scholarly journals Surrogate-assisted cooperative signal optimization for large-scale traffic networks

2021 ◽  
pp. 107542
Author(s):  
Yongsheng Liang ◽  
Zhigang Ren ◽  
Lin Wang ◽  
Hanqing Liu ◽  
Wenhao Du
Keyword(s):  
Large Scale ◽  
Traffic Networks ◽  
Signal Optimization

Benchmark Comparison of Methods for Approximate Neighborhood Estimation of Road Segments in Large-Scale Traffic Networks

2020 ◽  
Author(s):  
Athanasios I. Salamanis ◽  
George A. Gravvanis ◽  
Christos K. Filelis-Papadopoulos ◽  
Dimitrios Michail
Keyword(s):  
Large Scale ◽  
Traffic Networks ◽  
Comparison Of Methods ◽  
Road Segments

SIMPLIFIED DYNAMICS AND OPTIMIZATION OF LARGE SCALE TRAFFIC NETWORKS

2004 ◽  
Vol 14 (04) ◽  
pp. 579-601 ◽  
Author(s):  
MICHAEL HERTY ◽  
AXEL KLAR
Keyword(s):  
Differential Equations ◽  
Traffic Flow ◽  
Large Scale ◽  
Dynamic Models ◽  
Optimization Problems ◽  
Network Models ◽  
Traffic Networks ◽  
Algebraic Models ◽  
Simplified Models ◽  
Large Scale Networks

Simplified dynamic models for traffic flow on networks are derived from network models based on partial differential equations. We obtain simplified models of different complexity like models based on ordinary differential equations or algebraic models. Optimization problems for all models are investigated. Analytical and numerical properties are studied and comparisons are given for simple traffic situations. Finally, the simplified models are used to optimize large scale networks.

Abnormal dynamics induced by congestion effect in cascading failures

2019 ◽  
Vol 33 (02) ◽  
pp. 1950001
Author(s):  
Dayong Wang ◽  
Guozhu Jia ◽  
Hengshan Zong ◽  
Wei He
Keyword(s):  
Large Scale ◽  
Modern Society ◽  
Power Grids ◽  
Cascading Failures ◽  
Traffic Networks ◽  
Removal Capacity ◽  
Large Scale Network ◽  
Congestion Effect ◽  
Scale Network ◽  
The Relationship

Robustness of infrastructure networks is essential for our modern society. Cascading failures are the cause of most large-scale network outages. We study the cascading failure of networks due to overload, using the betweenness centrality of an edge as the measure of its initial load. Taking into account the congestion effect of a slightly overloading edge, we define two capacities (the basic capacity and the removal capacity) of every edge and give three possible states (the free state, the congestion state, and the removal state) of every edge according to its current load. We propose a new method to dynamically adjust two capacities of the slightly overloading edge and study the dynamical features of cascading propagation induced by removing the edge with the highest load in two artificial networks, two traffic networks, and two power grids. We mainly focus on the relationship between the capacity parameters and two robust metrics. By simulations, we find two interesting and counterintuitive results, i.e. enhancing the basic capacity of every edge may weaken the network robustness, and fixing the basic capacity of every edge, simply improving the removal capacity of every edge sometimes makes the whole network more invulnerable. These findings show that investing more maintenance resources to alleviate flow congestion is not always better to avoid the cascading propagation, which is similar to Braess’s paradox in traffic networks.

An Efficient Method for Accelerating Training of Short-Term Traffic Prediction Models in Large-Scale Traffic Networks

2020 ◽  
Author(s):  
Athanasios I. Salamanis ◽  
George A. Gravvanis ◽  
Christos K. Filelis-Papadopoulos ◽  
Dimitrios Tzovaras
Keyword(s):  
Efficient Method ◽  
Large Scale ◽  
Prediction Models ◽  
Traffic Prediction ◽  
Traffic Networks ◽  
Short Term

Novel road classifications for large scale traffic networks

2010 ◽  
Author(s):  
Werner Toplak ◽  
Hannes Koller ◽  
Melitta Dragaschnig ◽  
Dietmar Bauer ◽  
Johannes Asamer
Keyword(s):  
Large Scale ◽  
Traffic Networks
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