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

Department of Artificial Intelligence and Data Engineering

Istanbul Technical University

Research output: Contribution to journal › Conference article › peer-review

2 Citations (Scopus)

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.

Original language	English
Journal	CEUR Workshop Proceedings
Volume	2482
Publication status	Published - 2019
Event	2018 Conference on Information and Knowledge Management Workshops, CIKM 2018 - Torino, Italy Duration: 22 Oct 2018 → …

Bibliographical note

Publisher Copyright:
Copyright © CIKM 2018.

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Cite this

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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}",

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AU - Islek, Irem

AU - Oguducu, Sule Gunduz

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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.

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M3 - Conference article

AN - SCOPUS:85075638334

SN - 1613-0073

VL - 2482

JO - CEUR Workshop Proceedings

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

Abstract

Bibliographical note

UN SDGs

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