampling rates, but significant day-of-week differences are clear in both. Furthermore, INRIX appears to capture much more light-duty-vehicle travel than PTRs on Saturdays, but this may be due to location-based services’ over-counting of vehicles carrying multiple mobile devices and/or PTRs’ highway-site bias.