arXiv subjects

Shreyaa Raghavan

Publications and source records attributed to Shreyaa Raghavan.

5 recordsLinked to original sources

arXiv2026

Toward Controllability-Aware Performance Measures: A Case Study on Controllable Highway Congestion

Advancements in emerging intelligent transportation systems (ITS) have shown immense benefit in reducing congestion, emissions, and accidents and enable lower-cost alternatives to highway lane expansion. However, there currently exists no standard metric by which agencies can assess the improvement potential from these interventions. As a result, they may fail to deploy infrastructure where it will be most promising or risk investing in infrastructure that does not meaningfully enhance performance. Our objective is to guide ITS deployment by developing a metric that quantifies the upper bound of congestion improvement from controlling speed limits on a highway. We propose controllable congestion, a metric that quantifies the maximum achievable reduction in system delay. To estimate controllable congestion, we develop a nonlinear optimization framework grounded in a reformulation of the METANET macroscopic traffic model and solved using model predictive control (MPC). Using both a synthetic scenario and highway data from the I-24 SMART Corridor in Tennessee, we find that controllable congestion is largely independent of total delay, revealing that for two days with identical travel times, controllable congestion can vary from 13\% to 80\%. We further show that the realizable share of this upper bound depends on the operational constraints imposed. On I-24, the minimum posted speed limit has little effect on controllable congestion, while frequency of speed limit update and maximum difference between adjacent gantries have large impacts. This framework allows highway operators to distinguish between congestion that is structurally unavoidable and congestion that is highly responsive to ITS, enabling more cost-effective deployment of control-based traffic infrastructure.

eess.SY↗

arXiv2026

Open-Source METANET Calibration for Reproducible Freeway Traffic Macroscopic Simulation

METANET is a widely used second-order macroscopic traffic flow model for freeway networks, supporting applications across traffic simulation, ramp metering, and variable speed limit control. The predictive accuracy of any traffic model, however, hinges on careful calibration to real-world conditions. Despite its widespread use, there have not been open-source tools for calibrating METANET's parameters. Without open-source calibration, results cannot be easily reproduced or extended to other networks. This work provides an open-source METANET calibration, simulation, and data visualization tool. The calibration is formulated as a nonlinear program (NLP) solved via the interior-point method (IPOPT), with joint ramp flow estimation. We validate our calibration on real-world freeway data from two widely used traffic monitoring systems: Interstate-24 MObility Technology Interstate Observation Network (I-24 MOTION), one of the largest open-road trajectory instruments in the country, and loop detector data from the Caltrans Performance Measurement System (PeMS), which spans nearly 40,000 detectors across California freeways and serves as a standard benchmark in traffic research. Models calibrated using our method are able to reproduce these datasets' observed traffic patterns across diverse network geometries and traffic conditions including complex stop-and-go congestion waves. As large-scale traffic monitoring infrastructure continues to expand, open-source calibration tools are essential for translating growing volumes of sensor data into validated models that can support real-world traffic control. The complete code is publicly available at https://github.com/woxsao/metanet-calibration to support reproducible research in freeway traffic modeling and control.

eess.SY↗

arXiv2026

Dynamic Gradient-Based Calibration for Robust and Accurate Traffic Macrosimulation

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 desirable for high-dimensional parameter spaces, their application to real-world traffic scenarios is hindered by highly nonconvex optimization landscapes. Consequently, standard static calibration frequently yields parameter sets that produce unstable, unrealistic traffic dynamics, undermining confidence in the estimated parameters and compromising the simulation's utility for counterfactual scenario testing. To address this, we propose a dynamic, rolling-horizon calibration framework. By reformulating static one-time estimation into a dynamic control problem, parameters better maintain stability and accuracy amid measurement noise. Using real-world data from the I-24 MOTION testbed, this work empirically characterizes the instability of standard methods. It then shows that the proposed approach simultaneously enhances robustness to perturbations and achieves a 48% improvement in predictive accuracy over conventional static calibration.

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arXiv2025

A Roadmap for Climate-Relevant Robotics Research

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 climate-relevant robotics research, identifying high-impact opportunities for collaboration between roboticists and experts across climate domains such as energy, the built environment, transportation, industry, land use, and Earth sciences. These applications include problems such as energy systems optimization, construction, precision agriculture, building envelope retrofits, autonomous trucking, and large-scale environmental monitoring. Critically, we include opportunities to apply not only physical robots but also the broader robotics toolkit - including planning, perception, control, and estimation algorithms - to climate-relevant problems. A central goal of this roadmap is to inspire new research directions and collaboration by highlighting specific, actionable problems at the intersection of robotics and climate. This work represents a collaboration between robotics researchers and domain experts in various climate disciplines, and it serves as an invitation to the robotics community to bring their expertise to bear on urgent climate priorities.

cs.RO↗

arXiv2023

Data-Driven Traffic Reconstruction and Kernel Methods for Identifying Stop-and-Go Congestion

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 their substantial impact on carbon emissions, travel time, fuel consumption, and roadway safety. In fact, SAGs are estimated to account for 33-50% of highway driving externalities. However, insufficient attention has been paid to precisely quantifying where, when, and how much these SAGs take place -necessary for downstream decision making, such as intervention design and policy analysis. A key challenge is that the data available to researchers and governments are typically sparse and aggregated to a granularity that obscures SAGs. To overcome such data limitations, this study thus explores the use of traffic reconstruction techniques for SAG identification. In particular, we introduce a kernel-based method for identifying spatio-temporal features in traffic and leverage bootstrapping to quantify the uncertainty of the reconstruction process. Experimental results on California highway data demonstrate the promise of the method for capturing SAGs. This work contributes to a foundation for data-driven decision making to advance sustainability of traffic systems.

cs.LG↗