Traffic Flow as a Simple Fluid: Towards a Scaling Theory of Urban Congestion

Friday, October 13, 2023
12 PM – 1 PM ET

Abstract

The analogy between the theory of phase transitions in simple fluids and vehicular traffic flow has long been suspected, promising a new level of understanding of urban congestion by standing on one of the firmer foundations in physics. The obstacle has been the interpretation of the thermal energy of the gas-particle system, which remains unknown. This talk proposes the flow of cars through the network as a viable interpretation, where the fundamental diagram for traffic flow would be analogous to the coexistence curve in gas-liquid phase transitions.

Thanks to the power-law form of the coexistence curve, it was possible to formalize that the resulting network traffic model belongs to the Kardar-Parisi-Zhang universality class. The scaling relationships arising in this universality class are found to be consistent with West’s scaling theory for cities. It is shown that congestion costs (delays plus fuel consumption) scale super linearly with city population, possibly and worryingly more so than predicted by West’s theory. Implications for sustainability and resiliency are discussed.

Presenter

Jorge Laval, PhD is a professor at the School of Civil and Environmental Engineering at the Georgia Institute of Technology. He received his doctorate in civil engineering from the University of California, Berkeley, in 2004. Prior to joining Georgia Tech, Dr. Laval held two consecutive one-year postdoctoral positions at the Institute of Transportation Studies at UC Berkeley and the French National Institute for Safety and Transportation Research (INRETS/ENTPE). Dr. Laval’s main research is in the area of traffic flow theory, modeling and simulation, focusing on understanding congestion in urban networks and how to manage it. He has made significant contributions towards understanding the capacity of freeways, the connection between driver behavior and stop-and-go waves, freeway ramp-metering strategies, dynamic traffic assignment, congestion pricing, and machine learning models for congestion control.