A simultaneous model of household activity participation and trip chain generation
Section snippets
Background and objectives
From a standpoint of consumer behavior, time is the ultimate resource constraint. Financial constraints can be overcome by increasing income and wealth, but there are severe limits in how far one can go in reducing time constraints by purchasing time-saving goods and services. Engel et al. (1990) note that more money allows consumers to buy more of everything, but consumers cannot conceivably do more of everything.
In the 1970s, researchers in the field of travel demand modeling began to realize
The model concept
Our models are based on a three-level causal structure, which is depicted in Fig. 1. We propose that the demand for activities generate trips. Trips then generate travel time. Trip chaining behavior provides a feedback loop from trip demand to activity demand, as people find ways of satisfying activity demand by arranging their travel. Finally, time spent traveling cuts into time available for certain activities, thus limiting activity demand. This last set of feedback loops can be called “time
The data
The data are from the Portland, Oregon 1994 Activity and Travel Survey, conducted in the spring and autumn of 1994 and the winter of 1995. This survey involved a two-day activity diary, which was designed to record all activities involving travel and all in-home activities with a duration of at least 30 min, for all individuals in the household. Our sample consists of 3217 households with 6872 individuals (an average of 2.14 persons per household). This sample represents all of the households
Methodology
Structural equations modeling (SEM) with observed variables is defined by the system:where y is a column vector of p endogenous variables, x is a column vector of q exogenous variables, and ζ is a column vector of the error terms. The structural parameters are the elements of the three matrices:
B is the matrix (p × p) of direct effects between pairs of the p endogenous variables; Γ the matrix (p × q) of regression effects of the q exogenous variables, and the symmetric
The base model
Our base model has the first nine endogenous variables listed in Table 4 and 13 exogenous variables listed in Table 5. There are 28 direct effects among the nine endogenous variables, and each of these effects is shown as an arrow in the flow diagram of Fig. 2; these are freely estimated elements in the B matrix of system (1). In addition, there are 58 regression effects from 13 exogenous variables (Γ matrix elements), nine error-term variances (diagonal elements in the Ψ matrix), and one
Summary
These models are founded on the testable hypothesis that demand for out-of-home activities, broken down by type, causes households to generate trips in simple and complex chains, and the combination of demand for different activities determines, in part, the complexity of trip chaining. The trip chains in turn distribute travel time, broken down by travel to sites for the different types of activities and return-home travel. The activity demand and travel time variables account for all
Acknowledgements
This research was sponsored by the University of California Transportation Center. The author wishes to thank his colleague Mike McNally of the University of California, Irvine, for his intellectual stimulus and technical support in the conduct of this and related research conducted under the auspices of the Center for Activity Systems Analysis of the Institute of Transportation Studies. The author also wishes to thank Keith Lawton and the staff of Portland Metropolitan Service District
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2022, Transportation Research Part D: Transport and EnvironmentCitation Excerpt :For instance, travelers seem to engage in more complex work tours during peak hours, possibly due to the fact that most commute tours happen during peak hours (J G Strathman & Dueker, 1995). In addition, travelers tend to have different travel purposes and schedules on weekdays and weekends (Golob, 2000). Day of the week also influences the relationship between mode choice and trip chaining decision-making.