ITS Berkeley

Identifiability of Car-following Dynamics

Wang, Yanbing
Monache, Maria Laura Delle
Work, Daniel B.
2022

The advancement of in-vehicle sensors provides abundant datasets to estimate parameters of car-following models that describe driver behaviors. The question of parameter identifiability of such models (i.e., whether it is possible to infer its unknown parameters from the experimental data) is a central system analysis question, and yet still remains open. This article presents both structural and practical parameter identifiability analysis on four common car-following models: i) the constant-time headway relative-velocity (CTH-RV) model, ii) the optimal velocity model (OV), iii) the...

Macroscopic Modelling and Control of Heavy-Duty Electric Road Systems

Čičić, Mladen
Monache, Maria Laura Delle
2024

Electric road systems (ERS), where power is delivered to the vehicles as they drive, are an intriguing option for road freight sector electrification. In order to analyse various aspects of their operation, such as their economical feasibility, or their influence on the power system, appropriate modelling approaches are needed. While microscopic, agent-based models have successfully been used for this purpose, their complexity makes them unsuitable for control design and implementation. In this work, we propose a macroscopic model, capturing the interaction between the ERS and Heavy-Duty...

Maria Laura Delle Monache [People in Control]

Delle Monache, Maria Laura
2025

Mesoscale Traffic Forecasting for Real-Time Bottleneck and Shockwave Prediction

Chekroun, Raphael
Wang, Han
Lee, Jonathan
Toromanoff, Marin
Hornauer, Sascha
Moutarde, Fabien
2024

Accurate real-time traffic state forecasting plays a pivotal role in traffic control research. In particular, the CIRCLES consortium project necessitates predictive techniques to mitigate the impact of data source delays. After the success of the MegaVanderTest experiment, this paper aims at overcoming the current system limitations and develop a more suited approach to improve the real-time traffic state estimation for the next iterations of the experiment. In this paper, we introduce the SA-LSTM, a deep forecasting method integrating Self-Attention (SA) on the spatial dimension with Long...

Multi-Directional Continuous Traffic Model for Large-scale Urban Networks

Tumash, Liudmila
Canudas-de-Wit, Carlos
2022

In this paper, we propose a new multi-directional two-dimensional continuous model for urban traffic. It is called the NEWS model, since it represents a system of four partial differential equations (PDEs) that describe propagation of vehicle density in cardinal direction layers: North, South, West and East. The NEWS model can be applied to predict traffic evolution on a general urban network of arbitrary size by knowing only its boundary flows, as well as its topology and infrastructure parameters such as roads speed limits, number of lanes and capacities. The flux direction is retrieved...

Multi-stage Models for Dynamic Ride-Sharing in Taxi Services and Congestion Analysis

Quadrifoglio, Luca
Zhang, Cheng
Sun, Min-Ci
Delle Monache, Maria Laura
Yeo, Yuneil
2024

This research introduces practical optimization model for implementing ride-sharing in taxi services and studies the effects of ride-sharing on the congestion status through the case study of Chicago. Ride-sharing combines trips into one ride-shared trip with the objective of maximizing the total mileage saving. This research proposes a multi-stage model to optimize rider matches, aiming to reduce the total travel distance and enhance the matching of multiple riders. To validate the effectiveness of the model, real taxi data from Chicago is used, demonstrating significant improvements in...

Near Collision and Controllability Analysis of Nonlinear Optimal Velocity Follow-the-Leader Dynamical Model In Traffic Flow

Matin, Hossein Nick Zinat
Delle Monache, Maria Laura
2023

This paper examines the optimal velocity follow-the-leader dynamics, a microscopic traffic model, and explores different aspects of the dynamical model, with particular emphasis on collision analysis. More precisely, we present a rigorous boundary-layer analysis of the model which provides a careful understanding of the behavior of the dynamics in trade-off with the singularity of the model at collision.

Intelligent Transport Systems

Deakin, Elizabeth
Frick, Karen Trapenberg
Skabardonis, Alexander
2009

If you've seen an electronic message sign alongthehighwaythattells you how long it will take to get downtown or to the airport, or paid your toll or your parking fees with an electronic tag, or ridden a bus that triggered the traffic lights to turn green as it approached them, then you have experienced some of the benefits of Intelligent Transportation Systems (ITS)—an...

New Frontiers of Freeway Traffic Control and Estimation

Delle Monache, M. L.
Pasquale, C.
Barreau, M.
Stern, R.
2022

This article provides an overview of the classical and new techniques in traffic flow control and estimations. The overview begins with a description of the most used traffic flow models for estimation and control. Then, it shifts towards using those models for traffic flow estimation using physics-informed machine learning techniques. Lastly, it focuses on traffic flow control describing the most classical techniques and the new advancement in traffic control using autonomous vehicles.

Nonlinear Advection-Diffusion Models of Traffic Flow: a Numerical Study

Matin, Hossein Nick Zinat
Do, Dawson
Monache, Maria Laura Delle
2023

The first-order degenerate parabolic traffic flow dynamics are the simplest extension of the Lighthill, Whitham, and Richards (LWR) model which aim at correcting the discrepancies between the LWR model and the observation collected from real data. In addition, the nonlinearity and degeneracy of these models are designed to address the fundamental criticism of linear diffusivelycorrected kinematic-wave models. While several first-order methods have been proposed in the literature, the predictive capabilities of these models has not been sufficiently studied with respect to real data. The...