scholarly journals Adaptive Traffic Control for Large-Scale Dynamic Traffic Assignment Applications

2011 ◽  
Vol 2263 (1) ◽  
pp. 103-112 ◽  
Author(s):  
Alexander Paz ◽  
Yi-Chang Chiu
Keyword(s):  
Traffic Control ◽  
Large Scale ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Dynamic Traffic ◽  
Adaptive Traffic Control

Game-Theoretic Formulations of Interaction Between Dynamic Traffic Control and Dynamic Traffic Assignment

1998 ◽  
Vol 1617 (1) ◽  
pp. 179-188 ◽  
Author(s):  
Owen Chen ◽  
Moshe Ben-Akiva
Keyword(s):  
Dynamic System ◽  
Traffic Control ◽  
Assignment Problem ◽  
Traffic Assignment ◽  
Stackelberg Game ◽  
Dynamic Traffic Assignment ◽  
Noncooperative Game ◽  
Dynamic Traffic ◽  
System Optimal ◽  
Combined Control

The dynamic traffic control problem and the dynamic traffic assignment problem are integrated as a noncooperative game between a traffic authority and highway users. The objective of the combined control-assignment problem is to find a mutually consistent dynamic system-optimal signal setting and dynamic user-optimal traffic flow. The combined control-assignment problem is first formulated as a one-level Cournot game: the traffic authority and the users choose their strategies simultaneously. The combined control-assignment problem is subsequently formulated as a bi-level Stackelberg game. The traffic authority is the leader; it determines the signal settings in anticipation of the users’ reactions. The users are followers who choose their routes after the signal settings have been determined. Finally, the system-optimal control-assignment problem is formulated as a Monopoly game. The sole player—the traffic authority—determines both signal settings and traffic flows to achieve a dynamic system-optimal solution. A numerical example is provided to illustrate the equilibria of the games.

scholarly journals Improved Calibration Method for Dynamic Traffic Assignment Models: Constrained Extended Kalman Filter

2017 ◽  
Vol 2667 (1) ◽  
pp. 142-153 ◽  
Author(s):  
Haizheng Zhang ◽  
Ravi Seshadri ◽  
A. Arun Prakash ◽  
Francisco C. Pereira ◽  
Constantinos Antoniou ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Large Scale ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Generalized Least Squares ◽  
Descent Method ◽  
Coordinate Descent ◽  
Model Parameters ◽  
Dynamic Traffic

The calibration of dynamic traffic assignment (DTA) models involves the estimation of model parameters to best replicate real-world measurements. Good calibration is essential to estimate and predict accurately traffic states, which are crucial for traffic management applications to alleviate congestion. A widely used approach to calibrate simulation-based DTA models is the extended Kalman filter (EKF). The EKF assumes that the DTA model parameters are unconstrained, although they are in fact constrained; for instance, origin–destination (O-D) flows are nonnegative. This assumption is typically not problematic for small- and medium-scale networks in which the EKF has been successfully applied. However, in large-scale networks (which typically contain numbers of O-D pairs with small magnitudes of flow), the estimates may severely violate constraints. In consequence, simply truncating the infeasible estimates may result in the divergence of EKF, leading to extremely poor state estimations and predictions. To address this issue, a constrained EKF (CEKF) approach is presented; it imposes constraints on the posterior distribution of the state estimators to obtain the maximum a posteriori (MAP) estimates that are feasible. The MAP estimates are obtained with a heuristic followed by the coordinate descent method. The procedure determines the optimum and are computationally faster by 31.5% over coordinate descent and by 94.9% over the interior point method. Experiments on the Singapore expressway network indicated that the CEKF significantly improved model accuracy and outperformed the traditional EKF (up to 78.17%) and generalized least squares (up to 17.13%) approaches in state estimation and prediction.

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