Logistic regression models for predicting trip reporting accuracy in GPS-enhanced household travel surveys



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Texas A&M University


This thesis presents a methodology for conducting logistic regression modeling of trip and household information obtained from household travel surveys and vehicle trip information obtained from global positioning systems (GPS) to better understand the trip underreporting that occurs. The methodology presented here builds on previous research by adding additional variables to the logistic regression model that might be significant in contributing to underreporting, specifically, trip purpose. Understanding the trip purpose is crucial in transportation planning because many of the transportation models used today are based on the number of trips in a given area by the purpose of a trip. The methodology used here was applied to two study areas in Texas, Laredo and Tyler-Longview. In these two study areas, household travel survey data and GPS-based vehicle tracking data was collected over a 24-hour period for 254 households and 388 vehicles. From these 254 households, a total of 2,795 trips were made, averaging 11.0 trips per household. By comparing the trips reported in the household travel survey with those recorded by the GPS unit, trips not reported in the household travel survey were identified. Logistic regression was shown to be effective in determining which household- and trip-related variables significantly contributed to the likelihood of a trip being reported. Although different variables were identified as significant in each of the models tested, one variable was found to be significant in all of them - trip purpose. It was also found that the household residence type and the use of household vehicles for commercial purposes did not significantly affect reporting rates in any of the models tested. The results shown here support the need for modeling trips by trip purpose, but also indicate that, from urban area to urban area, there are different factors contributing to the level of underreporting that occurs. An analysis of additional significant variables in each urban area found combinations that yielded trip reporting rates of 0%. Similar to the results of Zmud and Wolf (2003), trip duration and the number of vehicles available were also found to be significant in a full model encompassing both study areas.