Monash University

Embargoed and Restricted Access

Reason: Under embargo until May 2023. After this date a copy can be supplied under Section 51(2) of the Australian Copyright Act 1968 by submitting a document delivery request through your library

Short-term traffic state estimation and prediction based on spatiotemporal neural networks

posted on 2022-05-01, 08:44 authored by Bo Wang
Intelligent transportation systems use forecasted traffic states as basic information. This study aims to improve the performance of short-term traffic prediction using neural networks (NN). As a data-driven approach, NN models are heavily impacted by data samples. Under general data availability settings, we develop enhanced approaches in input data, model structure, and forecasting output. For data shortage and data overstock settings, several novel approaches regarding transfer learning, feature visualization, domain shifting are proposed and discussed. Overall, we present a more systematic and general methodology framework in short-term traffic prediction that positively impacts transportation research and industry.


Campus location


Principal supervisor

Hai Vu

Additional supervisor 1

Inhi Kim

Additional supervisor 2

Chen Cai

Year of Award


Department, School or Centre

Civil Engineering


Doctor of Philosophy

Degree Type



Faculty of Engineering