arXiv subjects

Margherita Grossi

Publications and source records attributed to Margherita Grossi.

7 recordsLinked to original sources

arXiv2026

Antifragile perimeter control: Thriving on disruptions through reinforcement learning

The optimal operation of transportation systems is often susceptible to unexpected disruptions. Many established control strategies reliant on mathematical models can struggle with real-world disruptions, leading to significant divergence from their anticipated efficiency. This study integrates the cutting-edge concept of antifragility with learning-based traffic control strategies to optimize urban road network operations under disruptions. Antifragile systems not only withstand and recover from stressors but also thrive and enhance performance in the presence of such adversarial events. Incorporating antifragile modules composed of traffic state derivatives and redundancy, a deep reinforcement learning algorithm is developed. Subsequently, it is evaluated in a cordon-shaped transportation network and a case study with real-world data. Promising results highlight that the proposed algorithm provides: (i) superior performance achieving up to 27.6% and 41.9% performance gain over baselines under increasing demand and supply disruptions, (ii) lower distribution skewness under disruptions, demonstrating its relative antifragility against baselines, (iii) effectiveness under limited observability due to real-world data availability constraints, and (iv) the robustness and transferability to be combined with various state-of-the-art RL frameworks. The proposed antifragile methodology is generalizable and holds potential for applications beyond traffic engineering, offering integration into control systems exposed to disruptions across various disciplines.

eess.SY↗

arXiv2026

The fragile nature of road transportation networks

Major cities worldwide experience problems with the performance of their road transportation networks, and the continuous increase in traffic demand presents a substantial challenge to the optimal operation of urban road networks and the efficiency of traffic control strategies. The operation of transportation systems is widely considered to display fragile property, i.e., the loss in performance increases exponentially with the linearly growing magnitude of disruptions. Meanwhile, the risk engineering community is embracing the novel concept of antifragility, enabling systems to learn from past events and exhibit improved performance under disruptions of previously unseen magnitudes. In this study, based on established traffic flow theory knowledge, namely the Macroscopic Fundamental Diagram (MFD), we first conduct a rigorous mathematical analysis to theoretically prove the fragile nature of road transportation networks. Subsequently, we propose a skewness-based indicator that can be readily applied to cross-compare the degree of fragility for different networks solely dependent on the MFD-related parameters. Finally, we implement a numerical simulation calibrated with real-world network data to bridge the gap between the theoretical proof and the practical operations, with results showing the reinforcing effect of higher-order statistics and stochasticity on the fragility of the networks. This work aims to demonstrate the fragile nature of road transportation networks and guide researchers towards adopting the methods of antifragile design for future networks and traffic control strategies.

eess.SY↗

arXiv2025

Variable Selection in Maximum Mean Discrepancy for Interpretable Distribution Comparison

We study two-sample variable selection: identifying variables that discriminate between the distributions of two sets of data vectors. Such variables help scientists understand the mechanisms behind dataset discrepancies. Although domain-specific methods exist (e.g., in medical imaging, genetics, and computational social science), a general framework remains underdeveloped. We make two separate contributions. (i) We introduce a mathematical notion of the discriminating set of variables: the largest subset containing no variables whose marginals are identical across the two distributions and independent of the remaining variables. We prove this set is uniquely defined and establish further properties, making it a suitable ground truth for theory and evaluation. (ii) We propose two methods for two-sample variable selection that assign weights to variables and optimise them to maximise the power of a kernel two-sample test while enforcing sparsity to downweight redundant variables. To select the regularisation parameter - unknown in practice, as it controls the number of selected variables - we develop two data-driven procedures to balance recall and precision. Synthetic experiments show improved performance over baselines, and we illustrate the approach on two applications using datasets from water-pipe and traffic networks.

stat.ML↗

arXiv2024

Variable Selection for Comparing High-dimensional Time-Series Data

Given a pair of multivariate time-series data of the same length and dimensions, an approach is proposed to select variables and time intervals where the two series are significantly different. In applications where one time series is an output from a computationally expensive simulator, the approach may be used for validating the simulator against real data, for comparing the outputs of two simulators, and for validating a machine learning-based emulator against the simulator. With the proposed approach, the entire time interval is split into multiple subintervals, and on each subinterval, the two sample sets are compared to select variables that distinguish their distributions and a two-sample test is performed. The validity and limitations of the proposed approach are investigated in synthetic data experiments. Its usefulness is demonstrated in an application with a particle-based fluid simulator, where a deep neural network model is compared against the simulator, and in an application with a microscopic traffic simulator, where the effects of changing the simulator's parameters on traffic flows are analysed.

