Trip Chaining

A trip chain is a sequence of trips taken to pursue different activities. A simple trip chain might take the form of home–activity–home, namely, a trip chain with only one stop and two trip legs. Different taxonomies of multi-stop trip chains are possible, depending on the trip purpose and mode of travel. For example, a sequence of trips might be considered a shopping-trip chain or work-trip chain because work and shopping activities are both included. These trips may be taken by car only or a series of modes (e.g., walk, carpool, transit, etc.); the latter case may also be considered a mode-chaining travel behavior because it involves more than one mode.

In trip chaining, trips may be taken by the same person or different persons in the same or different households. In addition, socioeconomic and demographic characteristics (for example, age, gender, income, auto availability, multiple-worker households, and presence of children in the household) as well as transportation system changes may play a role in the realization of different trip-chaining patterns.

A one-worker household might reach five destinations within the same trip chain with six trips. A second household might reach only three destinations in six trips. It appears that bundling more trips [Page 1613]in a single chain results in a more efficient behavior. Some of the unintended consequences of longer trip chains may include longer commutes and longer morning and evening peak travel periods.

Trip-chaining behavior has important implications for travel forecasting. Traditionally, in urban travel forecasting, different trip purposes are estimated separately for home-based and non-home-based activities before combined and assigned to transportation networks.

The underlying assumption is that each trip from one location to a second location is independent of a subsequent trip from the second location to a third location. In that assumption, when trip-chaining behavior increases—given a constant total number of trips—the number of estimated home-based trips would be underpredicted while the number of estimated non-home-based trips would be overpredicted. It is important, therefore, to understand and account for trip interdependencies in trip-chain formation to improve the sensitivity of travel demand forecasting models. Considerable effort has been devoted to the study of trip-chaining behavior and has resulted in conceptual and methodological advancements.