B
Benjamin Seibold
Researcher at Temple University
Publications - 93
Citations - 2181
Benjamin Seibold is an academic researcher from Temple University. The author has contributed to research in topics: Traffic wave & Traffic flow. The author has an hindex of 19, co-authored 87 publications receiving 1534 citations. Previous affiliations of Benjamin Seibold include Kaiserslautern University of Technology & Massachusetts Institute of Technology.
Papers
More filters
Journal ArticleDOI
Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments
Raphael Stern,Shumo Cui,Maria Laura Delle Monache,Rahul Bhadani,Matt Bunting,Miles Churchill,Nathaniel Hamilton,R’mani Haulcy,Hannah Pohlmann,Fangyu Wu,Benedetto Piccoli,Benjamin Seibold,Jonathan Sprinkle,Daniel B. Work,Daniel B. Work +14 more
TL;DR: It is demonstrated experimentally that intelligent control of an autonomous vehicle is able to dampen stop-and-go waves that can arise even in the absence of geometric or lane changing triggers, suggesting a paradigm shift in traffic management.
Proceedings ArticleDOI
Stabilizing traffic flow via a single autonomous vehicle: Possibilities and limitations
TL;DR: This work studies under which circumstances the presence of a single autonomous vehicle can locally stabilize the flow, without changing the way the humans drive, to enable traffic flow control via very few AVs serving as mobile actuators.
Journal ArticleDOI
Self-sustained nonlinear waves in traffic flow
TL;DR: In analogy to gas-dynamical detonation waves, which consist of a shock with an attached exothermic reaction zone, nonlinear traveling wave solutions to the hyperbolic ("inviscid") continuum traffic equations are considered.
Journal ArticleDOI
A gradient-augmented level set method with an optimally local, coherent advection scheme
TL;DR: This work presents an approach that augments the level set function values by gradient information, and evolves both quantities in a fully coupled fashion, which maintains the coherence between function values and derivatives, while exploiting the extra information carried by the derivatives.
Journal ArticleDOI
Comparative model accuracy of a data-fitted generalized Aw-Rascle-Zhang model
TL;DR: An approach to determine the parameter functions of the GARZ model from fundamental diagram measurement data is presented and the predictive accuracy of the resulting data-fitted GARz model is compared to other traffic models by means of a three-detector test setup.