Empirical evidence for a jamming transition in urban traffic

Abstract

Understanding the mechanisms leading to the formation and the propagation of traffic jams in large cities is of crucial importance for urban planning and traffic management. Many studies have already considered the emergence of traffic jams from the point of view of phase transitions, but mostly in simple geometries such as highways for example, or in the framework of percolation where an external parameter is driving the transition. More generally, empirical evidence and characterization for a congestion transition in complex road networks is scarce, and here we use traffic measures for Paris (France) during the period 2014-2018 for testing the existence of a jamming transition at the urban level. In particular, we show that the correlation function of delays due to congestion is a power law (with exponent η ≈ 0.4) combined with an exponential cut-off ξ. This correlation length ξ is shown to diverge during rush hours, pointing to a jamming transition in urban traffic. We also discuss the spatial structure of congestion and identify a core of congested links that participate in most traffic jams and whose structure is specific during rush hours. Finally, we show that the spatial structure of congestion is consistent with a reaction-diffusion picture proposed previously.

Estimating Peak-Hour Traffic Congestion Patterns For Interacting Agents On Urban Networks

“…This phase transition has been well studied via percolation theory and it is known that the size distribution of the resulting sub networks follows a power law with a critical exponent that depends on traffic intensity and on the time of the day. This transition has been observed for real world datasets in large cities such as London, Beijing and New York City [4,13,14]. During the most extreme conditions, total network breakdown has been observed to last for several hours or even days [5].…”

“…If the fastest route from origin to destination comprises a dysfunctional edge (i.e., the network is disconnected) we still choose to add the initial part of the path, but we will skip the edges starting from the first dysfunctional one. This allows us to model the backward propagation of traffic jams observed at high traffic volumes [14,20]. Since the order in which OD pairs are added to the network can lead to different final results, we replicate the system and perform several simulations to analyze the stability of the results.…”