stat.ME↗

arXiv2023

Antifragile Control Systems: The case of an oscillator-based network model of urban road traffic dynamics

Existing traffic control systems only possess a local perspective over the multiple scales of traffic evolution, namely the intersection level, the corridor level, and the region level respectively. But luckily, despite its complex mechanics, traffic is described by various periodic phenomena. Workday flow distributions in the morning and evening commuting times can be exploited to make traffic adaptive and robust to disruptions. Additionally, controlling traffic is also based on a periodic process, choosing the phase of green time to allocate to opposite directions right of the pass and complementary red time phase for adjacent directions. In our work, we consider a novel system for road traffic control based on a network of interacting oscillators. Such a model has the advantage to capture temporal and spatial interactions of traffic light phasing as well as the network-level evolution of the traffic macroscopic features (i.e. flow, density). In this study, we propose a new realization of the antifragile control framework to control a network of interacting oscillator-based traffic light models to achieve region-level flow optimization. We demonstrate that antifragile control can capture the volatility of the urban road environment and the uncertainty about the distribution of the disruptions that can occur. We complement our control-theoretic design and analysis with experiments on a real-world setup comparatively discussing the benefits of an antifragile design for traffic control.

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arXiv2008

The impact of Early Dark Energy on non-linear structure formation

We study non-linear structure formation in high-resolution simulations of Early Dark Energy (EDE) cosmologies and compare their evolution with the standard LCDM model. Extensions of the spherical top-hat collapse model predict that the virial overdensity and linear threshold density for collapse should be modified in EDE model, yielding significant modifications in the expected halo mass function. Here we present numerical simulations that directly test these expectations. Interestingly, we find that the Sheth & Tormen formalism for estimating the abundance of dark matter halos continues to work very well in its standard form for the Early Dark Energy cosmologies, contrary to analytic predictions. The residuals are even slightly smaller than for LCDM. We also study the virial relationship between mass and dark matter velocity dispersion in different dark energy cosmologies, finding excellent agreement with the normalization for Lambda as calibrated by Evrard et al.(2008). The earlier growth of structure in EDE models relative to LCDM produces large differences in the mass functions at high redshift. This could be measured directly by counting groups as a function of the line-of-sight velocity dispersion, skirting the ambiguous problem of assigning a mass to the halo. Using dark matter substructures as a proxy for member galaxies, we demonstrate that even with 3-5 members sufficiently accurate measurements of the halo velocity dispersion function are possible. Finally, we determine the concentration-mass relationship for our EDE cosmologies. Consistent with the earlier formation time, the EDE halos show higher concentrations at a given halo mass. We find that the magnitude of the difference in concentration is well described by the prescription of Eke et al.(2001) for estimating halo concentrations.

astro-ph↗

arXiv2008

The Effect of Primordial Non--Gaussianity on the Topology of Large-Scale Structure

We study the effect of primordial non-Gaussianity on the development of large-scale cosmic structure using high-resolution N-body simulations. In particular, we focus on the topological properties of the "cosmic web", quantitatively characterized by the Minkowski Functionals, for models with quadratic non-linearities with different values of the usual non-Gaussianity parameter fNL. In the weakly non-linear regime, we find that analytic formulae derived from perturbation theory agree with the numerical results within a few percent of the amplitude of each MF when |fNL|<1000. In the non-linear regime, the detailed behavior of the MFs as functions of threshold density deviates more strongly from the analytical curves, while the overall amplitude of the primordial non-Gaussian effect remains comparable to the perturbative prediction. When smaller-scale information is included, the influence of primordial non-Gaussianity becomes increasingly significant statistically due to decreasing sample variance. We find that the effect of the primordial non-Gaussianity with |fNL|=50 is comparable to the sample variance of mass density fields with a volume of 0.125(Gpc/h)^3 when they are smoothed by Gaussian filter at a scale of 5Mpc/h. The detectability of this effect in actual galaxy surveys will strongly depend upon residual uncertainties in cosmological parameters and galaxy biasing.

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