R
Ryan Herring
Researcher at University of California, Berkeley
Publications - 18
Citations - 3316
Ryan Herring is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Global Positioning System & Traffic flow. The author has an hindex of 15, co-authored 18 publications receiving 3122 citations. Previous affiliations of Ryan Herring include Apple Inc. & Rensselaer Polytechnic Institute.
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Evaluation of Traffic Data Obtained via GPS-Enabled Mobile Phones: the Mobile Century Field Experiment
TL;DR: In this paper, a traffic monitoring system based on GPS-enabled smartphones exploits the extensive coverage provided by the cellular network, the high accuracy in position and velocity measurements provided by GPS devices, and the existing infrastructure of the communication network.
Journal ArticleDOI
Evaluation of traffic data obtained via GPS-enabled mobile phones: The Mobile Century field experiment
TL;DR: Results suggest that a 2-3% penetration of cell phones in the driver population is enough to provide accurate measurements of the velocity of the traffic flow, demonstrating the feasibility of the proposed system for real-time traffic monitoring.
Proceedings ArticleDOI
Virtual trip lines for distributed privacy-preserving traffic monitoring
Baik Hoh,Marco Gruteser,Ryan Herring,Jeff Ban,Daniel B. Work,Juan-Carlos Herrera,Alexandre M. Bayen,Murali Annavaram,Quinn Jacobson +8 more
TL;DR: This work proposes a system based on virtual trip lines and an associated cloaking technique that facilitates the design of a distributed architecture, where no single entity has a complete knowledge of probe identities and fine-grained location information.
Journal ArticleDOI
Learning the Dynamics of Arterial Traffic From Probe Data Using a Dynamic Bayesian Network
TL;DR: A model based on hydrodynamic traffic theory is introduced to learn the density of vehicles on arterial road segments, illustrating the distribution of delay within a road segment.
Proceedings ArticleDOI
Estimating arterial traffic conditions using sparse probe data
TL;DR: This article proposes a probabilistic modeling framework for estimating and predicting arterial travel time distributions using sparsely observed probe vehicles and provides an increase in estimation accuracy of 35% when compared to a baseline approach for processing probe vehicle data.