Laboratory Evaluation of Real-Time Dynamic Traffic Assignment Systems

1999 ◽  
Vol 14 (4) ◽  
pp. 281-298 ◽  
Author(s):  
Shaw-Pin Miaou ◽  
Mike S. Summers ◽  
Henry C. Lieu
Keyword(s):  
Real Time ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Laboratory Evaluation ◽  
Dynamic Traffic ◽  
Time Dynamic

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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