Macroscopic dynamics and the collapse of urban traffic

Abstract

Stories of mega-jams that last tens of hours or even days appear not only in fiction but also in reality. In this context, it is important to characterize the collapse of the network, defined as the transition from a characteristic travel time to orders of magnitude longer for the same distance traveled. In this multicity study, we unravel this complex phenomenon under various conditions of demand and translate it to the travel time of the individual drivers. First, we start with the current conditions, showing that there is a characteristic time τ that takes a representative group of commuters to arrive at their destinations once their maximum density has been reached. While this time differs from city to city, it can be explained by Γ, defined as the ratio of the vehicle miles traveled to the total vehicle distance the road network can support per hour. Modifying Γ can improve τ and directly inform planning and infrastructure interventions. In this study we focus on measuring the vulnerability of the system by increasing the volume of cars in the network, keeping the road capacity and the empirical spatial dynamics from origins to destinations unchanged. We identify three states of urban traffic, separated by two distinctive transitions. The first one describes the appearance of the first bottlenecks and the second one the collapse of the system. This collapse is marked by a given number of commuters in each city and it is formally characterized by a nonequilibrium phase transition.

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

“…Traffic jams in cities propagate over time and space. Existing approaches to model city traffic often rely on microscopic models with high computational burden as well as excessive parameterization required for calibration [1][2][3]. Further, the lack of available transport infrastructure data in many countries, especially those that are developing, poses a challenge for traffic modelers.…”

Forecasting innovations in science, technology, and education

“…The study uses original data collected on 3,000 different scientific prizes in diverse disciplines and the career histories of 10,455 worldwide prizewinners covering more than 100 years of science. Their work uncovers (i) the relatively small and densely clustered number of ideas and scholars who lead scientific thinking (e.g., 64.1% of prizewinners have won two prizes, and 13.7% have won five or more prizes); (ii) the interlocks among different prizes within and between disciplines, which are formed by multiple prizes being won by the same scientist whose ideas then gain credit and spread through the prize network; and (iii) the genealogical and coauthorship networks that predict who wins multiple prizes. Whereas scientific prizes were once mainly thought to be measures only of personal acclaim, they can now be recognized as performing multiple functions in science regarding the legitimation (44), spread, and stratification of ideas and having a network structure that reveals the "high level of interconnectedness among acclaimed scientists and their path breaking ideas.…”

“…"Macroscopic dynamics and the collapse of urban traffic" by Olmos et al (64) aims to inform planning and infrastructure interventions. Many workers-scholars included-endure long commute times (and long-distance relationships).…”

Drivers learn city-scale dynamic equilibrium

“…Quantitative understanding of collective human behavior and its dynamics has been the focus of a long-standing, interdisciplinary collection of research. Such efforts span across physics [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15], engineering [16][17][18][19][20], economics [21][22][23][24][25][26][27], and planning [28][29][30]. Cities, as agglomerations of human beings, are the natural setting for such studies.…”

“…Random walk models are used to study human mobility at individual level [4]. Non-equilibrium phase transition is observed in the transition of urban traffic from free flow to congestion [6]. Scaling laws of socioeconomic and infrastructure indicators on population have been discovered and generative mechanisms proposed [2,11].…”