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

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Vinayak, Pragun

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Activity-travel choices of decision makers are influenced by spatial dependency effects. As decision makers 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 decision makers is no longer solely dependent on spatial proximity. Rather, the strength of associations among decision makers 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 thesis, the frequency of usage of car-sharing and ride-sourcing services, collectively termed as shared mobility services, is modeled using a sequential generalized heterogeneous data model – spatial ordered response probit (GHDM - SORP) 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 inter-dependence in attitudinal space defined using latent psychometric constructs reflecting inherent attitudes, lifestyle preferences and habits. These latent constructs are based on variables in the data set that represent observed travel and locational choice behavior, as well as responses to attitudinal questions. Model estimation results show that social dependency effects arising from similarities in attitudes and preferences are significant in explaining shared mobility service usage, over and above what is explained by spatial dependency. Ignoring such effects may lead to erroneous estimates of the adoption and usage of future transportation technologies and mobility services.


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