Parameter and density estimation from real-world traffic data: A kinetic compartmental approach
Journal article, 2022
CFL condition
gradient descent
macroscopic model
real traffic data
Traffic reaction model
highD
Lax–Friedrichs scheme
hyperbolic PDE
finite volume scheme
viscosity solutions
parameter estimation
Author
Mike Pereira
University of Gothenburg
Chalmers, Electrical Engineering, Systems and control
Chalmers, Mathematical Sciences
Pinar Boyraz Baykas
Chalmers, Mechanics and Maritime Sciences (M2), Vehicle Safety
Balázs Adam Kulcsár
Chalmers, Electrical Engineering, Systems and control
Annika Lang
Chalmers, Mathematical Sciences, Applied Mathematics and Statistics
Transportation Research Part B: Methodological
0191-2615 (ISSN)
Vol. 155 210-239IRIS: Inverse Reinforcement-Learning and Intelligent Swarm Algorithms for Resilient Transportation Networks
Chalmers, 2020-01-01 -- 2021-12-31.
Efficient approximation methods for random fields on manifolds
Swedish Research Council (VR) (2020-04170), 2021-01-01 -- 2024-12-31.
OPerational Network Energy managemenT for electrified buses (OPNET)
Swedish Energy Agency (46365-1), 2018-10-01 -- 2021-12-31.
Stochastic Continuous-Depth Neural Networks
Chalmers AI Research Centre (CHAIR), 2020-08-15 -- .
Real-Time Robust and AdaptIve Learning in ElecTric VEhicles (RITE)
Chalmers AI Research Centre (CHAIR), 2020-01-01 -- 2021-12-31.
Chalmers, 2020-01-01 -- 2021-12-31.
STOchastic Traffic NEtworks (STONE)
Chalmers AI Research Centre (CHAIR), -- .
Chalmers, 2020-02-01 -- 2022-01-31.
Areas of Advance
Transport
Subject Categories
Computer and Information Science
Transport Systems and Logistics
Other Mathematics
Control Engineering
DOI
10.1016/j.trb.2021.11.006