Physics-informed deep learning for traffic state estimation based on the traffic flow model and computational graph method
Traffic state estimation (TSE) is a critical task for intelligent transportation systems. However,
it is extremely challenging because the traffic data quality is often affected by the installation …
it is extremely challenging because the traffic data quality is often affected by the installation …
Traffic state estimation using stochastic Lagrangian dynamics
This paper proposes a new stochastic model of traffic dynamics in Lagrangian coordinates.
The source of uncertainty is heterogeneity in driving behavior, captured using driver-specific …
The source of uncertainty is heterogeneity in driving behavior, captured using driver-specific …
Real-time traffic prediction and probing strategy for Lagrangian traffic data
The objective of this paper is to present a new analytical tool that predicts highway
congestion in real time by utilizing a macroscopic traffic flow model, and to investigate a data …
congestion in real time by utilizing a macroscopic traffic flow model, and to investigate a data …
Traffic state estimation based on Eulerian and Lagrangian observations in a mesoscopic modeling framework
The paper proposes a model-based framework for estimating traffic states from Eulerian
(loop) and/or Lagrangian (probe) data. Lagrangian-Space formulation of the LWR model …
(loop) and/or Lagrangian (probe) data. Lagrangian-Space formulation of the LWR model …
IQGA: A route selection method based on quantum genetic algorithm-toward urban traffic management under big data environment
The increasingly serious problem of traffic congestion has become a critical issue that urban
managers need to focus on. However, as urban scale and structure have already taken …
managers need to focus on. However, as urban scale and structure have already taken …
A multiagent systems with Petri Net approach for simulation of urban traffic networks
MF Geronimo, EGH Martinez, EDF Vazquez… - … Environment and Urban …, 2021 - Elsevier
This paper presents a novel model framework for complex urban traffic systems based on
the interconnection of a dynamical multi-agent system in a macroscopic level. The agents …
the interconnection of a dynamical multi-agent system in a macroscopic level. The agents …
A traffic state recognition model based on feature map and deep learning
C Wang, W Zhang, C Wu, H Hu, H Ding… - Physica A: Statistical …, 2022 - Elsevier
Real-time and accurate traffic state identification can provide reference for urban traffic
control and guidance. Due to the randomness and complexity of traffic flow, it is difficult to …
control and guidance. Due to the randomness and complexity of traffic flow, it is difficult to …
Traffic state estimation by backward moving observers: An application and validation under an incident
M Kuwahara, A Takenouchi, K Kawai - Transportation research part C …, 2021 - Elsevier
This study analyzes measurements by backward moving observers that could be probe
vehicles running backward on the opposite lane observing forward moving traffic to be …
vehicles running backward on the opposite lane observing forward moving traffic to be …
[PDF][PDF] Stochastic Lagrangian modeling of traffic dynamics
This paper proposes a new stochastic model of traffic dynamics in Lagrangian coordinates.
The source of uncertainty in the proposed model is parametric. Specifically, we assume that …
The source of uncertainty in the proposed model is parametric. Specifically, we assume that …
Traffic state estimation and its sensitivity utilizing measurements from the opposite lane
A Takenouchi, K Kawai, M Kuwahara - Transportation research part C …, 2019 - Elsevier
This study proposes a method that estimates traffic states using measurements from a
vehicle running on the opposite lane in addition to probe vehicle data and examine the …
vehicle running on the opposite lane in addition to probe vehicle data and examine the …