skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Accounting for multi-dimensional dependencies among decision-makers within a generalized model framework: An application to understanding shared mobility service usage levels

Abstract

Activity-travel choices of individuals are influenced by spatial dependency effects. As individuals interact and exchange information with, or observe the behaviors of, those in close proximity of themselves, they are likely to shape their behavioral choices accordingly. For this reason, econometric choice models that account for spatial dependency effects have been developed and applied in a number of fields, including transportation. However, spatial dependence models to date have largely defined the strength of association across behavioral units based on spatial or geographic proximity. In the current context of social media platforms and ubiquitous internet and mobile connectivity, the strength of associations among individuals is no longer solely dependent on spatial proximity. Rather, the strength of associations among individuals may be based on shared attitudes and preferences as well. In other words, behavioral choice models may benefit from defining dependency effects based on attitudinal constructs in addition to geographical constructs. In this paper, frequency of usage of car-sharing and ride-hailing services is modeled using a generalized heterogeneous data model (GHDM) framework that incorporates multi-dimensional dependencies among decision-makers. The model system is estimated on the 2014-2015 Puget Sound Regional Travel Study survey sample, with proximity in latent attitudinal constructs defined by amore » number of personality trait variables. Model estimation results show that social dependency effects arising from similarities in attitudes and preferences are significant in explaining shared mobility service usage. Ignoring such effects may lead to erroneous estimates of the adoption and usage of future transportation technologies and mobility services.« less

Authors:
; ORCiD logo; ; ; ;
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
U.S. Department of Transportation (DOT)
OSTI Identifier:
1480231
Report Number(s):
NREL/JA-5400-72686
Journal ID: ISSN 0967-070X
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Journal Article
Resource Relation:
Journal Name: Transport Policy; Journal Volume: 72; Journal Issue: C
Country of Publication:
United States
Language:
English
Subject:
33 ADVANCED PROPULSION SYSTEMS; spatial dependence; social interactions; attitudinal proximity; values and behavior; shared mobility service usage; latent constructs

Citation Formats

Vinayak, Pragun, Dias, Felipe F., Astroza, Sebastian, Bhat, Chandra R., Pendyala, Ram M., and Garikapati, Venu M. Accounting for multi-dimensional dependencies among decision-makers within a generalized model framework: An application to understanding shared mobility service usage levels. United States: N. p., 2018. Web. doi:10.1016/j.tranpol.2018.09.013.
Vinayak, Pragun, Dias, Felipe F., Astroza, Sebastian, Bhat, Chandra R., Pendyala, Ram M., & Garikapati, Venu M. Accounting for multi-dimensional dependencies among decision-makers within a generalized model framework: An application to understanding shared mobility service usage levels. United States. doi:10.1016/j.tranpol.2018.09.013.
Vinayak, Pragun, Dias, Felipe F., Astroza, Sebastian, Bhat, Chandra R., Pendyala, Ram M., and Garikapati, Venu M. Sat . "Accounting for multi-dimensional dependencies among decision-makers within a generalized model framework: An application to understanding shared mobility service usage levels". United States. doi:10.1016/j.tranpol.2018.09.013.
@article{osti_1480231,
title = {Accounting for multi-dimensional dependencies among decision-makers within a generalized model framework: An application to understanding shared mobility service usage levels},
author = {Vinayak, Pragun and Dias, Felipe F. and Astroza, Sebastian and Bhat, Chandra R. and Pendyala, Ram M. and Garikapati, Venu M.},
abstractNote = {Activity-travel choices of individuals are influenced by spatial dependency effects. As individuals interact and exchange information with, or observe the behaviors of, those in close proximity of themselves, they are likely to shape their behavioral choices accordingly. For this reason, econometric choice models that account for spatial dependency effects have been developed and applied in a number of fields, including transportation. However, spatial dependence models to date have largely defined the strength of association across behavioral units based on spatial or geographic proximity. In the current context of social media platforms and ubiquitous internet and mobile connectivity, the strength of associations among individuals is no longer solely dependent on spatial proximity. Rather, the strength of associations among individuals may be based on shared attitudes and preferences as well. In other words, behavioral choice models may benefit from defining dependency effects based on attitudinal constructs in addition to geographical constructs. In this paper, frequency of usage of car-sharing and ride-hailing services is modeled using a generalized heterogeneous data model (GHDM) framework that incorporates multi-dimensional dependencies among decision-makers. The model system is estimated on the 2014-2015 Puget Sound Regional Travel Study survey sample, with proximity in latent attitudinal constructs defined by a number of personality trait variables. Model estimation results show that social dependency effects arising from similarities in attitudes and preferences are significant in explaining shared mobility service usage. Ignoring such effects may lead to erroneous estimates of the adoption and usage of future transportation technologies and mobility services.},
doi = {10.1016/j.tranpol.2018.09.013},
journal = {Transport Policy},
number = C,
volume = 72,
place = {United States},
year = {Sat Dec 01 00:00:00 EST 2018},
month = {Sat Dec 01 00:00:00 EST 2018}
}