Traffic state estimation on highway: A comprehensive survey
Traffic state estimation (TSE) refers to the process of the inference of traffic state variables
(ie, flow, density, speed and other equivalent variables) on road segments using partially …
(ie, flow, density, speed and other equivalent variables) on road segments using partially …
Adaptive multi-kernel SVM with spatial–temporal correlation for short-term traffic flow prediction
X Feng, X Ling, H Zheng, Z Chen… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
Accurate estimation of the traffic state can help to address the issue of urban traffic
congestion, providing guiding advices for people's travel and traffic regulation. In this paper …
congestion, providing guiding advices for people's travel and traffic regulation. In this paper …
A novel wavelet-SVM short-time passenger flow prediction in Bei**g subway system
Y Sun, B Leng, W Guan - Neurocomputing, 2015 - Elsevier
In order to effectively manage the use of existing infrastructures and prevent the emergency
caused by the large gathered crowd, the short-term passenger flow forecasting technology …
caused by the large gathered crowd, the short-term passenger flow forecasting technology …
A physics-informed deep learning paradigm for traffic state and fundamental diagram estimation
Traffic state estimation (TSE) bifurcates into two main categories, model-driven and data-
driven (eg, machine learning, ML) approaches, while each suffers from either deficient …
driven (eg, machine learning, ML) approaches, while each suffers from either deficient …
A traffic congestion assessment method for urban road networks based on speed performance index
F He, X Yan, Y Liu, L Ma - Procedia engineering, 2016 - Elsevier
This study aimed to analyze traffic congestion in urban road networks. The speed
performance index was adopted to evaluate the existing road network conditions of …
performance index was adopted to evaluate the existing road network conditions of …
[KNIHA][B] Stability analysis and nonlinear observer design using Takagi-Sugeno fuzzy models
Many problems in decision making, monitoring, fault detection, and control rely on the
knowledge of state variables and time-varying parameters that are not directly measured by …
knowledge of state variables and time-varying parameters that are not directly measured by …
Physics-informed deep learning for traffic state estimation: A hybrid paradigm informed by second-order traffic models
Traffic state estimation (TSE) reconstructs the traffic variables (eg, density or average
velocity) on road segments using partially observed data, which is important for traffic …
velocity) on road segments using partially observed data, which is important for traffic …
Physics-informed neural networks for integrated traffic state and queue profile estimation: A differentiable programming approach on layered computational graphs
This paper presents an integrated framework for physics-informed joint traffic state and
queue profile estimation (JSQE) on freeway corridors, utilizing heterogeneous data sources …
queue profile estimation (JSQE) on freeway corridors, utilizing heterogeneous data sources …
Real-time joint traffic state and model parameter estimation on freeways with fixed sensors and connected vehicles: State-of-the-art overview, methods, and case …
Y Wang, M Zhao, X Yu, Y Hu, P Zheng, W Hua… - … Research Part C …, 2022 - Elsevier
This paper addresses real-time joint traffic state and model parameter estimation on
freeways using data from fixed sensors and connected vehicles. It investigates how the …
freeways using data from fixed sensors and connected vehicles. It investigates how the …
Highway traffic state estimation with mixed connected and conventional vehicles
We present a macroscopic model-based approach for the estimation of the total density and
flow of vehicles, for the case of “mixed” traffic, ie, traffic comprising both ordinary and …
flow of vehicles, for the case of “mixed” traffic, ie, traffic comprising both ordinary and …