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Open Access Publications from the University of California

Long Distance Travel in the California Household Travel Survey


The objective of this report is to first review what we know from the literature about long distance travelers, analyze the contents of the long distance travel log of the California Household Travel Survey (CHTS), demonstrate the augmentation of the trip/tour records with destination attractiveness indicators, derive prototypical traveler profiles, and provide amore detailed analysis of long distance tours. The data are from a simplified travel log that asked respondents from households to report all the trips 50 miles or longer they made in the 8-weeks preceding the day they were assigned a full travel diary. The survey instrument used for this reporting is shown in Figure 1. In this report we identify a few issues with the data collected using this travel log, and these issues motivate us to also investigate the long distance travel reported in the daily diary. The range of variables that we can analyze depends heavily on the accuracy with which respondents reported their trips, and we found they were generally more accurate in the daily diary. However, the long distance travel log contains data that span longer periods than 24 hours and therefore a better record of trips with overnight stays away from home. Past studies of long distance travel have found that commuting by people who sought out lower cost housing is a major contributor to long distance travel, and that higher income and employed persons travel more, but there are multiple shortcomings in the literature that we seek to address here. The literature contains a variety of definitions for “long distance” travel, including ones based on distance (e.g., longer than 50 miles, 100 miles, or longer than 100 kilometers) and travel time (e.g., 40 minutes). Long distance travel researchers have considered a variety of indicators including number of long distance trips, activity before and/or after commute, mode used, time of day of trip, and destination (Georggi and Pendyala, 2000, Axhausen, 2001, Beckman and Goulias, 2008, LaMondia and Bhat, 2011, Caltrans, 2015, Shahrin et al., 2014, Holz-Rau et al., 2014). Most studies did not address trip chaining (e.g., people going to a work place, then to a leisure destination, and then back home). Very little analysis is also found in differentiating trips with an overnight stay, despite the important differences between these trips and daily commuting. The choice of analysis in past studies was presumably driven by: a) an emphasis in the literature on trips to and from work; and b) a focus on a single trip by an individual person as the unit of analysis instead of multiple trips from household members. This focus on commute trips is also reflected in the multitude of person factors used to explain variation in travel behavior in past research (Table 1.1). Table 1.1 also shows household and location characteristics that have been considered as determinants of long distance travel behavior. It is also important to note that a few researchers (de Abreu et al., 2006, 2012) consider long distance travel, car ownership, and residential and job location (and the distance between the two) as a system of joint decisions that are best explained using methods that can disentangle the complex relationships among all these behavioral facets. From this viewpoint, long distance travel (particularly for commuters) cannot be separated from the choice of work and home location and should be modeled jointly. The review in Mitra (2016) is particularly useful in mapping recent literature on long distance travel and its determinants. His findings are exactly what one would expect: age, gender, education, employment and occupation, car ownership, household structure, place of residence and workplace as well as housing cost and accessibility influence long distance travel in a variety of ways. His analysis also shows that developing traveler profiles at the level of a household (rather than the individual) is a better choice to understand how and why long distance travel happens, and our analysis follows this lead. In another analysis of CHTS, Bierce and Kurth (2014) identified an issue of underreporting of repetitive trips in the 8-week long distance data. In essence, long distance commuters did not report all their commuting trips. We find that this underreporting may also exist for longer trips, though less severely than it does for shorter ones. Identifying the correct mix of distances and overall volume of travel is particularly important when one seeks to estimate the contribution of VMT from long distance travel to California estimates of VMT (see also Chapman, 2007). 

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