Real-time freeway traffic state estimation based on extended Kalman filter: a general approach

By Yibing Wang, Markos Papageorgiou. 2005

874 citations recorded by the source corpus.

Subfields: Building and Construction, Control and Systems Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/5ac335eadd259f82acbbba266511d9ee9b56df44

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Citations in the corpus

Analytical and grid-free solutions to the Lighthill-Whitham-Richards traffic flow model (2011, background): “For instance, most traffic estimation applications require high resolution output such as δx ≤ 200m, see for example [22, 36, 23, 31].”
Traffic Flow Prediction via Spatial Temporal Graph Neural Network (2020, methodology): “• ARIMA: Auto-regressive Integrated Moving Average model [21, 42, 47] with Kalman filter is widely used in time series analysis.”
Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments (2017, background): “Advancements in traffic state estimation [21, 22, 23] have facilitated high resolution traffic monitoring, through the advent of GPS smartphone sensors [24, 25, 26, 27] that are part of the flow—termed Lagrangian or mobile sensors.”
A traffic model for velocity data assimilation (2010, methodology): “…samples v i (x i (t), t) are shown at t = t m , from vehicles i transmitting their data (indicated by up-arrows above the vehicles). velocity trajectories into an extension of the CTM, called the using a model resulting from the discretization of a PDE system for speed and density was used in [32].”
Traffic state estimation on highway: A comprehensive survey (2017, methodology): “…knowledge, there is no comprehensive survey on TSE, although more than a decade has passed from the early and influential work on highway TSE by Wang and Papageorgiou (2005) and there have been considerable advances in this field.2 This makes it difficult to assess available approaches,…”
Data fusion in intelligent transportation systems: Progress and challenges - A survey (2011, methodology): “In [66] and [67], the problem of constructing an adaptive online traffic control in urban or freeway road networks was investigated.”
Microscopic Calibration and Validation of Car-Following Models – A Systematic Approach (2013, methodology): “Since we focus on model properties rather than on real-time tra ﬃ c state estimation, we will not investigate calibration by extended Kalman ﬁlters where the parameters and the tra ﬃ c state are estimated simultaneously by maximum-likelihood techniques [17, 18, 1].”
An ensemble Kalman filtering approach to highway traffic estimation using GPS enabled mobile devices (2008, methodology): “A real time algorithm for traffic estimation based on the Extended Kalman Filter (EKF) using second order PDE as a flow model was used in [13].”
Optimized Graph Convolution Recurrent Neural Network for Traffic Prediction (2021, methodology): “Then, based on KFs, Wang and Papageorgiou [5], [6] investigated an Extended Kalman Filters (EKFs) algorithm by incorporating a stochastic macroscopic freeway network.”
Real-time multi-sensor multi-source network data fusion using dynamic traffic assignment models (2009, background): “[17] Wang, Y., Papageorgiou, M., and Messmer, A. (2007) “Real-Time Freeway Traffic State Estimation based on Extended Kalman Filter: A Case Study”.”