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Ugur Demiryurek
Researcher at University of Southern California
Publications - 47
Citations - 2209
Ugur Demiryurek is an academic researcher from University of Southern California. The author has contributed to research in topics: Spatial network & Intelligent transportation system. The author has an hindex of 24, co-authored 47 publications receiving 1822 citations. Previous affiliations of Ugur Demiryurek include University of California, Los Angeles.
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Proceedings Article
Deep Learning: A Generic Approach for Extreme Condition Traffic Forecasting.
TL;DR: This work builds a deep neural network based on long short term memory (LSTM) units and applies Deep LSTM to forecast peak-hour traffic and manages to identify unique characteristics of the traffic data.
Proceedings ArticleDOI
Maximizing the number of worker's self-selected tasks in spatial crowdsourcing
TL;DR: This paper proves that the spatial crowd-sourcing problem in which the workers autonomously select their tasks is NP-hard, and proposes two exact algorithms based on dynamic programming and branch-and-bound strategies for small number of tasks.
Proceedings ArticleDOI
Utilizing Real-World Transportation Data for Accurate Traffic Prediction
TL;DR: This paper utilized the spatiotemporal behaviors of rush hours and events to perform a more accurate prediction of both short-term and long-term average speed on road-segments, even in the presence of infrequent events (e.g., accidents).
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Latent Space Model for Road Networks to Predict Time-Varying Traffic
TL;DR: This paper proposes a Latent Space Model for Road Networks (LSM-RN), a framework for real-time traffic prediction on large road networks over competitors as well as baseline graph-based LSM's and presents an incremental online algorithm which sequentially and adaptively learns the latent attributes from the temporal graph changes.
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Voronoi-Based Geospatial Query Processing with MapReduce
TL;DR: This paper creates a spatial index, Voronoi diagram, for given data points in 2D space and enables efficient processing of a wide range of GQs with the MapReduce programming model.