Real-Time Dynamic Traffic Assignment for Route Guidance: Comparison of Global Predictive vs. Local Reactive Strategies Under Stochastic Demands

1997 ◽  
Vol 30 (8) ◽  
pp. 543-548 ◽  
Author(s):  
Hani S. Mahmassani ◽  
Yaser E. Hawas
Keyword(s):  
Real Time ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Route Guidance ◽  
Dynamic Traffic ◽  
Time Dynamic ◽  
Stochastic Demands

Routing Profile Updating Strategies for Online Hybrid Dynamic Traffic Assignment Operation

2003 ◽  
Vol 1857 (1) ◽  
pp. 39-47 ◽  
Author(s):  
Yi-Chang Chiu ◽  
Hani S. Mahmassani
Keyword(s):  
Real Time ◽  
System Performance ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
A Priori ◽  
Traffic Network ◽  
Route Guidance ◽  
Dynamic Traffic ◽  
Simulation Experiments ◽  
Principal Mechanism

An online routing profile updating automaton (ORPUA) approach is introduced as a principal mechanism for operating an online hybrid dynamic traffic assignment (DTA) system for real-time route guidance in a traffic network. The hybrid DTA approach integrates the centralized and the decentralized DTA frameworks by partitioning the set of guided users into two classes according to an initial routing profile (IRP). One class receives the centralized DTA guidance, while the other follows the decentralized DTA routing. ORPUA takes the a priori IRP and updates the guidance supplied to vehicles in a real-time fashion according to the unfolding network conditions and relative performance of the two classes of users. It does not anticipate the future network conditions; instead, it reacts to them and optimizes the overall system performance by improving the performance of the underperforming class of vehicles. Simulation experiments illustrate ORPUA’s potential in maintaining desirable system performance and robustness in most of the demand-supply scenarios considered.

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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