Analytical prediction of self-organized traffic jams as a function of increasing ACC penetration

Kshitij Jerath, Sean N. Brennan

Research output: Contribution to journalArticlepeer-review

35 Scopus citations

Abstract

Self-organizing traffic jams are known to occur in medium-to-high density traffic flows, and it is suspected that adaptive cruise control (ACC) may affect their onset in mixed human-ACC traffic. Unfortunately, closed-form solutions that predict the occurrence of these jams in mixed human-ACC traffic do not exist. In this paper, both human and ACC driving behaviors are modeled using the General Motors fourth car-following model and are distinguished by using different model parameter values. A closed-form solution that explains the impact of ACC on congestion due to the formation of self-organized traffic jams (or phantom jams) is presented. The solution approach utilizes the master equation for modeling the self-organizing behavior of traffic flow at a mesoscopic scale and the General Motors fourth car-following model for describing the driver behavior at the microscopic scale. It is found that, although the introduction of ACC-enabled vehicles into the traffic stream may produce higher traffic flows, it also results in disproportionately higher susceptibility of the traffic flow to congestion.

Original languageEnglish (US)
Article number6342914
Pages (from-to)1782-1791
Number of pages10
JournalIEEE Transactions on Intelligent Transportation Systems
Volume13
Issue number4
DOIs
StatePublished - Nov 29 2012

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Computer Science Applications
  • Mechanical Engineering

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