A simple contagion process describes spreading of traffic jams in urban networks

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 \b{eta} and congestion dissipation rate {\mu}. We describe the dynamics of congestion propagation and dissipation using these new parameters, \b{eta}, and {\mu}, 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.Comment: 10 pages, 8 figure

Modelling Traffic Congestion as a Spreading Phenomenon

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. This dissertation presents frameworks 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. Specifically, we model the spread of congestion in urban networks by adapting a classical epidemic model to include a propagation and dissipation mechanism dependent on time-varying travel demand and consistent with the fundamentals of network traffic flow theory. We describe the dynamics of congestion spread using two macroscopic new parameters (propagation rate β and recovery rate μ) embedded within a system of ordinary differential equations, similar to the well-known susceptible-infected-recovered (SIR) model. For simplicity, we initially assumed the topological distribution of road networks to be homogeneous. The proposed contagion-based dynamics are verified through empirical multi-city analysis. In addition to the simplistic homogeneous SIR approach, we also explored the significance of the degree of heterogeneity in urban street networks using both empirical and simulation-based traffic data. However, this approach inherently assumes an undirected network with uniform recovery rate compared to a road network, which usually displays directed flow and topological reliance on congestion recovery. Keeping in view of these constraints, we proposed a modification to the heterogeneous mean-field model to describe the spreading process of congestion in urban street networks. A practical application of the proposed model is also tested in this dissertation in the context of signal optimisation using cycle length as a controlling function. The experiments helped quantify the characteristic differences between two widely used traffic assignment models, i.e. Dynamic User Equilibrium (DUE) and Stochastic Route Choice (SRC). Comparison has been made at two levels: link-level flows and network-level congestion patterns. Furthermore, we explored an alternative approach for congestion dynamic modelling, namely the "Reaction-Diffusion (RD) model", with a similar concept as our proposed frameworks but at link-level. This model, with its complexity, requires higher computation time with detailed link-level information of congestion dynamics

Traffic Congestion Analysis Using SIR Epidemic Model

In this work, we propose a mathematical model to represent traffic congestion in the street under some consideration. A congestion problem in a city highway becomes a critical issue since congestion at one point affected congestion propagation on the other points. We focus on the propagation of traffic propagation by adopting the concept of disease spread using the SIR model. We consider that the disease in traffic problems is congestion. Meanwhile, vehicles that enter the highway are susceptible to congestion. In contrast, vehicles free from traffic jams represent individuals free from disease. The SIR model can explain the spread of congestion by looking at the congestion variable as an infected variable. We discuss and analyze the existence and stability of the equilibrium points. The local stability equilibrium point is verified using the Routh-Hurwitz criteria. At the same time, the global stability is analyzed using Lyapunov function. The numerical simulation is provided in the last section to validate the discussion results

Impacts of Covid-19 mode shift on road traffic

This article is driven by the following question: as the communities reopen after the COVID-19 pandemic, will changing transportation mode share lead to worse traffic than before? This question could be critical especially if many people rush to single occupancy vehicles. To this end, we estimate how congestion will increases as the number of cars increase on the road, and identify the most sensitive cites to drop in transit usage. Travel time and mode share data from the American Community Survey of the US Census Bureau, for metro areas across the US. A BPR model is used to relate average travel times to the estimated number of commuters traveling by car. We then evaluate increased vehicle volumes on the road if different portions of transit and car pool users switch to single-occupancy vehicles, and report the resulting travel time from the BPR model. The scenarios predict that cities with large transit ridership are at risk for extreme traffic unless transit systems can resume safe, high throughput operations quickly.Comment: 14 pages, 11 figure

Why society is a complex problem? A review of Philip Ball’s book – Meeting Twenty-first Century Challenges with a New Kind of Science

The 21st century is burdened by a series of dramatic changes and efforts are carried out to find potential solutions to consumerism, access to information, transient climate disequilibria, health care and demographic transformations. A new page in human history will bear witness to the introduction of new ways of thinking, new changes, new relationships and interconnections that transcend states and societies. The moment is ripe for individuals aware of the implications carried by global changes and challenges, to step up and encourage responsibility and sustainable development. Mankind is currently living in a data-rich world, where information is widely dispersed. Nevertheless, extracting the right assumptions and conclusions from the available data proves difficult as numerous social phenomena do not run with clockwork precision as the laws governing the Newtonian universe.Human awareness and intelligence demand a more responsible approach to all operations and steps should be made in determining the consequences and their impact. The goal of this paper is not restricted to providing a review but also to enforce certain ideas in relation to the complex interactions specific to society and economic activities

Why society is a complex problem? A review of Philip Ball’s book – Meeting Twenty-first Century Challenges with a New Kind of Science

The 21st century is burdened by a series of dramatic changes and efforts are carried out to find potential solutions to consumerism, access to information, transient climate disequilibria, health care and demographic transformations. A new page in human history will bear witness to the introduction of new ways of thinking, new changes, new relationships and interconnections that transcend states and societies. The moment is ripe for individuals aware of the implications carried by global changes and challenges, to step up and encourage responsibility and sustainable development. Mankind is currently living in a data-rich world, where information is widely dispersed. Nevertheless, extracting the right assumptions and conclusions from the available data proves difficult as numerous social phenomena do not run with clockwork precision as the laws governing the Newtonian universe.Human awareness and intelligence demand a more responsible approach to all operations and steps should be made in determining the consequences and their impact. The goal of this paper is not restricted to providing a review but also to enforce certain ideas in relation to the complex interactions specific to society and economic activities

Challenges in Complex Systems Science

FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems route to Social Sciences. This include complex systems having: many heterogeneous interacting parts; multiple scales; complicated transition laws; unexpected or unpredicted emergence; sensitive dependence on initial conditions; path-dependent dynamics; networked hierarchical connectivities; interaction of autonomous agents; self-organisation; non-equilibrium dynamics; combinatorial explosion; adaptivity to changing environments; co-evolving subsystems; ill-defined boundaries; and multilevel dynamics. In this context, science is seen as the process of abstracting the dynamics of systems from data. This presents many challenges including: data gathering by large-scale experiment, participatory sensing and social computation, managing huge distributed dynamic and heterogeneous databases; moving from data to dynamical models, going beyond correlations to cause-effect relationships, understanding the relationship between simple and comprehensive models with appropriate choices of variables, ensemble modeling and data assimilation, modeling systems of systems of systems with many levels between micro and macro; and formulating new approaches to prediction, forecasting, and risk, especially in systems that can reflect on and change their behaviour in response to predictions, and systems whose apparently predictable behaviour is disrupted by apparently unpredictable rare or extreme events. These challenges are part of the FuturICT agenda