Real-time traffic management under emergency evacuation based on dynamic traffic assignment

2008 ◽  
Author(s):  
Yue-ming Chen ◽  
De-yun Xiao
Keyword(s):  
Real Time ◽  
Traffic Management ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Emergency Evacuation ◽  
Dynamic Traffic ◽  
Real Time Traffic

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.

Real-Time Traffic Flow Pattern Matching to Improve Predictive Performance of Online Simulation-Based Dynamic Traffic Assignment

2019 ◽  
Vol 2673 (9) ◽  
pp. 23-33
Author(s):  
Haleh Ale-Ahmad ◽  
Hani S. Mahmassani ◽  
Eunhye Kim ◽  
Marija Ostojic
Keyword(s):  
Real Time ◽  
Pattern Matching ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Predictive Performance ◽  
Dynamic Traffic ◽  
Time Pattern ◽  
Real Time Traffic ◽  
Simulation Based ◽  
Soft Matching

In real-time simulation-based dynamic traffic assignment, selection of the most suitable demand from the library of demands calibrated offline improves the accuracy of the prediction. In the era of data explosion, relying on contextual and rule-based pattern matching logic does not seem sufficient. A rolling horizon scheme for real-time pattern matching is introduced using two pattern matching frameworks. The hard matching algorithm chooses the closest pattern at each evaluation interval, while soft matching calculates the probability of being a match for each pattern. To make sure the pattern switch does not happen because of short-lived interruptions in traffic conditions, a persistency index is introduced. The results show that the number of switches in hard matching is bigger than soft matching but the error of real-time matching for both cases is low. The importance of the results is twofold: First, any observation that is not similar to only one pattern in the library can be mimicked using multiple available patterns; second, more advanced algorithms can match the patterns existing in the library, without any contextual logics for pattern matching.

Integration of Dynamic Traffic Assignment with Real-Time Traffic Adaptive Control System

1998 ◽  
Vol 1644 (1) ◽  
pp. 150-156 ◽  
Author(s):  
Nathan H. Gartner ◽  
Chronis Stamatiadis
Keyword(s):  
Adaptive Control ◽  
Control System ◽  
Real Time ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Transportation Systems ◽  
Signal Control ◽  
Adaptive Control System ◽  
Dynamic Traffic ◽  
Real Time Traffic

Intelligent transportation systems (ITS) are being designed to provide real-time control and route guidance to motorists to optimize traffic network performance. Current research and development efforts consist of a dynamic traffic assignment capability that can predict future traffic conditions and a real-time traffic adaptive control system (RT-TRACS) for generation of signal control strategies. Although these models are intimately connected, so far they have developed independently of one another. A framework is presented here for integrating the two models into a combined system with a practical approach for realizing it. First the static case involving the interaction between travelers (demand) and transportation facilities (supply) under recurrent conditions is discussed. This model is applicable in the design and planning of transportation systems management actions. The framework is then extended to the quasi-dynamic and the dynamic cases, which involve incorporation of advanced ITS technologies in the form of advanced traffic management systems and advanced traveler information systems. An innovative application of this framework to advanced traffic-adaptive signal control is presented using the hierarchic structure of RT-TRACS.

scholarly journals Distributed Dynamic Traffic Modeling and Implementation Oriented Different Levels of Induced Travelers

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Yan Liu ◽  
Yao Yu
Keyword(s):  
Traffic Management ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Distributed Model ◽  
Traffic Network ◽  
Dynamic Traffic ◽  
Assignment Method ◽  
Proposed Model ◽  
Network Operation ◽  
Different Levels

In order to respond to the variable state of traffic network in time, a distributed dynamic traffic assignment strategy is proposed which can improve the intelligent traffic management. The proposed dynamic assignment method is based on utility theory and is oriented to different levels of induced users. A distributed model based on the marginal utility is developed which combines the advantages of both decentralized paradigm and traveler preference, so as to provide efficient and robust dynamic traffic assignment solutions under uncertain network conditions. Then, the solution algorithm including subroute update and subroute calculation is proposed. To testify the effectiveness of the proposed model in optimizing traffic network operation and minimizing traveler’s cost on different induced levels, a sequence numerical experiment is conducted. In the experiment, there are two test environments: one is in different network load conditions and the other is in different deployment coverage of local agents. The numerical results show that the proposed model not only can improve the running efficiency of road network but also can significantly decrease the average travel time.

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