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Randall Guensler

Researcher at Georgia Institute of Technology

Publications -  234
Citations -  5345

Randall Guensler is an academic researcher from Georgia Institute of Technology. The author has contributed to research in topics: Travel behavior & Global Positioning System. The author has an hindex of 34, co-authored 226 publications receiving 4818 citations. Previous affiliations of Randall Guensler include University of Cambridge & University of Louisville.

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Journal ArticleDOI

Electric vehicles: How much range is required for a day’s driving?

TL;DR: In this article, a full year of high-resolution driving data from 484 instrumented gasoline vehicles in the US is used to analyze daily driving patterns, and from those infer the range requirements of electric vehicles (EVs).
Proceedings ArticleDOI

MDDV: a mobility-centric data dissemination algorithm for vehicular networks

TL;DR: MDDV is designed to exploit vehicle mobility for data dissemination, and combines the idea of opportunistic forwarding, trajectory based forwarding and geographical forwarding, and develops a generic mobile computing approach for designing localized algorithms in vehicular networks.
Journal ArticleDOI

Elimination of the Travel Diary: Experiment to Derive Trip Purpose from Global Positioning System Travel Data

TL;DR: It is demonstrated that it is feasible to derive trip purpose from the GPS data by using a spatially accurate and comprehensive GIS, and this study used GPS data loggers to collect travel data in personal vehicles.
Journal ArticleDOI

Sustainability assessment at the transportation planning level: Performance measures and indexes

TL;DR: In this paper, the authors reviewed methodologies that can be applied in sustainability assessment in transportation planning in order to shed light on the procedures being used to incorporate sustainability more effectively in the planning process.
Journal ArticleDOI

Smoothing Methods to Minimize Impact of Global Positioning System Random Error on Travel Distance, Speed, and Acceleration Profile Estimates

TL;DR: Various data-smoothing techniques were applied to the instrumented vehicle GPS speed data, and performance of the algorithms was evaluated in minimizing the impact of GPS random error on speed, acceleration, and distance estimates.