Georgia's Commute Atlanta Value Pricing Program: Recruitment Methods and Travel Diary Response Rates

Abstract

The Commute Atlanta program is an instrumented vehicle research program funded by the Federal Highway Administration (FHWA) Value Pricing Program and the Georgia Department of Transportation. One major objective of the multi-year program is to assess the effects of converting fixed automotive costs, such as vehicle registration and insurance fees, into variable operating costs. The main research hypothesis is that given a per-mile pricing system, participants will modify their driving patterns in an effort to reduce their total insurance and other payments, pocketing the savings. The Commute Atlanta project includes the parallel collection of instrumented vehicle data, household socio-demographic surveys, two-day travel diaries, and employer commute options surveys. The research team will monitor the changes in driving patterns and will use statistical analyses of household characteristics, vehicle travel, and relevant employer survey data to examine the relationships between the incentives offered and subsequent travel behavior changes. This paper focuses on the recruitment methods and travel diary response rates for the two-day diary surveys conducted in February/March 2004. As in other instrumented vehicle studies, researchers collected data that allow the comparison of reported diary travel with monitored vehicle travel. However, this paper focuses on a new type of comparison. Because the households had been recruited into the study 8-months prior to the diary study and their vehicles were transmitting activity data during the travel diary period, the research team had their first opportunity to examine whether there were differences in household vehicle activity across those 77% of households that completed the diary data collection and the 23% of those that did not. The differences were significant at both the high and low ends of the travel-reporting spectrum and may have some major implications for evolving household travel survey methods.

Transportation mode detection – an in-depth review of applicability and reliability

“…The automatic generation of travel diaries is studied within the context of TSc because of two main factors: the utility of data collected / derived from such diaries (Prelipcean, Gidófalvi, & Susilo, 2015;Drchal, Certicky, & Jakob, 2015) and the decreasing response rate of respondents to classical travel diary collection methods (Ogle, Guensler, & Elango, 2005). In comparison, the current paper does not investigate TSc solutions only, but rather analyzes the problem of transportation mode detection in multiple research fields.…”

“…One of the most common methods used to gather this type of information is via travel diaries, where users declare how and why they are traveling to their destinations during a certain time period (usually of one day). The classical approaches relied on users filling up paper diaries, having phone interviews, or declaring how they traveled via web forms, which have two main downsides: the participants are under-reporting their trips (Bricka & Bhat, 2006;Wolf, Oliveira, & Thompson, 2003), and the response rate is decreasing (Ogle et al, 2005;Zimowski, Tourangeau, Ghadialy, & Pedlow, 1997). As a solution to these problems, transportation scientists tried to automate parts of these diaries.…”

Geo-ICT in Transportation Science

“…Travel behaviour is one of the areas where GIS has been used for demand modelling of public (Choi and Jang, 2000) and private modes (Choi and Kim, 1996). GIS has been used to model travel choice (Byon et al, 2007;McGowen and McNally, 2007;Bricka and Bhat, 2006;Ogle et al, 2005;Tsui and Shalaby, 2006), destination choice (Chow et al, 2005), location choice (Nicholas et al, 2004;Shelton et al, 2004), mobility (Schlossberg, 2006), and accessibility (Hodge, 1997;Miller and Wu, 2000;Casas, 2003). It has been used for travel time forecasting (You and Kim, 2000), and risk and evacuation models (Church and Cova, 2000;Alexander and Waters, 2000;Horner and Downs, 2007).…”

Variability in Traffic Flow Quality Experienced by Drivers: Evidence from Instrumented Vehicles

“…The Commute Atlanta project has deployed instrumentation in 487 vehicles from 268 households in the 13-county metro Atlanta area and collected second-by-second speed and positional data using GPS devices (12). The analyses presented here employ the subset of the Commute Atlanta GPS data collected between October 21 and December 31, 2003 along the study corridor.…”