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Brian L. Smith
Researcher at University of Virginia
Publications - 155
Citations - 4807
Brian L. Smith is an academic researcher from University of Virginia. The author has contributed to research in topics: Traffic flow & Intelligent transportation system. The author has an hindex of 31, co-authored 153 publications receiving 4244 citations. Previous affiliations of Brian L. Smith include Old Dominion University & Texas Southern University.
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Comparison of parametric and nonparametric models for traffic flow forecasting
TL;DR: This research effort seeks to examine the theoretical foundation of nonparametric regression and to answer the question of whether non parametric regression based on heuristically improved forecast generation methods approach the single interval traffic flow prediction performance of seasonal ARIMA models.
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Traffic flow forecasting: comparison of modeling approaches
TL;DR: This research effort focused on developing traffic volume forecasting models for two sites on Northern Virginia's Capital Beltway, and found that the nonparametric regression model was easy to implement, and proved to be portable, performing well at two distinct sties.
Journal Article
Short-term traffic flow prediction: neural network approach
TL;DR: In a comparison of a backpropagation neural network model with the more traditional approaches of an historical, data-based algorithm and a time-series model, the back Propagation model was clearly superior, although all three models did an adequate job of predicting future traffic volumes.
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Traffic Signal Control with Connected Vehicles
TL;DR: A decentralized, fully adaptive traffic control algorithm, the predictive microscopic simulation algorithm, which uses a rolling-horizon strategy in which the phasing is chosen to optimize an objective function over a 15-s period in the future is developed.
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Exploring Imputation Techniques for Missing Data in Transportation Management Systems
TL;DR: The feasibility and applicability of imputing missing traffic data are addressed, and a preliminary analysis of several heuristic and statistical imputation techniques is performed.