Robust identification for multi-section freeway traffic models

2005 ◽  
Vol 3 (3) ◽  
pp. 213-217 ◽  
Author(s):  
Zhongke Shi
Keyword(s):  
Freeway Traffic ◽  
Robust Identification ◽  
Traffic Models

scholarly journals Automatic calibration of fundamental diagram for first-order macroscopic freeway traffic models

2015 ◽  
Vol 50 (3) ◽  
pp. 363-385 ◽  
Author(s):  
Renxin Zhong ◽  
Changjia Chen ◽  
Andy H. F. Chow ◽  
Tianlu Pan ◽  
Fangfang Yuan ◽  
...  
Keyword(s):  
Automatic Calibration ◽  
Fundamental Diagram ◽  
Freeway Traffic ◽  
Traffic Models ◽  
First Order

Linear Parameter Varying Identification of Freeway Traffic Models

2011 ◽  
Vol 19 (1) ◽  
pp. 31-45 ◽  
Author(s):  
Tamás Luspay ◽  
Balázs Kulcsar ◽  
Jan-Willem van Wingerden ◽  
Michel Verhaegen ◽  
József Bokor
Keyword(s):  
Linear Parameter ◽  
Freeway Traffic ◽  
Linear Parameter Varying ◽  
Traffic Models ◽  
Parameter Varying

Robust Identification of Pressure Oscillations in the Combustion Chamber of a Heavy-Duty Turbine

2017 ◽  
Author(s):  
Juliano Pierezan ◽  
Gabriel Maidl ◽  
Eduardo Yamao ◽  
João Paulo Silva Gonçalves ◽  
Flávio Chiesa ◽  
...  
Keyword(s):  
Combustion Chamber ◽  
Heavy Duty ◽  
Pressure Oscillations ◽  
Robust Identification

Using Traffic Disturbance Metrics to Estimate and Predict Freeway Traffic Breakdown and Safety Events

2021 ◽  
pp. 036119812110124
Author(s):  
Leila Azizi ◽  
Mohammed Hadi
Keyword(s):  
Traffic Safety ◽  
Management Strategies ◽  
Connected Vehicles ◽  
Freeway Traffic ◽  
Transportation Management ◽  
Time To Collision ◽  
Decision Tools ◽  
Traffic State ◽  
Traffic Performance ◽  
Traffic Disturbance

The introduction of connected vehicles, connected and automated vehicles, and advanced infrastructure sensors will allow the collection of microscopic metrics that can be used for better estimation and prediction of traffic performance. This study examines the use of disturbance metrics in combination with the macroscopic metrics usually used for the estimation of traffic safety and mobility. The disturbance metrics used are the number of oscillations and a measure of disturbance durations in the time exposed time to collision. The study investigates using the disturbance metrics in data clustering for better off-line categorization of traffic states. In addition, the study uses machine-learning based classifiers for the recognition and prediction of the traffic state and safety in real-time operations. The study also demonstrates that the disturbance metrics investigated are significantly related to crashes. Thus, this study recommends the use of these metrics as part of decision tools that support the activation of transportation management strategies to reduce the probability of traffic breakdown, ease traffic disturbances, and reduce the probability of crashes.

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