Searcharxiv⌕ Search

arXiv subjects

Marco Cogoni

Publications and source records attributed to Marco Cogoni.

8 recordsLinked to original sources
arXiv2024

Shape and Performance of Fastest Paths over Networks with Interacting Selfish Agents

We study the evolution of the fastest paths (FP) in transportation networks under increasing congestion. Moving from the common edge-based to a path-based analysis, we examine the directed FPs connecting random origin-destination pairs as traffic grows. We describe their shape through effective length, detour (maximum distance of FP from a straight line), inness (signed area between FP and straight line), and their performance through a novel metric measuring how fast and how far an agent travels toward its destination. The entire network is characterized by analyzing the distribution of the performance metric (and its Gini coefficient) across uniformly sampled paths. The study focuses on the traffic loading phenomenon that takes place during the morning peak hour for eight major cities: Networks start with empty edges that are progressively populated by the FPs of single vehicles. As vehicle density grows, the interactions among selfish agents becomes stronger at edge level, and travel speed linearly decreases, thus optimal paths dynamically change with traffic. We fully characterize the transition to congestion and discuss the common aspects among the cities (and some peculiarities), in particular we were able to pinpoint a critical traffic level (or a sequence) for which path shape, rejection ratio, and inequality of the performance degradation, show a concurrent qualitative change. For all cities we observe large peaks for both detour and inness (and their variance) in the proximity of the critical traffic level. Inness shows that paths are slightly attracted by city centers with light traffic, but switch to a strong repulsion immediately beyond the transition. Finally, our path performance metric highlighted a strongly asymmetric behavior when the city neighborhoods act as origins or destinations.

physics.soc-ph↗
arXiv2023

Predicting Network Congestion by Extending Betweenness Centrality to Interacting Agents

We present a simple model to predict network activity at the edge level, by extending a known approximation method to compute Betweenness Centrality (BC) with a repulsive mechanism to prevent unphysical densities. By taking into account the strong interaction effects often observed in real phenomena, we aim to obtain an improved measure of edge usage during rush hours as traffic congestion patterns emerge in urban networks. In this approach, the network is iteratively populated by agents following dynamically evolving fastest paths, that are progressively attracted towards uncongested parts of the network, as the global traffic volume increases. Following the transition of the network state from empty to saturated, we study the emergence of congestion and the progressive disruption of global connectivity due to a relatively small fraction of crowded edges. We assess the predictive power of our model by comparing the speed distribution against a large experimental dataset for the London area with remarkable results, which also translate into a qualitative similarity of the congestion maps. Also, percolation analysis confirms a quantitative agreement of the model with the real data for London. For seven other topologically different cities we performed simulations to obtain the Fisher critical exponent $τ$ that showed no common functional dependence on the traffic level. The critical exponent $γ$, studied to assess the power-law decay of spatial correlation, was found inversely proportional to the number of vehicles both for real and simulated traffic. This simulation approach seems particularly fit to describe qualitative and quantitative properties of the network loading process, culminating in peak-hour congestion, by using only topological and geographical network features.

physics.soc-ph↗
arXiv2022

Estimating Peak-Hour Traffic Congestion Patterns For Interacting Agents On Urban Networks

We study the emergence of congestion patterns in urban networks by modeling vehicular interaction by means of a simple traffic rule and by using a set of measures inspired by the standard Betweenness Centrality (BC). We consider a topologically heterogeneous group of cities and simulate the network loading during the morning peak-hour by increasing the number of circulating vehicles. At departure, vehicles are aware of the network state and choose paths with optimal traversal time. Each added path modifies the vehicular density and travel times for the following vehicles. Starting from an empty network and adding traffic until transportation collapses, provides a framework to study network's transition to congestion and how connectivity is progressively disrupted as the fraction of impossible paths becomes abruptly dominant. We use standard BC to probe into the instantaneous out-of-equilibrium network state for a range of traffic levels and show how this measure may be improved to build a better proxy for cumulative road usage during peak-hours. We define a novel dynamical measure to estimate cumulative road usage and the associated total time spent over the edges by the population of drivers. We also study how congestion starts with dysfunctional edges scattered over the network, then organizes itself into relatively small, but disruptive clusters.

