arXiv subjects

Antoine Tordeux

Publications and source records attributed to Antoine Tordeux.

At least 19 recordsLinked to original sources

arXiv2026

When legs and bodies synchronize: Two-level collective dynamics in dense crowds

Ultra-dense crowds, in which physical contact between people cannot be avoided, pose major safety concerns. Nevertheless, the underlying dynamics driving their collective behaviours remain poorly understood. Existing dense crowd models, mostly two-dimensional and contact-based, overlook biomechanical mechanisms that govern individual balance motion. In this study, we introduce a minimal two-level pedestrian model that couples upper body and legs dynamics, allowing us to capture transitions between balanced and unbalanced states at the individual scale. Whereas previous models fail to achieve it, this coupling gives rise to emergent collective behaviours observed empirically, such as self-organized waves and large-scale rotational motion within the crowd. The model bridges basic individual biomechanical concepts and macroscopic flow dynamics, offering a new framework for modelling and understanding collective motions in ultra-dense crowds.

physics.soc-ph↗

arXiv2025

Noise-induced stop-and-go traffic dynamics: Modelling and control

Stop-and-go waves in traffic flow are captivating collective phenomena with important safety and environmental consequences. While classical theories attribute these oscillations to linear instabilities caused by reaction delays and inertia, this study explores an alternative stochastic perspective. Using a linearly stable car-following model, we show that white Gaussian noise in the measurement of the inter-vehicle distance can destabilise the flow, inducing a phase transition to periodic stop-and-go dynamics via a nonlinear instability mechanism. Furthermore, we demonstrate that a simple linear transformation of the model, which amplifies the system response while introducing a positive acceleration bias, can counteract noise-induced effects and restores the stability of uniform traffic flow. These findings, supported by numerical simulations, aim to provide new insights into the modelling and control of oscillatory traffic dynamics.

physics.soc-ph↗

arXiv2025

Emergent Cooperative Driving Strategies for Stop-and-Go Wave Mitigation via Multi-Agent Reinforcement Learning

Stop-and-go waves in traffic flow pose a persistent challenge, compromising safety, efficiency, and environmental sustainability. This paper introduces a novel mitigation strategy discovered through training multi-agent deep reinforcement learning (DRL) agents in a simulated ring-road environment. The agents autonomously develop a cooperative driving policy, where most vehicles maintain minimal headways to maximize throughput, while a single "buffer" vehicle adopts a larger headway to absorb perturbations and prevent wave propagation. This strategy enhances stability without sacrificing overall flow. We further demonstrate that adapting this cooperative strategy to classical car-following models, such as the Intelligent Driver Model (IDM), yields improved stability and traffic efficiency. Furthermore, we show within a parametrised linear framework, that the cooperative strategy can optimise system performance under stability constraints. Our findings offer promising insights for future autonomous vehicle systems and highway management.

physics.soc-ph↗

arXiv2025

Exploring Dense Crowd Dynamics: State of the Art and Emerging Paradigms

Dense pedestrian crowds may pose significant safety risks, yet their underlying dynamics remain insufficiently understood to reliably prevent accidents. In these environments, physical interactions and contact forces fundamentally shape the dynamics of the crowd. However, accurately describing these interindividual interactions requires specific modeling and analytical approaches. This chapter reviews paradigms and models used to represent pedestrian dynamics in various contexts, highlighting the transition from classical approaches to models tailored for dense crowd conditions. We argue that further investigation is needed, featuring new experimental studies and new modeling paradigms, to better capture the complex dynamics that emerge in high-density situations.

physics.soc-ph↗

arXiv2025

Understanding Collective Stability of ACC Systems: From Theory to Real-World Observations

