Trajectory Length Prediction for Intelligent Traffic Signaling: A Data-Driven Approach

Gan, S, Liang, S, Li, K et al. (2 more authors) (2018) Trajectory Length Prediction for Intelligent Traffic Signaling: A Data-Driven Approach. IEEE Transactions on Intelligent Transportation Systems, 19 (2). pp. 426-435. ISSN 1524-9050

Abstract

Metadata

Authors/Creators:
  • Gan, S
  • Liang, S
  • Li, K
  • Deng, J
  • Cheng, T
Copyright, Publisher and Additional Information: © 2017 IEEE. This is an author produced version of a paper published in IEEE Transactions on Intelligent Transportation Systems. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: Trajectory prediction; data-driven; fuzzy c-means (FCM); artificial neural networks (ANN); intelligent traffic signalling system (ITSS)
Dates:
  • Published: February 2018
  • Accepted: 14 April 2017
  • Published (online): 2 June 2017
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 26 Nov 2018 11:57
Last Modified: 10 Mar 2019 05:46
Status: Published
Publisher: Institute of Electrical and Electronics Engineers
Identification Number: https://doi.org/10.1109/TITS.2017.2700209

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