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An Analysis of Weekly Activity Patterns and Travel Expenditure

UCD-ITS-RP-88-08

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Most data sets available for urban travel demand analysis are one-day data containing records of trips made by household members on a given survey day. It is logical to use the one-day time span as a study period because the day is a natural physiological time unit which regulates much of human behaviour as well as being a convenient time unit when administering surveys. In fact many human activities, from sleeping to commuting, recur with one-day cycles for physiological and institutional reasons.

Use of one-day data, however, inherently limits the scope of the analysis (e.g., Goodwin, 1981; Hanson and Huff, 1982). For example, day-to-day variations in travel patterns, activity scheduling, and allocation of travel resources over a multi-day period, have not been extensively analyzed in the past. This is mainly due to the limited availability of multi-day travel data. Presumably for the same reason, effective analytical methods for multi-day travel behaviour have not been developed yet.

Multi-day travel diary data have been used in the past in several studies (e.g., Bentley, et al., 1977; van der Hoorn, 1979; Hanson, 1980; Hanson and Huff, 1982, 1983, 1985; Koppelman and Pas, 1984; Pas, 1985; Pas and Koppelman, 1984, 1985; Golob, 1986; Golob and Meurs, 1986; Kitamura and van der Hoorn, 1987). These studies have shown that rich representation and extended analysis of travel behaviour is possible using multi-day data. Some have attempted to overcome the limitations arising from the use of one-day data. It has been questioned if unbiased representation of travel behaviour is possible at all with one-day data because of the day-to-day variations (Hanson and Huff, 1985). Pas and Koppelman (1984) propose to distinguish intra-personal (longitudinal) variability and inter-personal (cross-sectional) variability and empirically examine the extent of longitudinal variations in activity-travel behaviour of urban residents. Pas and Koppelman (1985) focus on the composition of daily patterns in a weekly activity-travel pattern. Hanson and Huff (1985), on the other hand, identify "typical" days and examine how frequently a person's daily travel patterns belong to typical patterns.

There are several advantages in using multi-day data. Longitudinal observation of repeated travel decisions (e.g., work trip mode choice over a week) will make it possible to examine the stochastic nature of the choice.
Published in Behavioral Modeling in Geography and Planning, ed. Reginald G. Golledge and Harry Timmermans, chapter 19.