Autonomated vehicles (AVs) are expected to have a profound impact on society, with high expectations for their potential benefits. One key anticipated benefit is the reduction of traffic congestion and stop-and-go waves, which negatively affect fuel efficiency, travel time, and environmental sustainability. This paper presents a comprehensive review and meta-analysis of the impact of AVs on the longitudinal collective stability of vehicle single file. We focus on adaptive cruise control (ACC) systems, a widely used precursor technology to fully autonomous driving. ACC controllers have been studied extensively in both theoretical and practical contexts, making it a valuable starting point for analysis. Our study systematically differentiates the findings from models and simulations, controlled experiments, and empirical observations to provide a structured overview of existing research. Although some results in the literature are contradictory, three key insights emerge from our analysis: i) String stability is highly dependent on the chosen time gap and reaction/response time. ii) ACC systems currently implemented in commercial vehicles are not string stable, as manufacturers prioritise individual comfort and smooth driving, resulting in high system response times. iii) Although cooperative ACC (CACC) systems are theoretically the most effective solution to ensure string stability, their widespread implementation in the near future remains uncertain. Instead, improvements in autonomous control algorithms should be considered to enhance system performance.

physics.soc-ph↗

arXiv2025

Noise-induced transition to stop-and-go waves in single-file traffic rationalized by an analogy with Kapitza's inverted pendulum

Stop-and-go waves in vehicular traffic are commonly explained as a linear collective instability induced by e.g. response delays. We explore an alternative mechanism that more faithfully mirrors oscillation formation in dense single-file traffic. Stochastic noise plays a key role in this model; as it is increased, the base (uniform) flow abruptly switches to stop-and-go dynamics despite its unconditional linear stability. We elucidate the instability mechanism and rationalize it quantitatively by likening the system to a cyclically driven Kapitza pendulum.

physics.soc-ph↗

arXiv2025

Modelling car-following dynamics with stochastic input-state-output port-Hamiltonian systems

In this contribution, we introduce a general class of car-following models with an input-state-output port-Hamiltonian structure. We derive stability conditions and long-term behavior of the finite system with periodic boundaries and quadratic interaction potential by spectral analysis and using asymptotic properties of multivariate Ornstein-Uhlenbeck processes. The uncontrolled dynamics exhibit instability and random collective behavior under stochastic perturbations. By implementing an open-loop speed control, the system stabilizes and weakly converges to Gaussian limit distributions. The convergence is unconditional for constant speed control. However, a stability condition arises for the closed-loop system where the speed control acts as a dynamic feedback depending on the distance ahead. The results are illustrated by numerical simulations. Interestingly, only the closed-loop system is able to reproduce, at least transiently, realistic stop-and-go behavior that can be resolved using the Hamiltonian component of the model.

math.DS↗

arXiv2024

Comprehensive Review and New Analysis Software for Single-file Pedestrian Experiments

This paper offers a comprehensive examination of single-file experiments within the field of pedestrian dynamics, providing a review from both theoretical and analytical perspectives. It begins by tracing the historical context of single-file movement studies in pedestrian dynamics. The significance of understanding the fundamental relationships between density, speed, and flow in pedestrian dynamics is explored through the lens of simple single-file systems. Furthermore, we examine various traffic systems involving human or non-human entities such as ants, mice, bicycles, and cars, and provide insights. We explore the types of experimental setups, data collection methods, and factors that influence pedestrian movement. We also define and explain the common concepts related to single-file movement, particularly in experimental research. Finally, we present a Python tool named "SingleFileMovementAnalysis" designed for analyzing single-file experimental data, specifically head trajectories. This tool provides a unified approach for computing movement metrics like speed, density, and headway. The article aims to stimulate further research and underscore the areas where future researchers can contribute to the advancement and improvement of single-file studies.

physics.soc-ph↗

arXiv2024

System Reliability Engineering in the Age of Industry 4.0: Challenges and Innovations

