Daniel Work

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Associate Professor

Vanderbilt University

United States

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About

Daniel Work is an Associate Professor at Vanderbilt University in the United States. His research focuses on traffic control systems, multi-agent reinforcement learning, and the application of adaptive cruise control to improve traffic flow. Recent articles authored by Professor Work include studies on super-resolution reconstruction of stop-and-go waves and the deployment of variable-speed-limit control systems in real-world scenarios.

Recent Grants

Grant: Close

CPS: Synergy: Collaborative Research: Control of Vehicular Traffic Flow via Low Density Autonomous Vehicles

Open Date: 2015-01-01

Close Date: 2017-12-31

Grant: Close

CAREER: Modeling and Estimation Methods for Complex Traffic

Open Date: 2014-08-01

Close Date: 2019-07-31

Grant: Close

RAPID: Monitoring the Response of Transportation Cyber Physical Systems in the Wake of Hurricane Sandy

Open Date: 2013-01-15

Close Date: 2013-12-31

Grant: Close

Risk-informed Management and Post-disaster Operations of Lifeline Networks by Rapid, Condition-based System Reliability Analysis

Open Date: 2010-08-15

Close Date: 2015-01-31

Articles (16)

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2025

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2022

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2021

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2021

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UNITED STATES

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Daniel Work

Research Interests

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About

Recent Grants

Articles (16)

Stop-and-go wave super-resolution reconstruction via iterative refinement

On the Constrained CAV Platoon Control Problem

Real-World Deployment and Assessment of a Multiagent Reinforcement Learning-Based Variable-Speed-Limit Control System

From Sim to Real: A Pipeline for Training and Deploying Traffic Smoothing Cruise Controllers

Modifying Adaptive Cruise Control Systems for String Stable Stop-and -Go Wave Control

CAV Traffic Control to Mitigate the Impact of Congestion from Bottlenecks: A Linear Quadratic Regulator Approach and Microsimulation Study

MARVEL: Bringing Multi-Agent Reinforcement- Learning Based Variable Speed Limit Controllers Closer to Deployment

I-24 MOTION: An instrument for freeway traffic science

Gaussian Process-Based Personalized Adaptive Cruise Control

Detecting Extreme Traffic Events Via a Context Augmented Graph Autoencoder

Streaming Data Preprocessing via Online Tensor Recovery for Large Environmental Sensor Networks

Estimation for heterogeneous traffic using enhanced particle filters

Online Parameter Estimation Methods for Adaptive Cruise Control Systems

Optimization methods for analysis of empirical rail dispatching relative to train plans

Enhanced data reconciliation of freight rail dispatch data

Model-Based String Stability of Adaptive Cruise Control Systems Using Field Data

Collaborators (5)

Ziran Wang

Maria Laura Delle Monache

Benedetto Piccoli

Junyi Ji

Jonathan Sprinkle

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