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Empirical evaluation of deep learning-based travel time prediction

Citation

Wang, M and Li, W and Kong, Y and Bai, Q, Empirical evaluation of deep learning-based travel time prediction, PKAW 2019 Conference Proceedings, 26-27 August 2019, Cuvu, Fiji, pp. 54-65. ISSN 0302-9743 (2019) [Refereed Conference Paper]

Copyright Statement

Copyright 2019 Springer Nature Switzerland AG

DOI: doi:10.1007/978-3-030-30639-7_6

Abstract

Travel time prediction is critical in the urban traffic management system. Accurate travel time prediction can assist better city planning and reduce carbon footprints. In this paper, we conducted an empirical work on deep learning-based travel time prediction. The objective of this study is to compare the prediction performance of different machine learning methods. Meanwhile, through the comparison, a neural network module with high prediction accuracy can be offered for alleviating traffic congestion. In addition, to eliminate the influence of nonlinear external factors, a variety of extrinsic data with abrupt properties will be acquired in real time and become part of the research considerations.

Item Details

Item Type:Refereed Conference Paper
Keywords:deep learning, traffic prediction. intelligent transport systems, travel time prediction
Research Division:Information and Computing Sciences
Research Group:Machine learning
Research Field:Neural networks
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Bai, Q (Dr Quan Bai)
ID Code:138234
Year Published:2019
Deposited By:Information and Communication Technology
Deposited On:2020-03-27
Last Modified:2020-05-21
Downloads:0

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