2,010 research outputs found
Macroscopic traffic models from microscopic car-following models
We present a method to derive macroscopic fluid-dynamic models from
microscopic car-following models via a coarse-graining procedure. The method is
first demonstrated for the optimal velocity model. The derived macroscopic
model consists of a conservation equation and a momentum equation, and the
latter contains a relaxation term, an anticipation term, and a diffusion term.
Properties of the resulting macroscopic model are compared with those of the
optimal velocity model through numerical simulations, and reasonable agreement
is found although there are deviations in the quantitative level. The
derivation is also extended to general car-following models.Comment: 12 pages, 4 figures; to appear in Phys. Rev.
Offline reconstruction of missing vehicle trajectory data from 3D LIDAR
LIDAR has become an important part of many autonomous vehicles with its
advantages on distance measurement and obstacle detection. LIDAR produces point
clouds which have important information about surrounding environment. In this
paper, we collected trajectory data on a two lane urban road using a Velodyne
VLP-16 Lidar. Due to dynamic nature of data collection and limited range of the
sensor, some of these trajectories have missing points or gaps. In this paper,
we propose a novel method for recovery of missing vehicle trajectory data
points using microscopic traffic flow models. While short gaps (less than 5
seconds) can be recovered with simple linear regression, and longer gaps are
recovered with the proposed method that makes use of car following models
calibrated by assigning weights to known points based on proximity to the gaps.
Newell's, Pipes, IDM and Gipps' car following models are calibrated and tested
with the ground truth trajectory data from LIDAR and NGSIM I-80 dataset. Gipps'
calibrated model yielded the best result
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