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.
GP3: Gaussian Process Path Planning for Reliable Shortest Path in Transportation Networks
IEEE Transactions on Intelligent Transportation Systems ; 23 , 8 ; 11575-11590
2022-08-01
2934020 byte
Article (Journal)
Electronic Resource
English
Reliable Shortest Path Problems in Stochastic Time-Dependent Networks
Online Contents | 2014
|Reliable Shortest Path Problems in Stochastic Time-Dependent Networks
Taylor & Francis Verlag | 2014
|Impacts of Correlations on Reliable Shortest Path Finding
Transportation Research Record | 2013
|Shortest safe path planning for vehicles
IEEE | 2003
|Shortest Path Optimization Algorithm in Intelligent Transportation System
Trans Tech Publications | 2013
|