Alternative Activity Pattern Generation for Stated Preference Surveys
Name
AAPG_2017-11-10_REVISION.pdf
Description
Accepted version
Size
415.3 KB
Format
Adobe PDF
Checksum (MD5)
da6cb63ecc0396bf035a5b45e94aec38
Author(s)
He, He
Atasoy, Bilge
Brazier, J. Cressica
Zegras, Pericles C
Ben-Akiva, Moshe E
Date Issued
December 2018
Journal
Transportation Research Record
Publisher
SAGE Publications
Citation
He, He et al. “Alternative Activity Pattern Generation for Stated Preference Surveys” Transportation Research Record, vol. 2672, no. 47, 2018, pp. 135-145 © 2018 The Author(s)
Version
Author's final manuscript
Abstract
We present a systematic method for generating activity-driven, multi-day alternative activity patterns that form choice sets for stated preference surveys. An activity pattern consists of information about an individual’s activity agenda, travel modes between activity episodes, and the location and duration of each episode. The proposed method adjusts an individual’s observed activity pattern using a hill-climbing algorithm, an iterative algorithm that finds local optima, to search for the best response to hypothetical system changes. The multi-day approach allows for flexibility to reschedule activities on different days and thus presents a more complete view of demand for activity participation, as these demands are rarely confined to a single day in reality. As a proof-of-concept, we apply the method to a multi-day activity-travel survey in Singapore and consider the hypothetical implementation of an on-demand autonomous vehicles service. The demonstration shows promising results, with the algorithm exhibiting overall desirable behavior with reasonable responses. In addition to representing the individual’s direct response, the use of observed patterns also reveals the propagation of impacts, that is, indirect effects, across the multi-day activity pattern.
MIT Department
Massachusetts Institute of Technology. Department of Architecture
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1177/0361198118782760
