scholarly journals Feedback control solutions to network level user-equilibrium real-time dynamic traffic assignment problems

2002 ◽  
Author(s):  
P. Kachroo ◽  
K. Ozbay
Keyword(s):  
Feedback Control ◽  
Real Time ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
User Equilibrium ◽  
Dynamic Traffic ◽  
Assignment Problems ◽  
Time Dynamic

Fuzzy Feedback Control for Real-Time Dynamic Traffic Routing: User Equilibrium Model Formulations and Controller Design

1996 ◽  
Vol 1556 (1) ◽  
pp. 137-146 ◽  
Author(s):  
Pushkin Kachroo ◽  
Kaan Özbay
Keyword(s):  
Feedback Control ◽  
Real Time ◽  
Controller Design ◽  
User Equilibrium ◽  
System Dynamics Model ◽  
Dynamics Model ◽  
Dynamic Traffic ◽  
Control Laws ◽  
Time Dynamic ◽  
Traffic Routing

The formulation of a system dynamics model for the dynamic traffic routing (DTR) problem is addressed, specifically for the application of real-time feedback control. Also addressed is the design of fuzzy feedback control laws for this problem. Fuzzy feedback control is suitable for solving the DTR problem, which is nonlinear and time varying and contains uncertainties. To illustrate the applicability of fuzzy logic in the design of feedback control for DTR, a simple software simulation was conducted that provided encouraging results.

scholarly journals Improving Scalability of Generic Online Calibration for Real-Time Dynamic Traffic Assignment Systems

2018 ◽  
Vol 2672 (48) ◽  
pp. 79-92 ◽  
Author(s):  
A. Arun Prakash ◽  
Ravi Seshadri ◽  
Constantinos Antoniou ◽  
Francisco C. Pereira ◽  
Moshe Ben-Akiva
Keyword(s):  
Dimensionality Reduction ◽  
Real Time ◽  
Traffic Management ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Specific Class ◽  
Dynamic Traffic ◽  
Widespread Application ◽  
Time Dynamic ◽  
Online Calibration

Flexible calibration of dynamic traffic assignment (DTA) systems in real time has important applications in effective traffic management. However, the existing approaches are either limited to small networks or to a specific class of parameters. In this light, this study presents a framework to systematically reduce the dimension of the generic online calibration problem, making it more scalable. Specifically, a state–space formulation of the problem in the reduced dimension space is proposed. Following this the problem is solved using the constrained extended Kalman filter, which is made tractable because of the low dimensionality of the formulated problem. The effectiveness of the proposed approach is demonstrated using a real-world network leading to better state estimation by 13% and better state predictions by 11%—with a 50 fold dimensionality reduction. Insights into choosing the right degree of dimensionality reduction are also discussed. This work has the potential for a more widespread application of real-time DTA systems in practice.

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