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Title: Understanding activity engagement across weekdays and weekend days: A multivariate multiple discrete-continuous modeling approach

Abstract

This paper is motivated by the increasing recognition that modeling activity-travel demand for a single day of the week, as is done in virtually all travel forecasting models, may be inadequate in capturing underlying processes that govern activity-travel scheduling behavior. The considerable variability in daily travel suggests that there are important complementary relationships and competing tradeoffs involved in scheduling and allocating time to various activities across days of the week. Both limited survey data availability and methodological challenges in modeling week-long activity-travel schedules have precluded the development of multi-day activity-travel demand models. With passive and technology-based data collection methods increasingly in vogue, the collection of multi-day travel data may become increasingly commonplace in the years ahead. This paper addresses the methodological challenge associated with modeling multi-day activity-travel demand by formulating a multivariate multiple discrete-continuous probit (MDCP) model system. The comprehensive framework ties together two MDCP model components, one corresponding to weekday time allocation and the other to weekend activity-time allocation. By tying the two MDCP components together, the model system also captures relationships in activity-time allocation between weekdays on the one hand and weekend days on the other. Model estimation on a week-long travel diary data set from the Unitedmore » Kingdom shows that there are significant inter-relationships between weekdays and weekend days in activity-travel scheduling behavior. In conclusion, the model system presented in this paper may serve as a higher-level multi-day activity scheduler in conjunction with existing daily activity-based travel models.« less

Authors:
 [1];  [2];  [3];  [4];  [5]
  1. The Univ. of Texas at Austin, Austin, TX (United States); Univ. de Concepcion, Concepcion (Chile)
  2. Yale Univ., New Haven, CT (United States)
  3. The Univ. of Texas at Austin, Austin, TX (United States); The Hong Kong Polytechnic Univ., Kowloon (Hong Kong)
  4. Arizona State Univ., Tempe, AZ (United States)
  5. National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
U.S. Department of Transportation (DOT); USDOE
OSTI Identifier:
1457662
Report Number(s):
NREL/JA-5400-71824
Journal ID: ISSN 1755-5345
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of Choice Modelling
Additional Journal Information:
Journal Volume: 28; Journal Issue: C; Journal ID: ISSN 1755-5345
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
30 DIRECT ENERGY CONVERSION; activity-travel demand; modeling weekly activity schedules; time allocation; multiple discrete-continuous probit model; weekday and weekend travel

Citation Formats

Astroza, Sebastian, Bhat, Prerna C., Bhat, Chandra R., Pendyala, Ram M., and Garikapati, Venu M. Understanding activity engagement across weekdays and weekend days: A multivariate multiple discrete-continuous modeling approach. United States: N. p., 2018. Web. doi:10.1016/j.jocm.2018.05.004.
Astroza, Sebastian, Bhat, Prerna C., Bhat, Chandra R., Pendyala, Ram M., & Garikapati, Venu M. Understanding activity engagement across weekdays and weekend days: A multivariate multiple discrete-continuous modeling approach. United States. doi:10.1016/j.jocm.2018.05.004.
Astroza, Sebastian, Bhat, Prerna C., Bhat, Chandra R., Pendyala, Ram M., and Garikapati, Venu M. Thu . "Understanding activity engagement across weekdays and weekend days: A multivariate multiple discrete-continuous modeling approach". United States. doi:10.1016/j.jocm.2018.05.004.
@article{osti_1457662,
title = {Understanding activity engagement across weekdays and weekend days: A multivariate multiple discrete-continuous modeling approach},
author = {Astroza, Sebastian and Bhat, Prerna C. and Bhat, Chandra R. and Pendyala, Ram M. and Garikapati, Venu M.},
abstractNote = {This paper is motivated by the increasing recognition that modeling activity-travel demand for a single day of the week, as is done in virtually all travel forecasting models, may be inadequate in capturing underlying processes that govern activity-travel scheduling behavior. The considerable variability in daily travel suggests that there are important complementary relationships and competing tradeoffs involved in scheduling and allocating time to various activities across days of the week. Both limited survey data availability and methodological challenges in modeling week-long activity-travel schedules have precluded the development of multi-day activity-travel demand models. With passive and technology-based data collection methods increasingly in vogue, the collection of multi-day travel data may become increasingly commonplace in the years ahead. This paper addresses the methodological challenge associated with modeling multi-day activity-travel demand by formulating a multivariate multiple discrete-continuous probit (MDCP) model system. The comprehensive framework ties together two MDCP model components, one corresponding to weekday time allocation and the other to weekend activity-time allocation. By tying the two MDCP components together, the model system also captures relationships in activity-time allocation between weekdays on the one hand and weekend days on the other. Model estimation on a week-long travel diary data set from the United Kingdom shows that there are significant inter-relationships between weekdays and weekend days in activity-travel scheduling behavior. In conclusion, the model system presented in this paper may serve as a higher-level multi-day activity scheduler in conjunction with existing daily activity-based travel models.},
doi = {10.1016/j.jocm.2018.05.004},
journal = {Journal of Choice Modelling},
number = C,
volume = 28,
place = {United States},
year = {Thu Jun 14 00:00:00 EDT 2018},
month = {Thu Jun 14 00:00:00 EDT 2018}
}

Journal Article:
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