Modeling the Evolution of Ride-Hailing Adoption and Usage: A Case Study of the Puget Sound Region
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"Modeling the Evolution of Ride-Hail..." refers background in this paper
...In terms of behavioral survey-based studies, initial work has reported that ride-hailing users are generally younger, more educated, live in urban areas, earn higher incomes, and own fewer cars than the general population (1, 6-9)....
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691 citations
"Modeling the Evolution of Ride-Hail..." refers methods in this paper
...We refer the reader to Greene and Hensher (39) for estimation details, which is relatively straightforward using the maximum likelihood inference approach....
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691 citations
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"Modeling the Evolution of Ride-Hail..." refers background in this paper
..., 2018; Dias et al. 2019; and Komanduri et al., 2018). Dias et al. (2019) fused secondary land use and census data to the RideAustin data to draw inferences about the characteristics of frequent ride-hailing users and ride-hailing trip purposes....
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..., 2018; Dias et al. 2019; and Komanduri et al., 2018). Dias et al. (2019) fused secondary land use and census data to the RideAustin data to draw inferences about the characteristics of frequent ride-hailing users and ride-hailing trip purposes. Wenzel et al. (2019) explored the RideAustin trip data to analyze energy implications of ride-hailing; they estimated that empty trips between servicing rides accounts for 26% of total ride-hailing VMT and the net effect of ride-hailing on energy use is a 41–90% increase compared to baseline, pre-TNC, personal travel....
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..., 2018; Dias et al. 2019; and Komanduri et al., 2018). Dias et al. (2019) fused secondary land use and census data to the RideAustin data to draw inferences about the characteristics of frequent ride-hailing users and ride-hailing trip purposes. Wenzel et al. (2019) explored the RideAustin trip data to analyze energy implications of ride-hailing; they estimated that empty trips between servicing rides accounts for 26% of total ride-hailing VMT and the net effect of ride-hailing on energy use is a 41–90% increase compared to baseline, pre-TNC, personal travel. The studies mentioned above provide rich insights on the nature of ride-hailing trips and empty VMT. However, to our knowledge, no study has explored the temporal behavioral dynamics of ride-hailing use. Of course, the study of changes in traveler behavior over time relates to considerations of temporal stability (or not) of behaviors, which has been examined extensively within the broad context of temporal transferability of travel demand models (see NASEM, 2012 for an exhaustive review of such temporal transferability studies). Some other studies examine travel behavior variability without necessarily tying to transferability, including Marchetii (1994), Meyer (1999), Wu et al. (2013), Miller and Shalaby (2003), Sharaby and Shiftan (2012), Zhao et al....
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