Understanding the Lateral Dimension of Traffic: Measuring and Modeling Lane Discipline

There is growing interest in understanding the lateral dimension of traffic. This trend has been motivated by the detection of phenomena unexplained by traditional models and the emergence of new technologies.Previous attempts to address this dimension have focused on lane changing and non-lane-based traffic. The literature on vehicles keeping their lanes has generally been limited to simple statistics on vehicle position while models assume vehicles stay perfectly centered.Previously the author developed a two-dimensional traffic model aimed to capture such behavior qualitatively. Still pending is a deeper, more accurate comprehension and modelling of the relationships between variables in both axes.The present article is based on the NGSIM datasets. It was found that lateral position is highly dependent on the longitudinal position, a phenomenon consistent with data capture from multiple cameras. A methodology is proposed to alleviate this problem. Also discovered was that the standard deviation of lateral velocity grows with longitudinal velocity and that the average lateral position varies with longitudinal velocity by up to 8 cm, possibly reflecting greater caution in overtaking.Random walk models were proposed and calibrated to reproduce some of the characteristics measured. It was determined that drivers' response is much more sensitive to the lateral velocity than to position.These results provide a basis for further advances in understanding the lateral dimension. It is hoped that such comprehension will facilitate the design of autonomous vehicle (AV) algorithms that are friendlier to both passengers and the occupants of surrounding vehicles.


  • English

Media Info

  • Media Type: Web
  • Features: Figures; References; Tables;
  • Pagination: 20p

Subject/Index Terms

Filing Info

  • Accession Number: 01764229
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-02762
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:23AM