Paper Info
Title
Hybrid Solution Combining Kalman Filtering with Takagi-Sugeno Fuzzy Inference System for Online Car-Following Model Calibration.
Abstract
Nowadays, the intelligent transportation concept has become one of the most important research fields. All of us depend on mobility, even when we talk about people, provide services, or move goods. Researchers have tried to create and test different transportation models that can optimize traffic flow through road networks and, implicitly, reduce travel times. To validate these new models, the necessity of having a calibration process defined has emerged. Calibration is mandatory in the modeling process because it ensures the achievement of a model closer to the real system. The purpose of this paper is to propose a new multidisciplinary approach combining microscopic traffic modeling theory with intelligent control systems concepts like fuzzy inference in the traffic model calibration. The chosen Takagi-Sugeno fuzzy inference system proves its adaptive capacity for real-time systems. This concept will be applied to the specific microscopic car-following model parameters in combination with a Kalman filter. The results will demonstrate how the microscopic traffic model parameters can adapt based on real data to prove the model validity.
Year
DOI
Venue
2020
10.3390/s20195539
SENSORS
Keywords
DocType
Volume
fuzzy inference,calibration,car-following,Takagi&#8211,Sugeno,Kalman filter,microscopic traffic model,continuous-time model
Journal
20
Issue
ISSN
Citations 
19
1424-8220
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Mădălin-Dorin Pop100.34
Octavian Prostean22921.20
Tudor-Mihai David300.34
Gabriela Prostean499.61