A multi-agent learning approach for online calibration and consistency checking of real-time traffic network management systems

2016 ◽  
Vol 5 (3) ◽  
pp. 364-384
Author(s):  
Hossein Hashemi ◽  
Khaled F. Abdelghany ◽  
Ahmed F. Abdelghany
Keyword(s):  
Network Management ◽  
Real Time ◽  
Management Systems ◽  
Learning Approach ◽  
Traffic Network ◽  
Consistency Checking ◽  
Online Calibration ◽  
Real Time Traffic ◽  
Agent Learning ◽  
Multi Agent

Integrated Method for Online Calibration of Real-Time Traffic Network Management Systems

2015 ◽  
Vol 2528 (1) ◽  
pp. 106-115 ◽  
Author(s):  
Hossein Hashemi ◽  
Khaled Abdelghany
Keyword(s):  
Network Management ◽  
Real Time ◽  
Simulation Models ◽  
Calibration Method ◽  
Propagation Model ◽  
Traffic Network ◽  
Operational Conditions ◽  
Online Calibration ◽  
Integrated Method ◽  
Real Time Traffic

This paper presents an integrated method for online calibration of realtime traffic network simulation models. The method integrates a time-dependent demand adjustment module and a link-based traffic flow propagation model calibration module. These modules use available realtime traffic observations to minimize inconsistency between the model estimation results and real-world observations. The modules are integrated into a real-time traffic network management system that was developed for the US-75 corridor in Dallas, Texas. Results illustrate that the online calibration method is effective in enhancing the model's consistency in the different operational conditions.

Toward an Autonomic Architecture for Real-Time Traffic Network Management

2012 ◽  
Vol 16 (2) ◽  
pp. 45-59 ◽  
Author(s):  
Hamideh Etemadnia ◽  
Khaled Abdelghany ◽  
Salim Hariri
Keyword(s):  
Network Management ◽  
Real Time ◽  
Traffic Network ◽  
Real Time Traffic

MAS Learning Based on Bayesian Learning Method

2010 ◽  
Vol 20-23 ◽  
pp. 1292-1298
Author(s):  
De Jia Shi ◽  
Zhi Qiang Liu ◽  
Jing He
Keyword(s):  
Learning Strategies ◽  
Bayesian Learning ◽  
Free Text ◽  
Learning Approach ◽  
Learning Method ◽  
Multi Agent System ◽  
Agent System ◽  
Agent Learning ◽  
Multi Agent ◽  
Keyword Searches

Mulit-agent system[MAS] research on learning has been in the area of negotiation, and learning strategies of other agents.This paper presents an agent learning approach in multi-agent system based on Bayesian learning, it researches to develop agents that learn free-text queries and keyword searches in MAS. The MAS learns to identify an appropriate agent to answer free-text and natural language queries as well as keyword searches submitted by users. The paper describes how Bayesian learning is implemented in MAS, and analyzes the effectiveness of MAS learning based on the Bayesian learning approach by analyzing the accuracy and degree of learning.

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