Learning Traffic Flow Dynamics Using Random Fields

Learning Traffic Flow Dynamics Using Random Fields

This paper presents a mesoscopic traffic flow model that explicitly describes the spatio-temporal evolution of the probability distributions of vehicle trajectories. The dynamics are represented by a sequence of factor graphs, which enable learning of traffic dynamics from limited Lagrangian measure...

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Bibliographic Details
Main Authors: Saif Eddin G. Jabari, Deepthi Mary Dilip, Dianchao Lin, Bilal Thonnam Thodi
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Stochastic traffic dynamics
conditional random fields
Markov random fields
factor graphs
traffic state estimation
Online Access:https://ieeexplore.ieee.org/document/8835040/

Internet

https://ieeexplore.ieee.org/document/8835040/

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