GP3: Gaussian process path planning for reliable shortest path in transportation networks

• Main Authors: GUO, Hongliang, HOU, Xuejie, CAO, Zhiguang, ZHANG, Jie
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: 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; OS and Networks
• Online Access: https://ink.library.smu.edu.sg/sis_research/8126; https://ink.library.smu.edu.sg/context/sis_research/article/9129/viewcontent/GP3_Gaussian_Process_Path_Planning_for_Reliable_Shortest_Path_in_Transportation_Networks.pdf
• Institution: Singapore Management University

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.