2 citations · 2 across the 5 of their papers we have counts for
6 papers
On the Input Sensitivity of METANET Models and the Robustness of Dynamic Calibration
Cameron Hickert, Shreyaa Raghavan, Cathy Wu
While traffic modeling and control rely upon effective calibration of macroscopic traffic simulation models like METANET, recent empirical work shows these models can exhibit sever…
Toward Controllability-Aware Performance Measures: A Case Study on Controllable Highway Congestion
Shreyaa Raghavan, Edgar Ramirez-Sanchez, Zhengbing He +1
Advancements in emerging intelligent transportation systems (ITS) have shown immense benefit in reducing congestion, emissions, and accidents and enable lower-cost alternatives to…
Dynamic Gradient-Based Calibration for Robust and Accurate Traffic Macrosimulation
Shreyaa Raghavan, Cameron Hickert, Monica Chan +1
Robust and accurate calibration of macroscopic traffic flow models such as METANET is critical for reliable prediction and effective control. While gradient-based methods are desir…
Open-Source METANET Calibration for Reproducible Freeway Traffic Macroscopic Simulation
Monica Chan, Shreyaa Raghavan, Cathy Wu
METANET is a widely used second-order macroscopic traffic flow model for freeway networks, supporting applications across traffic simulation, ramp metering, and variable speed limi…
A Roadmap for Climate-Relevant Robotics Research
Alan Papalia, Charles Dawson, Laurentiu L. Anton +25
Climate change is one of the defining challenges of the 21st century, and many in the robotics community are looking for ways to contribute. This paper presents a roadmap for clima…
Data-Driven Traffic Reconstruction and Kernel Methods for Identifying Stop-and-Go Congestion
Edgar Ramirez Sanchez, Shreyaa Raghavan, Cathy Wu
Identifying stop-and-go events (SAGs) in traffic flow presents an important avenue for advancing data-driven research for climate change mitigation and sustainability, owing to the…