Maria Laura Delle Monache

About

Maria Laura Delle Monache is an Assistant Professor in the Department of Civil and Environmental Engineering at UC Berkeley. Her research lies at the intersection of transportation engineering, mathematics, and control theory, with a focus on understanding the implications of technology on transportation systems. She is particularly interested in building mathematical models and control strategies to assess how new vehicular technologies impact traffic congestion, traffic emissions, and access to transport. Prior to her current position, she was a research scientist at Inria in Grenoble, France (2016-2021) and a Postdoctoral fellow at Rutgers University - Camden in the USA (2014-2016). She received her Ph.D. in applied mathematics from the University of Nice-Sophia Antipolis, France, in 2014, along with a M.Sc. in Mathematical Engineering from the University of L'Aquila (Italy) and the University of Hamburg (Germany), and a B.Sc. in Industrial Engineering from the University of L'Aquila. Dr. Delle Monache’s work involves building mathematical models and control strategies to evaluate the impact of vehicular technology on traffic flow, safety, and energy consumption. She has received multiple awards, including the 2024 IEEE Intelligent Transportation Systems Society (ITSS) Institutional Lead Award, the 2024 ITS Faculty of the Year, the 2023 IEEE Technical Committee on Cyber-Physical Systems (TCCPS) mid-career award, and the 2023 IEEE ITS Society Young Researcher/Engineer…

Research topics

• Artificial Intelligence
• Computer Science
• Machine Learning
• Simulation
• Mathematics

Selected publications

• Traffic Control via Connected and Automated Vehicles (CAVs): An Open-Road Field Experiment with 100 CAVs

  IEEE Control Systems · 2025-01-30 · 22 citations

  articleOpen access

  The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. Also called “phantom jams” or “stop-and-go waves,” these instabilities are a significant source of wasted energy. Toward this goal, the CIRCLES project designed a control system, referred to as the MegaController by the CIRCLES team, that could be deployed in real traffic. Our field experiment, the MegaVanderTest (MVT), leveraged a heterogeneous fleet of 100 lo…

  PublisherOA PDFDOI

• Design, Preparation, and Execution of the 100-AV Field Test for the CIRCLES Consortium: Methodology and Implementation of the Largest Mobile Traffic Control Experiment to Date

  IEEE Control Systems · 2025-01-30 · 9 citations

  articleOpen access

  This article presents the comprehensive design, setup, execution, and evaluation of the MegaVanderTest (MVT) experiment conducted by the Congestion Impacts Reduction via CAV-in-the-Loop Lagrangian Energy Smoothing (CIRCLES) Consortium, which aimed to mitigate traffic congestion using partially autonomous vehicles (AVs) (see “Summary”). The experiment involved 100 vehicles on Nashville’s Interstate 24 (I-24) highway, utilizing various control algorithms to smooth stop-and-go traffic waves. The ex…

  PublisherOA PDFDOI

• Hierarchical Speed Planner for Automated Vehicles: A Framework for Lagrangian Variable Speed Limit in Mixed-Autonomy Traffic

  IEEE Control Systems · 2025-01-30 · 8 citations

  articleOpen accessSenior author

  This article presents a novel hierarchical speed planning framework for variable speed limits in mixed-autonomy traffic environments, leveraging server-side macroscopic control and vehicle-side microscopic execution. The framework integrates real-time traffic state estimation (TSE) and reinforcement learning (RL)-based control to mitigate congestion and improve traffic flow. A TSE enhancement module combines macroscopic data from sources like INRIX with high-resolution observations from connecte…

  PublisherOA PDFDOI

• Traffic Control via Fleets of Connected and Automated Vehicles

  IEEE Transactions on Intelligent Transportation Systems · 2024-12-09 · 7 citations

  articleOpen access

  In this paper, we propose three control strategies, based on different levels of cooperation (centralized, decentralized and quasi-decentralized), to improve density dependent traffic performance indexes, such as fuel consumption, by acting on a small number of Connected and Automated Vehicles (CAVs) operating as moving bottlenecks on the surrounding flow. We rely on a multi-scale approach to model mixed traffic of CAVs in the bulk flow. In particular, CAVs are individually tracked and they are…

  PublisherOA PDFDOI

• Traffic Smoothing Using Explicit Local Controllers: Experimental Evidence for Dissipating Stop-and-go Waves with a Single Automated Vehicle in Dense Traffic

  IEEE Control Systems · 2025-01-30 · 6 citations

  articleOpen access

  This article presents experimental evidence of the ability of a single automated vehicle acting as a controller to effectively dissipate stop-and-go waves in real traffic. The automated vehicle succeeded in stabilizing the speed profile by reducing oscillations in time and speed variations between vehicles during rush hour on I-24 in the Nashville area. We detail the control design, deployment and results obtained in this experiment, conducted as part of the CIRCLES consortium’s “MegaVanderTest”…

  PublisherOA PDFDOI

Frequent coauthors

• Paola Goatin

  Observatoire de la Côte d’Azur

  104 shared

• Benedetto Piccoli

  Rutgers, The State University of New Jersey

  93 shared

• Daniel B. Work

  84 shared

• Carlos Canudas de Wit

  Centre Inria de l'Université Grenoble Alpes

  75 shared

• Benjamin Seibold

  57 shared

• Paolo Frasca

  56 shared

• Raphael Stern

  University of Minnesota

  55 shared

• Jonathan Sprinkle

  Vanderbilt University

  50 shared

Awards & honors

• 2024 IEEE Intelligent Transportation Systems Society (ITSS)…
• 2024 ITS Faculty of the year
• 2023 IEEE Technical committee on cyber-physical systems (TCC…
• 2023 IEEE Intelligent Transportation Systems Society (ITSS)…

Similar researchers at University of California, Berkeley

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