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Traffic Speed Prediction Using Big D...

Wang, Shuo.

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Traffic Speed Prediction Using Big Data Enabled Deep Learning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Traffic Speed Prediction Using Big Data Enabled Deep Learning./
Author:	Wang, Shuo.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	109 p.
Notes:	Source: Dissertation Abstracts International, Volume: 79-11(E), Section: A.
Contained By:	Dissertation Abstracts International79-11A(E).
Subject:	Transportation. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10746489
ISBN:	9780438076662

Traffic Speed Prediction Using Big Data Enabled Deep Learning.
Wang, Shuo.

Traffic Speed Prediction Using Big Data Enabled Deep Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 109 p.

Source: Dissertation Abstracts International, Volume: 79-11(E), Section: A.

Thesis (Ph.D.)--Iowa State University, 2018.

This item is not available from ProQuest Dissertations & Theses.

The objective of the proposed study is to predict traffic speeds at a route level so that the traffic management has a chance to operate proactively. A distributed file system and parallel computing platform is used to store the big data sets of state-wide traffic and weather data in a fault-tolerant way and process the big data in a timely manner. Traffic speed prediction problem is studies at two levels and two deep networks are proposed accordingly: a fully convolutional deep network for long-term speed prediction and a hybrid LSTM network for short-term speed prediction. The fully convolutional deep network utilizes both weather information and historical traffic speeds to make long-term traffic speed prediction and a trained model can be transferred to predict traffic speed at any spatial-temporal scale. The hybrid LSTM network utilizes the previous traffic speeds on the current day as well as historical traffic speeds to make short-term speed prediction and a trained model can be used to predict speeds at any timestamps ahead in a streaming fashion. The proposed long-term and short-term traffic speed prediction models can be combined as a multi-layer decision supporting system to provide traffic management an opportunity to operate proactively.

ISBN: 9780438076662Subjects--Topical Terms:

555912
Transportation.

Traffic Speed Prediction Using Big Data Enabled Deep Learning.
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500 $a Source: Dissertation Abstracts International, Volume: 79-11(E), Section: A.
500 $a Advisers: Anuj Sharma; Soumik Sarkar.
502 $a Thesis (Ph.D.)--Iowa State University, 2018.
506 $a This item is not available from ProQuest Dissertations & Theses.
520 $a The objective of the proposed study is to predict traffic speeds at a route level so that the traffic management has a chance to operate proactively. A distributed file system and parallel computing platform is used to store the big data sets of state-wide traffic and weather data in a fault-tolerant way and process the big data in a timely manner. Traffic speed prediction problem is studies at two levels and two deep networks are proposed accordingly: a fully convolutional deep network for long-term speed prediction and a hybrid LSTM network for short-term speed prediction. The fully convolutional deep network utilizes both weather information and historical traffic speeds to make long-term traffic speed prediction and a trained model can be transferred to predict traffic speed at any spatial-temporal scale. The hybrid LSTM network utilizes the previous traffic speeds on the current day as well as historical traffic speeds to make short-term speed prediction and a trained model can be used to predict speeds at any timestamps ahead in a streaming fashion. The proposed long-term and short-term traffic speed prediction models can be combined as a multi-layer decision supporting system to provide traffic management an opportunity to operate proactively.
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