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Door-to-door travel times in RP departure time choice models: An approximation method using GPS data

Stefanie Peer (), Jasper Knockaert, Paul Koster (), Yin-Yen Tseng and Erik Verhoef ()

Transportation Research Part B: Methodological, 2013, vol. 58, issue C, 134-150

Abstract: A common way to determine values of travel time and schedule delay is to estimate departure time choice models, using stated preference (SP) or revealed preference (RP) data. The latter are used less frequently, mainly because of the difficulties to collect the data required for the model estimation. One main requirement is knowledge of the (expected) travel times for both chosen and unchosen departure time alternatives. As the availability of such data is limited, most RP-based scheduling models only take into account travel times on trip segments rather than door-to-door travel times, or use very rough measures of door-to-door travel times. We show that ignoring the temporal and spatial variation of travel times, and, in particular, the correlation of travel times across links may lead to biased estimates of the value of time (VOT). To approximate door-to-door travel times for which no complete measurement is possible, we develop a method that relates travel times on links with continuous speed measurements to travel times on links where relatively infrequent GPS-based speed measurements are available. We use geographically weighted regression to estimate the location-specific relation between the speeds on these two types of links, which is then used for travel time prediction at different locations, days, and times of the day. This method is not only useful for the approximation of door-to-door travel times in departure time choice models, but is generally relevant for predicting travel times in situations where continuous speed measurements can be enriched with GPS data.

Keywords: Valuation of travel time and schedule delays; Door-to-door travel times; Departure time choice; Revealed preference (RP) data; GPS data; Geographically weighted regression (GWR) (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1016/j.trb.2013.10.006

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