Statistical Models of Interactions between Vehicles during Overtaking Maneuvers

Joaquín Espinoza, Rafael Delpiano*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


A practical challenge facing the adoption of self-driving vehicles is the complex influence of the lateral dimension in vehicle traffic. This phenomenon has received little attention in the literature and few quantitative descriptions of interactions between vehicles are available for model validation. This paper proposes an analysis of the kinematic variables describing vehicle interactions on both axes during overtaking maneuvers using linear as well as nonlinear and nonparametric models based on real-world highway data. The principal findings are as follows: (a) a mutual influence between pairs of vehicles, especially at small lateral separation distances; (b) the higher the longitudinal velocity, the greater the lateral distances, no doubt to avoid collisions; and (c) lateral accelerations that tend to narrow lateral distance are associated with longitudinal accelerations that tend to widen it. These results are consistent across the different models applied and also with previous studies.

Original languageEnglish
Pages (from-to)323 - 333
Number of pages10
JournalTransportation Research Record
Issue number4
StatePublished - 13 Jul 2023

Bibliographical note

Publisher Copyright:
© National Academy of Sciences: Transportation Research Board 2023.


  • data and data science
  • microscopic traffic simulation
  • operations
  • spatial data
  • traffic flow


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