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Daniel B. Work
Researcher at Vanderbilt University
Publications - 136
Citations - 5461
Daniel B. Work is an academic researcher from Vanderbilt University. The author has contributed to research in topics: Traffic flow & Computer science. The author has an hindex of 27, co-authored 115 publications receiving 4384 citations. Previous affiliations of Daniel B. Work include Urbana University & University of California, Berkeley.
Papers
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Journal ArticleDOI
Application of robust optimization in matrix-based LCI for decision making under uncertainty
Ren Wang,Daniel B. Work +1 more
TL;DR: A mathematical method for life cycle assessment (LCA) optimization that protects decisions against uncertainty at the life cycle inventory (LCI) stage and provides a mechanism to control the level of protection against uncertainty.
Journal ArticleDOI
Tracking the Evolution of Infrastructure Systems and Mass Responses Using Publically Available Data.
TL;DR: This work develops a methodology of tracking the evolutionary dynamics of the two networks by incorporating flows and the microstructure of networks such as motifs, and shows that significant changes in the system-level structure of networks can be detected on a continuous basis.
Proceedings ArticleDOI
Interactive multiple model ensemble Kalman filter for traffic estimation and incident detection
Ren Wang,Daniel B. Work +1 more
TL;DR: An interactive multiple model ensemble Kalman filter is proposed to solve the sequential estimation problem, and to accommodate the switching dynamics and nonlinearity of the traffic incident model.
Measuring trajectories and fuel consumption in oscillatory traffic: experimental results
Fangyu Wu,Raphael Stern,Miles Churchill,Maria Laura Delle Monache,Ke Han,Benedetto Piccoli,Daniel B. Work +6 more
TL;DR: This article presents data collected through a set of experiments with nine to 10 vehicles driving on a ring road constructed on a closed track to collect detailed trajectory data and fuel consumption data with smooth and unsteady traffic flow in a controlled experimental environment.
Proceedings ArticleDOI
Personalized Adaptive Cruise Control via Gaussian Process Regression
TL;DR: In this paper, an algorithm for learning personalized longitudinal driving behaviors via a Gaussian Process (GP) model was developed to learn from the individual driver's naturalistic car-following behaviors, and outputs a desired acceleration that suits the user's preference.