A multiclass cell transmission model for shared human and autonomous vehicle roads

Michael W. Levin, Stephen D. Boyles

Research output: Contribution to journalArticlepeer-review

119 Scopus citations

Abstract

Autonomous vehicles have the potential to improve link and intersection traffic behavior. Computer reaction times may admit reduced following headways and increase capacity and backwards wave speed. The degree of these improvements will depend on the proportion of autonomous vehicles in the network. To model arbitrary shared road scenarios, we develop a multiclass cell transmission model that admits variations in capacity and backwards wave speed in response to class proportions within each cell. The multiclass cell transmission model is shown to be consistent with the hydrodynamic theory. This paper then develops a car following model incorporating driver reaction time to predict capacity and backwards wave speed for multiclass scenarios. For intersection modeling, we adapt the legacy early method for intelligent traffic management (Bento et al., 2013) to general simulation-based dynamic traffic assignment models. Empirical results on a city network show that intersection controls are a major bottleneck in the model, and that the legacy early method improves over traffic signals when the autonomous vehicle proportion is sufficiently high.

Original languageEnglish (US)
Pages (from-to)103-116
Number of pages14
JournalTransportation Research Part C: Emerging Technologies
Volume62
DOIs
StatePublished - Jan 1 2016

Bibliographical note

Funding Information:
The authors gratefully acknowledge the support of the Data-Supported Transportation Operations & Planning Center and the National Science Foundation under Grant No. 1254921 .

Publisher Copyright:
© 2015 Elsevier Ltd.

Keywords

  • Autonomous vehicles
  • Cell transmission model
  • Dynamic traffic assignment
  • Multiclass
  • Shared road

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