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Ram M. Pendyala

Researcher at Arizona State University

Publications -  269
Citations -  9647

Ram M. Pendyala is an academic researcher from Arizona State University. The author has contributed to research in topics: Travel behavior & Mode choice. The author has an hindex of 53, co-authored 251 publications receiving 8344 citations. Previous affiliations of Ram M. Pendyala include Sewanee: The University of the South & Georgia Institute of Technology.

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Modeling the choice continuum: An integrated model of residential location, auto ownership, bicycle ownership, and commute tour mode choice decisions

TL;DR: In this article, an integrated simultaneous multi-dimensional choice model of residential location, auto ownership, bicycle ownership, and commute tour mode choices using a mixed multidimensional choice modeling methodology is presented.
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A behavioral choice model of the use of car-sharing and ride-sourcing services

TL;DR: In this article, the authors present a bivariate ordered probit model estimated on a survey data set derived from the 2014-2015 Puget Sound Regional Travel Study to better understand the influence of various exogenous socioeconomic and demographic variables on the frequency of use of ride-sourcing and car-sharing services.
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Machine learning approaches for estimating commercial building energy consumption

TL;DR: In this article, a novel technique for estimating commercial building energy consumption from a small number of building features by training machine learning models on national data from the Commercial Buildings Energy Consumption Survey (CBECS) is presented.
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A Conceptual Analysis of the Impact of Travel Demand Management on Private Car Use

TL;DR: In this article, a conceptual framework is presented that may be utilized when analyzing changes in household travel arising from the range of potential measures available to policy makers, with its basis in goal setting and control theories.
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An Exploration of the Relationship Between Mode Choice and Complexity of Trip Chaining Patterns

TL;DR: Results show that the causal structure in which trip chain complexity precedes mode choice performs best for both work and non-work tour samples and has important implications for the development of activity-based and tour-based modeling systems and for the design and planning of public transport systems.