Stochastic Simulation on System Reliability and Component Probabilistic Importance of Road Network

Because of the combination explosion problem, it is difficult to use probability analytical method to calculate the system reliability of large networks. The paper develops a stochastic simulation (Monte Carlo-based) method to study the system reliability and component probabilistic importance of th...

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Bibliographic Details
Published in:Mathematical problems in engineering 2015-01, Vol.2015 (2015), p.1-5
Main Authors: Gu, Ziyuan, Yan, Yadan, Wang, Dongwei, Zhao, Xiangyu
Format: Article
Language:English
Subjects:
Automobile driving
Component reliability
Computer simulation
Economic models
Engineering
Links
Mathematical analysis
Mathematical models
Network reliability
Networks
Object oriented programming
Object-oriented languages
Parking violations
Probability theory
Programming languages
Random variables
Reliability analysis
Roads
Roads & highways
Statistical analysis
Stochasticity
System reliability
Toll roads
Traffic
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Summary:Because of the combination explosion problem, it is difficult to use probability analytical method to calculate the system reliability of large networks. The paper develops a stochastic simulation (Monte Carlo-based) method to study the system reliability and component probabilistic importance of the road network. The proposed method considers the characteristics of the practical road network as follows: both link (roadway segment) and node (intersection) components are emphasized in the road network; the reliability for a link or node component may be at the in-between state; namely, its reliability value is between 0 and 1. The method is then implemented using the object-oriented programming language C++ and integrated into a RARN-MGG (reliability analysis of road network using Monte Carlo, GIS, and grid) system. Finally, two numerical examples based on a simple road network and a large real road network, respectively, are carried out to characterize the feasibility and to demonstrate the strength of the stochastic simulation method.
ISSN:1024-123X
1563-5147
DOI:10.1155/2015/456485