Publication Type

Journal Article

Version

publishedVersion

Publication Date

8-2021

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.

Keywords

Reliability, Transportation, Path planning, Planning, Gaussian processes, Standards, Covariance matrices, Reliable shortest path (RSP), mean-std minimization, Gaussian process path planning (GP3), a priori path, stochastic on time arrival (SOTA), Lagrangian relaxation

Discipline

OS and Networks | Transportation

Research Areas

Intelligent Systems and Optimization

Publication

IEEE Transactions on Intelligent Transportation Systems

Volume

23

Issue

8

First Page

11575

Last Page

11590

ISSN

1524-9050

Identifier

10.1109/TITS.2021.3105415

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TITS.2021.3105415

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