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 macroscopic characteristics for network traffic dynamics, namely congestion propagation rate β and congestion dissipation rate μ. We describe the dynamics of congestion spread using these new parameters embedded within a system of ordinary differential equations, similar 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.

Whether it be the passengers’ mobility demand in transportation systems, or the consumers’ energy demand in power grids, the primary purpose of many infrastructure networks is to best serve this flow demand. In reality, the volume of flow demand fluctuates unevenly across complex networks while simultaneously being hindered by some form of congestion or overload. Nevertheless, there is little known about how the heterogeneity of flow demand influences the network flow dynamics under congestion. To explore this, we introduce a percolation-based network analysis framework underpinned by flow heterogeneity. Thereby, we theoretically identify bottleneck links with guaranteed decisive impact on how flows are passed through the network. The effectiveness of the framework is demonstrated on large-scale real transportation networks, where mitigating the congestion on a small fraction of the links identified as bottlenecks results in a significant network improvement.

The original social force model of pedestrians and its variants have been extensively studied in the past. However, little attention has been given to their parameter estimation and validation specifically for the environments with high density and complex phenomena such as self-organization and lane formation. This paper presents a macroscopic framework for calibration and validation of a modified social force model for bidirectional pedestrian streams. We use empirical three-dimensional trajectories from a set of controlled experiments to quantify macroscopic traffic flow characteristics based on the concept of pedestrian area-wide fundamental diagram, also known as pedestrian Macroscopic Fundamental Diagram (p-MFD), measured over a 28.8 m² area. The calibration framework is formulated as a non-linear optimization problem minimizing the discrepancy between the observed and simulated p-MFD. A metaheuristic solution method based on genetic algorithm is implemented to solve the problem. The calibrated model is then applied to simulate a multi-platform train station as a complex environment with multi-directional pedestrian movements. Results demonstrate that the proposed calibration process and the modified social force model can reproduce the empirically observed p-MFD with features such as hysteresis while consistently generating microscopic emergent self-organization and lane formation phenomena. Overall, validation and application results suggest that the calibrated model successfully replicates pedestrian traffic patterns and support the future applications of the model for various operations and planning purposes.

Previous theoretical and data-driven studies on urban mobility uncovered the repeating patterns in individual and collective human behavior. This paper analyzes the travel demand characteristics of mobility networks through studying a coarse-grained representation of individual trips. Building on the idea of reducing the complexity of the mobility network, we investigate the preserved spatial and temporal information in a simplified representations of large-scale origin-destination matrices derived from more than 16 million taxi trip records from New York and Chicago. We reduce the numerous individual flows on the network into four major groups, to uncover latent collective mobility patterns in those cities. The new simplified representation of the origin-destination matrices leads to categorization of trips into distinctive flow types with specific temporal and spatial properties in each city under study. Collocation of the descriptive statistics of flow types within the two cities suggests the generalizability of the proposed approach. We extract an overall displacement metric from each of the major flows to analyze the evolution of their temporal attributes. The new representation of the demand network reveals insightful properties of the mobility system which could not have been identified from the original disaggregated representation.

Vaccination campaigns have both direct and indirect effects that act to control an infectious disease as it spreads through a population. Indirect effects arise when vaccinated individuals block disease transmission in any infection chain they are part of, and this in turn can benefit both vaccinated and unvaccinated individuals. Indirect effects are difficult to quantify in practice but, in this article, working with the susceptible–infected–recovered (SIR) model, they are analytically calculated in important cases, through pivoting on the final size formula for epidemics. Their relationship to herd immunity is also clarified. The analysis allows us to identify the important distinction between quantifying the indirect effects of vaccination at the ‘population level’ versus the ‘per capita’ level, which often results in radically different conclusions. As an example, our analysis unpacks why the population-level indirect effect can appear significantly larger than its per capita analogue. In addition, we consider a recently proposed epidemiological non-pharmaceutical intervention (by the means of recovered individuals) used over the COVID-19 pandemic, referred to as ‘shielding’, and study its impact on our mathematical analysis. The shielding scheme is extended to take advantage of vaccination including imperfect vaccination.