In the era of Industry 4.0, system reliability engineering faces both challenges and opportunities. On the one hand, the complexity of cyber-physical systems, the integration of novel numerical technologies, and the handling of large amounts of data pose new difficulties for ensuring system reliability. On the other hand, innovations such as AI-driven prognostics, digital twins, and IoT-enabled systems enable the implementation of new methodologies that are transforming reliability engineering. Condition-based monitoring and predictive maintenance are examples of key advancements, leveraging real-time sensor data collection and AI to predict and prevent equipment failures. These approaches reduce failures and downtime, lower costs, and extend equipment lifespan and sustainability. However, it also brings challenges such as data management, integrating complexity, and the need for fast and accurate models and algorithms. Overall, the convergence of advanced technologies in Industry 4.0 requires a rethinking of reliability tasks, emphasising adaptability and real-time data processing. In this chapter, we propose to review recent innovations in the field, related methods and applications, as well as challenges and barriers that remain to be explored. In the red lane, we focus on smart manufacturing and automotive engineering applications with sensor-based monitoring and driver assistance systems.

cs.CY↗

arXiv2024

Modelling vehicle and pedestrian collective dynamics: Challenges and advances

In our urbanised societies, the management and regulation of traffic and pedestrian flows is of considerable interest for public safety, economic development, and the conservation of the environment. However, modelling and controlling the collective dynamics of vehicles and pedestrians raises several challenges. Not only are the individual entities self-propelled and hard to describe, but their complex nonlinear physical and social interactions makes the multi-agent problem of crowd and traffic flow even more involved. In this chapter, we purport to review the suitability and limitations of classical modelling approaches through four examples of collective behaviour: stop-and-go waves in traffic flow, lane formation, long-term avoidance behaviour, and load balancing in pedestrian dynamics. While stop-and-go dynamics and lane formation can both be addressed by basic reactive models (at least to some extent), the latter two require anticipation and/or coordination at the level of the group. The results highlight the limitations of classical force-based models, but also the need for long-term anticipation mechanisms and multiscale modelling approaches. In response, we review new developments and modelling concepts.

physics.soc-ph↗

arXiv2024

Dense Crowd Dynamics and Pedestrian Trajectories: A Multiscale Field Study at the Fête des Lumières in Lyon

We present one of the first comprehensive field datasets capturing dense pedestrian dynamics across multiple scales, ranging from macroscopic crowd flows over distances of several hundred meters to microscopic individual trajectories, including approximately 7,000 recorded trajectories. The dataset also includes a sample of GPS traces, statistics on contact and push interactions, as well as a catalog of non-standard crowd phenomena observed in video recordings. Data were collected during the 2022 Festival of Lights in Lyon, France, within the framework of the French-German MADRAS project, covering pedestrian densities up to 4 individuals per square meter.

physics.soc-ph↗

arXiv2024

Stability of heterogeneous linear and nonlinear car-following models

Stop-and-go waves in road traffic are complex collective phenomena with significant implications for traffic engineering, safety and the environment. Despite decades of research, understanding and controlling these dynamics remains challenging. This article examines two classes of heterogeneous car-following models with quenched disorder to shed light on the underlying mechanisms that drive traffic instability and stop-and-go dynamics. Specifically, a scaled heterogeneity model and an additive heterogeneity model are investigated, each of which affects the stability of linear and nonlinear car-following models differently. We derive general linear stability conditions which we apply to specific models and illustrate by simulation. The study provides insights into the role of individual heterogeneity in vehicle behaviour and its influence on traffic stability.

physics.soc-ph↗

arXiv2024

Stability analysis of a stochastic port-Hamiltonian car-following model

Port-Hamiltonian systems are pertinent representations of many nonlinear physical systems. In this study, we formulate and analyse a general class of stochastic car-following models with a systematic port-Hamiltonian structure. The model class is a generalisation of classical car-following approaches, including the optimal velocity model of Bando et al. (1995), the full velocity difference model of Jiang et al. (2001), and recent stochastic following models based on the Ornstein-Uhlenbeck process. In contrast to traditional models where the interaction is totally asymmetric (i.e., depending only on the speed and distance to the predecessor), the port-Hamiltonian car-following model also depends on the distance to the follower. We determine the exact stability condition of the finite system with $N$ vehicles and periodic boundaries. The stable system is ergodic with a unique Gaussian invariant measure. Other properties of the model are studied using numerical simulation. It turns out that the Hamiltonian component improves the flow stability and reduces the total energy in the system. Furthermore, it prevents the problematic formation of stop-and-go waves with oscillatory dynamics, even in the presence of stochastic perturbations.

