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A Framework for Traffic Prediction Integrated with Deep Learning
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.ORCID iD: 0000-0001-7218-9082
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.ORCID iD: 0000-0001-5361-6034
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.ORCID iD: 0000-0003-1164-8403
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

City-scale traffic prediction is an important task for public safety, traffic management, and deployment of intelligent transportation systems. Many approaches have been proposed to address traffic prediction task using machine learning techniques. In this paper, we present a framework to help on addressing the task at hand (density-, traffic flow- and origin-destination flow predictions) considering data type, features, deep learning techniques such as Convolutional Neural Networks (CNNs), e.g., Autoencoder, Recurrent Neural Networks (RNNs), e.g., Long Short Term Memory (LSTM), and Graph Convolutional Networks (GCNs). An autoencoder model is designed in this paper to predict traffic density based on historical data. Experiments on real-world taxi order data demonstrate the effectiveness of the model.

Place, publisher, year, edition, pages
2019.
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:kth:diva-254200OAI: oai:DiVA.org:kth-254200DiVA, id: diva2:1329004
Conference
The 8th Symposium of the European Association for Research in Transportation
Note

QC 20190625

Available from: 2019-06-24 Created: 2019-06-24 Last updated: 2019-06-25Bibliographically approved

Open Access in DiVA

Traffic_Prediction_Framework(3660 kB)33 downloads
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File name FULLTEXT01.pdfFile size 3660 kBChecksum SHA-512
544abf641bf399282a4cdd075d41b37a36dc1918c0426cd1defdaf7778b6dca7a2b68e949d156cc5413e198c7497ee1a4a8bb73ad0c276c7ac094ccaf6e480e4
Type fulltextMimetype application/pdf

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http://heart2019.bme.hu/

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Cumbane, Silvino PedroYang, CanGidofalvi, Gyözö
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf