GP3: Gaussian Process Path Planning for Reliable Shortest Path in Transportation Networks

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GP3: Gaussian Process Path Planning for Reliable Shortest Path in Transportation Networks
Title:
GP3: Gaussian Process Path Planning for Reliable Shortest Path in Transportation Networks
Journal Title:
IEEE Transactions on Intelligent Transportation Systems
Publication Date:
25 August 2021
Citation:
Guo, H., Hou, X., Cao, Z., & Zhang, J. (2022). GP3: Gaussian Process Path Planning for Reliable Shortest Path in Transportation Networks. IEEE Transactions on Intelligent Transportation Systems, 23(8), 11575–11590. https://doi.org/10.1109/tits.2021.3105415
Abstract:
This paper investigates the reliable shortest path (RSP) problem in Gaussian process (GP) regulated transportation networks. Specifically, the RSP problem that we are targeting at is to minimize the (weighted) linear combination of mean and standard deviation of the path’s travel time. With the reasonable assumption that the travel times of the underlying transportation network follow a multi-variate Gaussian distribution, we propose a Gaussian process path planning (GP3) algorithm to calculate the a priori optimal path as the RSP solution. With a series of equivalent RSP problem transformations, we are able to reach a polynomial time complexity algorithm with guaranteed solution accuracy. Extensive experimental results over various sizes of realistic transportation networks demonstrate the superior performance of GP3 over the state-of-the-art algorithms.
License type:
Publisher Copyright
Funding Info:
This work was supported in part by the National Natural Science Foundation of China under Grant 61803104
Description:
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
1524-9050
1558-0016
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