Freight vehicle travel time prediction using sparse Gaussian processes regression with trajectory data

Abstract

Travel time prediction is important for freight transportation companies. Accurate travel time prediction can help these companies make better planning and task scheduling. For several reasons, most companies are not able to obtain traffic flow data from traffic management authorities, but a large amount of trajectory data were collected everyday which has not been fully utilised. In this study, we aim to fill this gap and performed travel time prediction for freight vehicles at individual level using sparse Gaussian processes regression (SGPR) models with trajectory data. The results show that the prediction performance can be gradually improved by adding more mean speed estimates of traveled distance from the first 5 min as the real-time information. The overall performances of SGPR models are very similar to full GP, supported vector regression (SVR) and artificial neural network (ANN) models. The computational complexity of SGPR models is O(mn2), and it does not require lengthy model fitting process as SVR and ANN. This makes GP models more practicable for real-world practice in large-scale transportation data analyses.