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The congestion formation on a urban road network is one of the key issue for the development of a sustainable mobility in the future smart cities. In this work we propose a reductionist approach studying the stationary states of a simple transport model using of a random process on a graph, where each node represents a location and the weight links give the transition rates to move from one node to another that represent the mobility demand. Each node has a finite transport capacity and a maximum load capacity and we assume that the average. In the approximation of the single step process we are able to analytically characterize the traffic load distribution on the single nodes, using a local Maximum Entropy Principle. Our results explain how the congested nodes emerge when the total traffic load increases in analogous way to a percolation transition where the appearance of a congested node is a independent random event, However, using numerical simulations, we show that in the more realistic case of the synchronous dynamics for the nodes, there are entropic forces that introduce correlation among the node state and favor the clustering of the empty and congested nodes. Our aim is to highlight universal properties of the congestion formation and, in particular, to understand the role traffic load fluctuations as a possible precursor of congestion in a transport network., Comment: 24 pages, 8 figures, regular article

Diffusion on complex networks is a convenient framework to describe a great variety of transport systems. Failure phenomena in a link of the network may simulate the presence of a break or a congestion effect in the system. A real time detection of failures can mitigate their effect and allow to optimize the control procedures on the transport network. The main objective of this work is to provide a dimensionality reduction technique for a transport network where a diffusive dynamics takes place, to detect presence of a failure by a limited number of observations. Our approach is based on the susceptibility response of the network state under random perturbations of the link weights. The correlations among the nodes fluctuations is exploited in order to provide the clustering procedure. The network dimensionality is therefore reduced introducing `representative nodes' for each cluster and generating a reduced network model, whose dynamical state is detected by the observations. We realize a failure identification procedure for the whole network, studying the dynamics of the coarse-grained network. The localization efficiency of the proposed clustering algorithm, averaging over all possible single-edge failures, is compared with traditional structure-based clustering using different graph configurations. We show that the proposed clustering algorithm is more sensitive than traditional clustering techniques to detect link failure with higher stationary fluxes., Comment: 31 pages

The statistical properties of human mobility have been studied in the framework of complex systems physics. Taking advantage from the new datasets made available by the information and communication technologies, the distributions of mobility path lengths and of trip duration have been considered to discover the fingerprints of complexity characters, but the role of the different transportation means on the statistical properties of urban mobility has not been studied in deep. In this paper we cope with the problem of pointing out the existence of universal features for different type of individual mobility: pedestrian, cycling and vehicular urban mobility. In particular, we propose the use of travel time as universal 'energy' for the mobility and we define a simple survival model that explains the travel time distribution of the different types of mobility. the analysis is performed in the metropolitan area of Bologna (Italy), where GPS datasets were available on individual trips using different transport means. Our results could suggest how to plan the different transportation networks to realize a multimodal mobility compatibly with the citizens propensities to use the different transport means.

In this paper we introduce a spectra preserving relation between graphs with loops and graphs without loops. This relation is achieved in two steps. First, by generalizing spectra results got on (m, k)-stars to a wider class of graphs, the (m, k, s)-stars with or without loops. Second, by defining a covering space of graphs with loops that allows to remove the presence of loops by increasing the graph dimension. The equivalence of the two class of graphs allows to study graph with loops as simple graph without loosing information.

In this paper we study two classes of graphs, the (m,k)-stars and l-dependent graphs, investigating the relation between spectrum characteristics and graph structure: conditions on the topology and edge weights are given in order to get values and multiplicities of Laplacian matrix eigenvalues. We prove that a vertex set reduction on graphs with (m,k)-star subgraphs is feasible, keeping the same eigenvalues with reduced multiplicity. Moreover, some useful eigenvectors properties are derived up to a product with a suitable matrix. Finally, we relate these results with Fiedler spectral partitioning of the graph. The physical relevance of the results is shortly discussed.

Transportation planning is strongly influenced by the assumption that every individual has for his daily mobility a constant daily budget of ~1 hour. However, recent experimental results are proving this assumption as wrong. Here, we study the differences in daily travel-time expenditures among 24 Italian cities, extracted from a large set of GPS data on vehicles mobility. To understand these variations at the level of individual behaviour, we introduce a trip duration model that allows for a description of the distribution of travel-time expenditures in a given city using two parameters. The first parameter reflects the accessibility of desired destinations, whereas the second one can be associated to a travel-time budget and represents physiological limits due to stress and fatigue. Within the same city, we observe variations in the distributions according to home position, number of mobility days and a driver's average number of daily trips. These results can be interpreted by a stochastic time-consumption model, where the generalised cost of travel times is given by a logarithmic-like function, in agreement with the Weber-Fechner law. Our experimental results show a significant variability in the travel-time budgets in different cities and for different categories of drivers within the same city. This explicitly clashes with the idea of the existence of a constant travel-time budget and opens new perspectives for the modeling and governance of urban mobility., Comment: 12+6 Pages, 6+2 Figures, 1+1 Tables