math.DS↗

arXiv2024

Reliability of Redundant M-Out-Of-N Architectures With Dependent Components: A Comprehensible Approach With Monte Carlo Simulation

Redundant architectures can improve the reliability of complex systems. However, component dependencies can affect the architecture and negate the benefit of redundancy. In this paper, we develop three component dependency models and analyze the reliability of different M-out-of-N configurations using Monte Carlo simulation. The first model assumes a linear component dependency. The second and third models consider common cause failures, in the latter for all components and in the second for random groups of components. As expected, the results show that interdependency degrades the reliability of parallel 1ooN systems while improving it for serial NooN systems. Interestingly, 2oo3 systems produce intermediate results that show an improvement in reliability for certain indicators and a deterioration for some others, depending on the type of dependency models. The results show nonlinear properties of MooN systems with dependent components, which suggest careful handling in applications. An online simulation platform based on Monte Carlo Simulation enables product designers to use the models efficiently and achieve tailored results

stat.AP↗

arXiv2024

Stabilisation of stochastic single-file dynamics using port-Hamiltonian systems

This study revisits a recently proposed symmetric port-Hamiltonian single-file model in one dimension. The uniform streaming solutions are stable in the deterministic model. However, the introduction of white noise into the dynamics causes the model to exhibit divergence. In response, we introduce a control term in a port-Hamiltonian framework. Our results show that this control term effectively stabilises the dynamics even in the presence of noise, providing valuable insights for the control of road traffic flows.

math.DS↗

arXiv2023

Time-continuous microscopic pedestrian models: an overview

We give an overview of time-continuous pedestrian models with a focus on data-driven modelling. Starting from pioneer, reactive force-based models we move forward to modern, active pedestrian models with sophisticated collision-avoidance and anticipation techniques through optimisation problems. The overview focuses on the mathematical aspects of the models and their different components. We include methods used for data-based calibration of model parameters, hybrid approaches incorporating neural networks, and purely data-based models fitted by deep learning. The conclusion outlines some development perspectives that we expect to grow in the coming years.

physics.soc-ph↗

arXiv2023

Single-file pedestrian dynamics: a review of agent-following models

Single-file dynamics has been studied intensively, both experimentally and theoretically. It shows interesting collective effects, such as stop-and-go waves, which are validation cornerstones for any agent-based modeling approach of traffic systems. Many models have been proposed, e.g. in the form of car-following models for vehicular traffic. These approaches can be adapted for pedestrian streams. In this study, we delve deeper into these models, with particular attention on their interconnections. We do this by scrutinizing the influence of different parameters, including relaxation times, anticipation time, and reaction time. Specifically, we analyze the inherent fundamental problems with force-based models, a classical approach in pedestrian dynamics. Furthermore, we categorize car-following models into stimulus-response and optimal velocity models, highlighting their historical and conceptual differences. These classes can further be subdivided considering the conceptual definitions of the models, e.g. first-order vs. second-order models, or stochastic vs. deterministic models with and without noise. Our analysis shows how car-following models originally developed for vehicular traffic can provide new insights into pedestrian behavior. The focus on single-file motion, which is similar to single-lane vehicular traffic, allows for a detailed examination of the relevant interactions between pedestrians.

physics.soc-ph↗

arXiv2023

Social distancing and the future of pedestrian dynamics

Many countries have introduced social distancing rules during the COVID-19 pandemic in order to reduce spreading of the virus in public spaces. This has inspired research on pedestrian dynamics in more ways than just one. Many studies have been performed, not only to determine the effectiveness of such measures, but also in order to understand its effects on crowd dynamics in general. In this Perspective article, we reflect on the insights derived from these investigations and their relevance for future advancements in the field of pedestrian dynamics. This includes impacts on safety regulations and the potential for new theoretical and experimental approaches which may hold relevance even beyond applied restrictions.

physics.soc-ph↗