Abstract:
This thesis approaches the road traffic modeling process at the microscopic level,
focusing on the car-following models. A review of the recent trends in this field is
provided by the state of the art chapter. This research emphasizes the disadvantage
of the standard car-following model consisting of neglection of the behavior of
vehicles moving on the adjacent traffic lanes. This study proposes a refinement
process of this standard car-following model considering the behavior of the
vehicles moving on the adjacent road traffic lanes in the follower vehicle
acceleration control. Traffic lanes are modeled as nodes in a Markov chain, and the
lane choice probabilities computation employ the Bayesian reasoning concept. A
fault detection based on parity equations is performed to detect computational
faults introduced by the refinement process. This research also brings novelties to
the calibration process by proposing a hybrid online calibration method that
combines the concept of Kalman filtering with the Takagi-Sugeno fuzzy inference
system. This calibration method proved its utility for both standard and refined
car-following models. All these new approaches have been validated through
simulation experiments done in Simulink, part of MATLAB R2020a (MathWorks,
Natick, MA, USA) based on real traffic data received from Timișoara City Hall -
General Directorate of Roads, Bridges, Parking, and Utility Networks - Traffic
Monitoring Office, Timișoara, Romania.
