The relative effect that each of a wide variety of factors has on the extent to which a traveler will chain trips was investigated. The objectives were to empirically determine which factors influence a traveler’s tendency to chain two or more trips within one tour, as well as the relative significance of these considerations; to more specifically determine the level of influence that urban centers have on trip chaining; and to evaluate the potential effects on trip-chaining behavior of specific transportation demand management (TDM) strategies through examination of variables that describe effects associated with TDM. A negative binomial regression model was developed in which the number of trips in a chain is related to household characteristics, traveler characteristics, trip characteristics, and urban form. After the model was estimated, the significance of individual variables was analyzed. Characteristics from each of these categories were found to be statistically significant. A number of the significant variables help to describe effects of specific TDM strategies, and the relative effects of these variables on trip-chaining behavior were addressed. Some of the variables representing TDM strategies increased the level of trip chaining, whereas other variables decreased the level of chaining. Potential policy conflicts between trip chaining and specific TDM programs are discussed.

Most trip generation models are insensitive to the effects of transportation demand management (TDM) strategies. To evaluate the potential effectiveness of TDM solutions, transportation professionals must rely largely on the results of case studies, which cannot be generalized for all urban areas. To evaluate TDM strategies in a context that is sensitive to the unique characteristics of each urban area, TDM strategies should be incorporated into regional travel demand models. Five TDM strategies affecting trip generation rates are examined: telecommunications, alternative work schedules, on-site amenities, pricing strategies, and land use strategies. To analyze these strategies, data from the Puget Sound Transportation Panel (PSTP) were used. Variables derived from the PSTP data that may help explain the impacts of these strategies were evaluated for significance in trip generation models for several home-based and non-home-based trip purposes. The trip generation models were specified using Poisson and negative binomial regression techniques. After the models were estimated, the significance of the variables representing the impacts of TDM strategies was analyzed. Many of the TDM variables were indeed significant in the trip generation models; however, in some cases, the significance of the variables can be attributed to factors such as trip chaining, which does not describe the effects of TDM strategies. Additional research is needed to fully determine the effects of trip chaining on the variables examined in this study, and additional data could enable the development of variables that more accurately describe the effects of TDM strategies.

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