13 citations · 17 across the 12 of their papers we have counts for
5 papers · 1 filter
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…
The Calibration Illusion in Traffic Microsimulation
Cameron Hickert, Maryam Samaei, Athena Wang +5
The transportation community seeks to use calibration methods for highway traffic microsimulation. This is a response to the time-consuming and subjective nature of traditional man…
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…
Trojans in Artificial Intelligence (TrojAI) Final Report
Kristopher W. Reese, Taylor Kulp-McDowall, Michael Majurski +68
The Intelligence Advanced Research Projects Activity (IARPA) launched the TrojAI program to confront an emerging vulnerability in modern artificial intelligence: the threat of AI T…
Probability-Aware Parking Selection
Cameron Hickert, Sirui Li, Zhengbing He +1
Current navigation systems conflate time-to-drive with the true time-to-arrive by ignoring parking search duration and the final walking leg. Such underestimation can significantly…