Incident Detection for an Uncertain Traffic Model

Lemarchand, Antoine; Martínez, John J.; Koenig, Damien

doi:10.3182/20120829-3-mx-2028.00115

Cited by 4 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The mode identification is based on density measurements at cell boundaries. Other works have considered the mode identification [49] which we consider as given in the paper. That is, the proposed methods in this paper are not considered with the problem of modes detection and identification; we are rather concerned with the classification of the nonlinear dynamics and scalable, robust state estimation methods.…”

Section: Dynamic Modeling Of Highway Traffic With Ramp Flowsmentioning

confidence: 99%

A Control-Theoretic Approach for Scalable and Robust Traffic Density Estimation Using Convex Optimization

Nugroho

Taha

Claudel

2021

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

Monitoring and control of traffic networks represent alternative, inexpensive strategies to minimize traffic congestion. As the number of traffic sensors is naturally constrained by budgetary requirements, real-time estimation of traffic flow in road segments that are not equipped with sensors is of significant importance-thereby providing situational awareness and guiding real-time feedback control strategies. To that end, firstly we build a generalized traffic flow model for stretched highways with arbitrary number of ramp flows based on the Lighthill Whitham Richards (LWR) flow model. Secondly, we characterize the function set corresponding to the nonlinearities present in the LWR model, and use this characterization to design real-time and robust state estimators (SE) for stretched highway segments. Specifically, we show that the nonlinearities from the derived models are locally Lipschitz continuous by providing the analytical Lipschitz constants. Thirdly, the analytical derivation is then incorporated through a robust SE method given a limited number of traffic sensors, under the impact of process and measurement disturbances and unknown inputs. The estimator is based on deriving a convex semidefinite optimization problem. Finally, numerical tests are given showcasing the applicability, scalability, and robustness of the proposed estimator for large systems under high magnitude disturbances, parametric uncertainty, and unknown inputs.

show abstract

Section: Dynamic Modeling Of Highway Traffic With Ramp Flowsmentioning

confidence: 99%

A Control-Theoretic Approach for Scalable and Robust Traffic Density Estimation Using Convex Optimization

Nugroho

Taha

Claudel

2021

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…Macroscopic traffic flow model based incident detection methods have also been developed. The work [17] detects traffic incident by identifying when the measurements obtained from the field significantly deviates from the prediction by the traffic flow model. However, the incident does not change any properties on the macroscopic model, and the traffic estimates under an incident suffers as a result.…”

Section: B Related Workmentioning

confidence: 99%

Multiple Model Particle Filter for Traffic Estimation and Incident Detection

Wang

Work

Sowers

2016

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

This paper poses the joint traffic state estimation and incident detection problem as a hybrid state estimation problem, in which a continuous variable denotes the traffic state and a discrete model variable identifies the location and severity of an incident. A multiple model particle smoother is proposed to solve the hybrid estimation problem, in which the multiple model particle filter is used to accommodate the nonlinearity and switching dynamics of the traffic incident model, and the smoothing algorithm is applied to improve the accuracy of the estimate when data are limited. The proposed algorithms are evaluated through numerical experiments using CORSIM as the true model. The proposed algorithm is also compared with a standard macroscopic traffic estimator via particle filtering and the California incident detection algorithm. The results show that jointly estimating the state and incidents in one algorithm is better than two dedicated algorithms working independently.

show abstract

“…The authors again considered a similar approach in Harrou et al (2018), however using a generalized likelihood ratio test for determining if residuals belonged to one of two possible generating distributions. Uncertainty in macroscopic models was considered in Lemarchand et al (2012), where the authors specifically consider how to detect blocked lanes and speed drops. Using the Lighthill-Whitham-Richards model with a piece-wise linear relationship between density and flow, the authors raised alarms when the residuals in flow were beyond some thresholds.…”

Section: Literature Reviewmentioning

confidence: 99%