Abstract

After natural disaster, especially for large-scale disasters and affected areas, vast relief materials are often needed. In the meantime, the traffic networks are always of uncertainty because of the disaster. In this paper, we assume that the edges in the network are either connected or blocked, and the connection probability of each edge is known. In order to ensure the arrival of these supplies at the affected areas, it is important to select a reliable path. A reliable path selection model is formulated, and two algorithms for solving this model are presented. Then, adjustable reliable path selection model is proposed when the edge of the selected reliable path is broken. And the corresponding algorithms are shown to be efficient both theoretically and numerically.

Highlights

  • After natural disaster, especially for large-scale disasters, such as earthquake, the roads are destroyed which makes the traffic network uncertain

  • Vast relief materials need to be transported to the affected areas, and a reliable path may ensure the arrival of these supplies

  • The existing reliability studies of road networks are mainly limited to three aspects: connectivity reliability, travel time reliability and capacity reliability

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Summary

Introduction

Especially for large-scale disasters, such as earthquake, the roads are destroyed which makes the traffic network uncertain. A special case is the terminal reliability which concerns the existence of a path between a specific origin-destination pair [4] Another measure of network reliability is travel time reliability or the coefficient of variation of travel time [5]. This is concerned with the probability that a trip between a given OD pair can be made successful within a specified interval of time. Because the traffic network is uncertain in disasters and the edge may become unconnected, we choose the reliable path to ensure the arrival of the materials.

Reliable Path Selection Model
Algorithms for Reliable Path Selection Model
Adjustable Reliable Path Selection Model and Algorithm
Conclusions
Full Text
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