physics.soc-ph↗
arXiv2021

On the stability of traffic breakup patterns in urban networks

We investigate the behavior of extended urban traffic networks within the framework of percolation theory by using real and synthetic traffic data. Our main focus shifts from the statistical properties of the cluster size distribution studied recently, to the spatial analysis of the clusters at criticality and to the definition of a similarity measure between whole urban configurations. We discover that the breakup patterns of the complete network, formed by the connected functional road clusters at criticality, show remarkable stability from one hour to the next, and predictability for different days at the same time. We prove this by showing how the average spatial distributions of the highest-rank clusters evolve over time, and by building a taxonomy of traffic states via dimensionality-reduction of the distance matrix, obtained via a clustering similarity score. Finally, we show that a simple random percolation model can approximate the breakup patterns of heavy real traffic when long-ranged spatial correlations are imposed.

physics.soc-ph↗
arXiv2020

On the breakup patterns of urban networks under load

The urban networks of London and New York City are investigated as directed graphs within the paradigm of graph percolation. It has been recently observed that urban networks show a critical percolation transition when a fraction of edges are removed. The resulting strongly connected components form a cluster structure whose size distribution follows a power law with critical exponent $τ$. We start by analyzing how the networks react when subjected to increasingly widespread random edge removal and the effect of spatial correlations with power-law decay. We observe a progressive decrease of $τ$ as spatial correlations grow. A similar phenomenon happens when real traffic data (UBER) is used to delete congested graph edges: during low congestion periods $τ$ is close to that obtained for uncorrelated percolation, while during rush hours the exponent becomes closer to the one obtained from spatially correlated edge removal. We show that spatial correlations seem to play an important role to model different traffic regimes. We finally present some preliminary evidence that, at criticality, the largest clusters display spatial predictability and that cluster configurations form a taxonomy with few main clades: only a finite number of breakup patterns, specific for each city, exists. This holds both for random percolation and real traffic, where the three largest clusters are well localized and spatially distinct. This consistent cluster organization is strongly influenced by local topographical structures such as rivers and bridges.

physics.soc-ph↗
arXiv2017

Ultrametricity of optimal transport substates for multiple interacting paths over a square lattice network

We model a set of point-to-point transports on a network as a system of polydisperse interacting self-avoiding walks (SAWs) over a finite square lattice. The ends of each SAW may be located both at random, uniformly distributed, positions or with one end fixed at a lattice corner. The total energy of the system is computed as the sum over all SAWs, which may represent either the time needed to complete the transport over the network, or the resources needed to build the networking infrastructure. We focus especially on the second aspect by assigning a concave cost function to each site to encourage path overlap. A Simulated Annealing optimization, based on a modified BFACF Montecarlo algorithm developed for polymers, is used to probe the complex conformational substates structure. We characterize the average cost gains (and path-length variation) for increasing polymer density with respect to a Dijkstra routing and find a non-monotonic behavior as previously found in random networks. We observe the expected phase transition when switching from a convex to a concave cost function (e.g., $x^γ$, where $x$ represents the node overlap) and the emergence of ergodicity breaking, finally we show that the space of ground states for $γ<1$ is compatible with an ultrametric structure as seen in many complex systems such as some spin glasses.

cond-mat.stat-mech↗
arXiv2016

Transition to congestion in communication/computation networks: from ideal to realistic resource allocation via Montecarlo simulations

We generalize previous studies on critical phenomena in communication networks by adding computational capabilities to the nodes to better describe real-world situations such as cloud computing. A set of tasks with random origin and destination with a multi-tier computational structure is distributed on a network modeled as a graph. The execution time (or latency) of each task is statically computed and the sum is used as the energy in a Montecarlo simulation in which the temperature parameter controls the resource allocation optimality. We study the transition to congestion by varying temperature and system load. A method to approximately recover the time-evolution of the system by interpolating the latency probability distributions is presented. This allows us to study the standard transition to the congested phase by varying the task production rate. We are able to reproduce the main known results on network congestion and to gain a deeper insight over the maximum theoretical performance of a system and its sensitivity to routing and load balancing errors.

cs.NI↗