Abstract
There is a growing interest in finding a global optimal path in transportation networks particularly when the network suffers from unexpected disturbance. This paper studies the problem of finding a global optimal path to guarantee a given probability of arriving on time in a network with uncertainty, in which the travel time is stochastic instead of deterministic. Traditional path finding methods based on least expected travel time cannot capture the network user’s risk-taking behaviors in path finding. To overcome such limitation, the reliable path finding algorithms have been proposed but the convergence of global optimum is seldom addressed in the literature. This paper integrates the K-shortest path algorithm into Backtracking method to propose a new path finding algorithm under uncertainty. The global optimum of the proposed method can be guaranteed. Numerical examples are conducted to demonstrate the correctness and efficiency of the proposed algorithm.
Highlights
Finding a global optimal path in networks is a practical problem among communication and transportation
This paper studies the problem of finding a global optimal path to guarantee a given probability of arriving on time in a network with uncertainty, in which the travel time is stochastic instead of deterministic
This paper proposed a global optimal algorithm for travel time reliability-based path finding problem using Backtracking method
Summary
Finding a global optimal path in networks is a practical problem among communication and transportation. It has attracted much attention from researchers in numerous fields owing to its broad applications. Traffic condition is affected by many uncertain factors and the travel time is stochastic. Under such circumstances, the network users may face the risk of being late. The travel time reliability-based path finding problem has attracted much attention by some recently proposed algorithms. The problem of finding a global optimal path based on time reliability is studied in this paper
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