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Empirical evaluation of deep learning-based travel time prediction

conference contribution
posted on 2023-05-23, 14:32 authored by Wang, M, Li, W, Kong, Y, Quan BaiQuan Bai
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.

History

Publication title

PKAW 2019 Conference Proceedings

Editors

K Ohara and Q Bai

Pagination

54-65

ISSN

0302-9743

Department/School

School of Information and Communication Technology

Publisher

Springer Nature

Place of publication

Switzerland

Event title

2019 Pacific Rim Knowledge Acquisition Workshop (PKAW 2019)

Event Venue

Cuvu, Fiji

Date of Event (Start Date)

2019-08-26

Date of Event (End Date)

2019-08-27

Rights statement

Copyright 2019 Springer Nature Switzerland AG

Repository Status

  • Restricted

Socio-economic Objectives

Information systems, technologies and services not elsewhere classified

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