Use of LSTM for short-term and long-term travel time prediction

Irem Islek; Sule Gunduz Oguducu

Use of LSTM for short-term and long-term travel time prediction

Irem Islek
, Sule Gunduz Oguducu

Yapay Zeka ve Veri Mühendisliği Bölümü

Istanbul Technical University

Araştırma sonucu: Dergiye katkı › Konferans makalesi › bilirkişi

3 Atıf (Scopus)

Özet

Travel time prediction is an important component in intelligent transportation systems, and plays a key role in daily life. Predicting travel time for a trip is quite challenging and has been studied by many researcher. However, most of the studies focus on short term travel time prediction. In this study, LSTM (Long-Short Term Memory) neural network models are constructed to predict travel time for both long term and short term using real world data of New York city. Results of this study show that, LSTM provides satisfying results for long term travel time prediction as well as short term.

Orijinal dil	İngilizce
Dergi	CEUR Workshop Proceedings
Hacim	2482
Yayın durumu	Yayınlandı - 2019
Etkinlik	2018 Conference on Information and Knowledge Management Workshops, CIKM 2018 - Torino, Italy Süre: 22 Eki 2018 → …

Bibliyografik not

Publisher Copyright:
Copyright © CIKM 2018.

BM SKH

Bu sonuç, aşağıdaki Sürdürülebilir Kalkınma Hedefine/Hedeflerine katkıda bulunur

SKH 11 Sürdürülebilir Şehirler ve Topluluklar

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Link to publication in Scopus

Alıntı Yap

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title = "Use of LSTM for short-term and long-term travel time prediction",

abstract = "Travel time prediction is an important component in intelligent transportation systems, and plays a key role in daily life. Predicting travel time for a trip is quite challenging and has been studied by many researcher. However, most of the studies focus on short term travel time prediction. In this study, LSTM (Long-Short Term Memory) neural network models are constructed to predict travel time for both long term and short term using real world data of New York city. Results of this study show that, LSTM provides satisfying results for long term travel time prediction as well as short term.",

author = "Irem Islek and Oguducu, \{Sule Gunduz\}",

note = "Publisher Copyright: Copyright {\textcopyright} CIKM 2018.; 2018 Conference on Information and Knowledge Management Workshops, CIKM 2018 ; Conference date: 22-10-2018",

year = "2019",

language = "English",

volume = "2482",

journal = "CEUR Workshop Proceedings",

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TY - JOUR

T1 - Use of LSTM for short-term and long-term travel time prediction

AU - Islek, Irem

AU - Oguducu, Sule Gunduz

PY - 2019

Y1 - 2019

N2 - Travel time prediction is an important component in intelligent transportation systems, and plays a key role in daily life. Predicting travel time for a trip is quite challenging and has been studied by many researcher. However, most of the studies focus on short term travel time prediction. In this study, LSTM (Long-Short Term Memory) neural network models are constructed to predict travel time for both long term and short term using real world data of New York city. Results of this study show that, LSTM provides satisfying results for long term travel time prediction as well as short term.

AB - Travel time prediction is an important component in intelligent transportation systems, and plays a key role in daily life. Predicting travel time for a trip is quite challenging and has been studied by many researcher. However, most of the studies focus on short term travel time prediction. In this study, LSTM (Long-Short Term Memory) neural network models are constructed to predict travel time for both long term and short term using real world data of New York city. Results of this study show that, LSTM provides satisfying results for long term travel time prediction as well as short term.

UR - https://www.scopus.com/pages/publications/85075638334

M3 - Conference article

AN - SCOPUS:85075638334

SN - 1613-0073

VL - 2482

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

T2 - 2018 Conference on Information and Knowledge Management Workshops, CIKM 2018

Y2 - 22 October 2018

ER -

Use of LSTM for short-term and long-term travel time prediction

Özet

Bibliyografik not

BM SKH

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