The spread of traffic jams in urban networks has long been viewed as a complex spatio-temporal phenomenon that often requires computationally intensive microscopic models for analysis purposes. In this study, we present a framework to describe the dynamics of congestion propagation and dissipation of traffic in cities using a simple contagion process, inspired by those used to model infectious disease spread in a population. We introduce two novel macroscopic characteristics of network traffic, namely congestion propagation rate and congestion dissipation rate . We describe the dynamics of congestion propagation and dissipation using these new parameters, , and , embedded within a system of ordinary differential equations, analogous to the well-known Susceptible-Infected-Recovered (SIR) model. The proposed contagion-based dynamics are verified through an empirical multi-city analysis, and can be used to monitor, predict and control the fraction of congested links in the network over time.Unlike individual link traffic shockwaves in a two-dimensional time-space diagram, which are categorized as forward or backward moving, network traffic jams evolve in multi directions over space. Therefore, we propose that a network's propagation and recovery can be characterized by two average rates, namely the congestion propagation rate and a congestion recovery rate , which together reflect the number of congested links in the network over time. These two macroscopic characteristics are critical in modeling congestion propagation and dissipation as a simple contagion process [21].Despite the complex human behavior-driven nature of traffic, we demonstrate that urban network traffic congestion follows a surprisingly similar spreading pattern as in other systems, including the spread of infectious disease in a population or diffusion of ideas in a social network, and can be described using a similar parsimonious theoretical network framework. Specifically, we model the spread of congestion in urban networks by adapting a classical epidemic model to include a propagation and recovery mechanism dependent on time-varying travel demand and consistent with fundamentals of network traffic flow theory. We illustrate the model to be a robust and predictive analytical model, and validate the framework using empirical and simulation-based numerical experiments.
Abstract:The restructuring of the electrical market, improvement in the technologies of energy production, and energy crisis have paved the way for increasing applications of distributed generation (DG) resources in recent years. Installing DG units in a distribution network may result in positive impacts, such as voltage profile improvement and loss reduction, and negative impacts, such as an increase in the short-circuit level. These impacts depend on the type, capacity, and place of these resources. Therefore, finding the optimal place and capacity of DG resources is of crucial importance. Accordingly, this paper is aimed at finding the optimal place and capacity of DG resources, in order to improve the technical parameters of the network, including the power losses, voltage profile, and short-circuit level. The proposed formulation of this paper significantly increases the convergence and the speed of the finding the answers. Furthermore, to select the optimal weighting coefficients, an algorithm is proposed. The weighting coefficients are decided on according to the requirements of each network and deciding on them optimally prevents the arbitrarily selection of these resources.The genetic algorithm is used to minimize the objective function and to find the best answers during the investigation.Finally, the proposed algorithm is tested on the Zanjan Province distribution network in Iran and the simulation results are presented and discussed.
A comprehensive numerical study was conducted to investigate heat transfer enhancement during the melting process in a 2D square cavity through dispersion of nanoparticles. A paraffin-based nanofluid containing various volume fractions of Cu was applied. The governing equations were solved on a non-uniform mesh using a pressure-based finite volume method with an enthalpy porosity technique to trace the solid-liquid interface. The effects of nanoparticle dispersion in a pure fluid and of some significant parameters, namely nanoparticle volume fraction, cavity size and hot wall temperature, on the fluid flow, heat transfer features and melting time were studied. The results are presented in terms of temperature and velocity profiles, streamlines, isotherms, moving interface position, solid fraction and dimensionless heat flux. The suspended nanoparticles caused an increase in thermal conductivity of nano-enhanced phase change material (NEPCM) compared to conventional PCM, resulting in heat transfer enhancement and a higher melting rate. In addition, the nanofluid heat transfer rate increased and the melting time decreased as the volume fraction of nanoparticles increased. The higher temperature difference between the melting temperature and the hot wall temperature expedited the melting process of NEPCM.
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