Dynamic traffic assignment integration with real-time ramp metering for large-scale network management

2017 ◽  
Author(s):  
Minha Lee ◽  
Zheng Zhu ◽  
Chenfeng Xiong ◽  
Lei Zhang
Keyword(s):  
Network Management ◽  
Real Time ◽  
Large Scale ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Ramp Metering ◽  
Dynamic Traffic ◽  
Large Scale Network ◽  
Scale Network

Integrated simulation-based dynamic traffic and transit assignment model for large-scale network

2020 ◽  
Vol 47 (8) ◽  
pp. 898-907 ◽  
Author(s):  
Islam Kamel ◽  
Amer Shalaby ◽  
Baher Abdulhai
Keyword(s):  
Large Scale ◽  
Traffic Assignment ◽  
Dynamic Traffic ◽  
Transit Assignment ◽  
Large Scale Network ◽  
Assignment Model ◽  
Morning Peak ◽  
Simulation Based ◽  
Scale Network ◽  
Assignment Models

Although the traffic and transit assignment processes are intertwined, the interactions between them are usually ignored in practice, especially for large-scale networks. In this paper, we build a simulation-based traffic and transit assignment model that preserves the interactions between the two assignment processes for the large-scale network of the Greater Toronto Area during the morning peak. This traffic assignment model is dynamic, user-equilibrium seeking, and includes surface transit routes. It utilizes the congested travel times, determined by the dynamic traffic assignment, rather than using predefined timetables. Unlike the static transit assignment models, the proposed transit model distinguishes between different intervals within the morning peak by using the accurate demand, transit schedule, and time-based road level-of-service. The traffic and transit assignment models are calibrated against actual field observations. The resulting dynamic model is suitable for testing different demand management strategies that impose dynamic changes on multiple modes simultaneously.

Subnet Broadcast Polling Algorithm of Network Management Based on SNMP

2013 ◽  
Vol 347-350 ◽  
pp. 2915-2918
Author(s):  
Mei Gen Huang ◽  
Zhi Lei Wang
Keyword(s):  
Network Management ◽  
Real Time ◽  
Management System ◽  
Large Scale ◽  
Current Data ◽  
Work Efficiency ◽  
Network Management System ◽  
Large Scale Network ◽  
Scale Network ◽  
Network Device

The current data collection of a network device in the network management system exists low real-time and long polling cycle, this paper proposes a subnet broadcast algorithm based on SNMP. The algorithm introduces the idea of Subnet division and broadcasting, when polling, the algorithm polled network devices by sending a SNMP data broadcast packet to each subnet, which reduced the polling packets, shortened the polling cycle and lightened the burden of management station, thus the proposed algorithm improves real-time and work efficiency for the large-scale network management system.

scholarly journals Self-regulation of stress-related large-scale brain network balance using real-time fMRI Neurofeedback

2021 ◽  
Author(s):  
Florian Krause ◽  
Nikolaos Kogias ◽  
Martin Krentz ◽  
Michael Luehrs ◽  
Rainer Goebel ◽  
...  
Keyword(s):  
Real Time ◽  
Executive Control ◽  
Large Scale ◽  
Acute Stress ◽  
Self Regulation ◽  
Real Life ◽  
Brain Network ◽  
Large Scale Network ◽  
Novel Approach ◽  
Scale Network

It has recently been shown that acute stress affects the allocation of neural resources between large-scale brain networks, and the balance between the executive control network and the salience network in particular. Maladaptation of this dynamic resource reallocation process is thought to play a major role in stress-related psychopathology, suggesting that stress resilience may be determined by the retained ability to adaptively reallocate neural resources between these two networks. Actively training this ability could hence be a potentially promising way to increase resilience in individuals at risk for developing stress-related symptomatology. Using real-time functional Magnetic Resonance Imaging, the current study investigated whether individuals can learn to self-regulate stress-related large-scale network balance. Participants were engaged in a bidirectional and implicit real-time fMRI neurofeedback paradigm in which they were intermittently provided with a visual representation of the difference signal between the average activation of the salience and executive control networks, and tasked with attempting to self-regulate this signal. Our results show that, given feedback about their performance over three training sessions, participants were able to (1) learn strategies to differentially control the balance between SN and ECN activation on demand, as well as (2) successfully transfer this newly learned skill to a situation where they (a) did not receive any feedback anymore, and (b) were exposed to an acute stressor in form of the prospect of a mild electric stimulation. The current study hence constitutes an important first successful demonstration of neurofeedback training based on stress-related large-scale network balance - a novel approach that has the potential to train control over the central response to stressors in real-life and could build the foundation for future clinical interventions that aim at increasing resilience.

scholarly journals An Efficient Approach For the Nodal Water Demand Estimation in Large-Scale Water Distribution Systems

2021 ◽  
Author(s):  
Shipeng Chu ◽  
Tuqiao Zhang ◽  
Xinhong Zhou ◽  
Tingchao Yu ◽  
Yu Shao
Keyword(s):  
Real Time ◽  
Water Demand ◽  
Water Distribution ◽  
Large Scale ◽  
Jacobian Matrix ◽  
Hessian Matrix ◽  
Demand Estimation ◽  
Matrix Inversion ◽  
Large Scale Network ◽  
Scale Network

Abstract Real-time modeling of the water distribution system (WDS) is a critical step for the control and operation of such systems. The nodal water demand as the most important time-varying parameter must be estimated in real-time. The computational burden of nodal water demand estimation is intensive, leading to inefficiency for the modeling of the large-scale network. The Jacobian matrix computation and Hessian matrix inversion are the processes that dominate the main computation time. To address this problem, an approach to shorten the computational time for the real-time demand estimation in the large-scale network is proposed. The approach can efficiently compute the Jacobian matrix based on solving a system of linear equations, and a Hessian matrix inversion method based on matrix partition and Iterative Woodbury-Matrix-Identity Formula is proposed. The developed approach is applied to a large-scale network, of which the number of nodal water demand is 12523, and the number of measurements ranging from 10 to 2000. Results show that the time consumptions of both Jacobian computation and Hessian matrix inversion are significantly shortened compared with the existing approach.

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