The recent availability of large databases allows to study macroscopic properties of many complex systems. However, inferring a model from a fit of empirical data without any knowledge of the dynamics might lead to erroneous interpretations [6]. We illustrate this in the case of human mobility [1-3] and foraging human patterns [4] where empirical long-tailed distributions of jump sizes have been associated to scale-free super-diffusive random walks called L\'evy flights [5]. Here, we introduce a new class of accelerated random walks where the velocity changes due to acceleration kicks at random times, which combined with a peaked distribution of travel times [7], displays a jump length distribution that could easily be misinterpreted as a truncated power law, but that is not governed by large fluctuations. This stochastic model allows us to explain empirical observations about the movements of 780,000 private vehicles in Italy, and more generally, to get a deeper quantitative understanding of human mobility., Comment: 10 pages, 6 figures + Supplementary information

We model chaotic diffusion, in a symplectic 4D map by using the result of a theorem that was developed for stochastically perturbed integrable Hamiltonian systems. We explicitly consider a map defined by a free rotator (FR) coupled to a standard map (SM). We focus in the diffusion process in the action, $I$, of the FR, obtaining a semi--numerical method to compute the diffusion coefficient. We study two cases corresponding to a thick and a thin chaotic layer in the SM phase space and we discuss a related conjecture stated in the past. In the first case the numerically computed probability density function for the action $I$ is well interpolated by the solution of a Fokker-Planck (F-P) equation, whereas it presents a non--constant time delay respect to the concomitant F-P solution in the second case suggesting the presence of an anomalous diffusion time scale. The explicit calculation of a diffusion coefficient for a 4D symplectic map can be useful to understand the slow diffusion observed in Celestial Mechanics and Accelerator Physics., Comment: This is the author's version of a work that was submitted to Physical Review E (http://pre.aps.org)

Understanding human mobility from a microscopic point of view may represent a fundamental breakthrough for the development of a statistical physics for cognitive systems and it can shed light on the applicability of macroscopic statistical laws for social systems. Even if the complexity of individual behaviors prevents a true microscopic approach, the introduction of mesoscopic models allows the study of the dynamical properties for the non-stationary states of the considered system. We propose to compute various entropy measures of the individual mobility patterns obtained from GPS data that record the movements of private vehicles in the Florence district, in order to point out new features of human mobility related to the use of time and space and to define the dynamical properties of a stochastic model that could generate similar patterns. Moreover, we can relate the predictability properties of human mobility to the distribution of time passed between two successive trips. Our analysis suggests the existence of a hierarchical structure in the mobility patterns which divides the performed activities into three different categories, according to the time cost, with different information contents. We show that a Markov process defined by using the individual mobility network is not able to reproduce this hierarchy, which seems the consequence of different strategies in the activity choice. Our results could contribute to the development of governance policies for a sustainable mobility in modern cities.

In this paper, we extend some ideas of statistical physics to describe the properties of human mobility. From a physical point of view, we consider the statistical empirical laws of private cars mobility, taking advantage of a GPS database which contains a sampling of the individual trajectories of 2% of the whole vehicle population in an Italian region. Our aim is to discover possible "universal laws" that can be related to the dynamical cognitive features of individuals. Analyzing the empirical trip length distribution we study if the travel time can be used as universal cost function in a mesoscopic model of mobility. We discuss the implications of the elapsed times distribution between successive trips that shows an underlying Benford's law, and we study the rank distribution of the average visitation frequency to understand how people organize their daily agenda. We also propose simple stochastic models to suggest possible explanations of the empirical observations and we compare our results with analogous results on statistical properties of human mobility presented in the literature.

In this paper we study noise-induced bistability in a specific circuit with many biological implications, namely a single-step enzymatic cycle described by Michaelis Menten equations with quasi-steady state assumption. We study the system both with a Master Equation formalism, and with the Fokker-Planck continuous approximation, characterizing the conditions in which the continuous approach is a good approximation of the exact discrete model. An analysis of the stationary distribution in both cases shows that bimodality can not occur in such a system. We discuss which additional requirements can generate stochastic bimodality, by coupling the system with a chemical reaction involving enzyme production and turnover. This extended system shows a bistable behaviour only in specific parameter windows depending on the number of molecules involved, providing hints about which should be a feasible system size in order that such a phenomenon could be exploited in real biological systems., Comment: 6 pages, 4 figures

The application of Statistical Physics to social systems is mainly related to the search for macroscopic laws, that can be derived from experimental data averaged in time or space,assuming the system in a steady state. One of the major goals would be to find a connection between the statistical laws to the microscopic properties: for example to understand the nature of the microscopic interactions or to point out the existence of interaction networks. The probability theory suggests the existence of few classes of stationary distributions in the thermodynamics limit, so that the question is if a statistical physics approach could be able to enroll the complex nature of the social systems. We have analyzed a large GPS data base for single vehicle mobility in the Florence urban area, obtaining statistical laws for path lengths, for activity downtimes and for activity degrees. We show also that simple generic assumptions on the microscopic behavior could explain the existence of stationary macroscopic laws, with an universal function describing the distribution. Our conclusion is that understanding the system complexity requires dynamical data-base for the microscopic evolution, that allow to solve both small space and time scales in order to study the transients., Comment: 17 pages, 14 figures .jpg, use imsart.cls