Literature DB >> 30530677

Macroscopic dynamics and the collapse of urban traffic.

Luis E Olmos1,2,3,4, Serdar Çolak2, Sajjad Shafiei5, Meead Saberi6, Marta C González7,3,4.   

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.

Keywords:  directed percolation; human mobility; mobile phone; nonequilibrium phase transition; urban traffic gridlock

Year:  2018        PMID: 30530677      PMCID: PMC6294880          DOI: 10.1073/pnas.1800474115

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  2 in total

1.  Forecasting innovations in science, technology, and education.

Authors:  Katy Börner; William B Rouse; Paul Trunfio; H Eugene Stanley
Journal:  Proc Natl Acad Sci U S A       Date:  2018-12-11       Impact factor: 11.205

2.  Large-scale simulation of traffic flow using Markov model.

Authors:  Renátó Besenczi; Norbert Bátfai; Péter Jeszenszky; Roland Major; Fanny Monori; Márton Ispány
Journal:  PLoS One       Date:  2021-02-09       Impact factor: 3.240

  2 in total

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