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A Framework for Traffic Prediction Integrated with Deep Learning
KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Samhällsplanering och miljö, Geoinformatik.ORCID-id: 0000-0001-7218-9082
KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Samhällsplanering och miljö, Geoinformatik.ORCID-id: 0000-0001-5361-6034
KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Samhällsplanering och miljö, Geoinformatik.ORCID-id: 0000-0003-1164-8403
2019 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
2019.
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-254200OAI: oai:DiVA.org:kth-254200DiVA, id: diva2:1329004
Konferanse
The 8th Symposium of the European Association for Research in Transportation
Merknad

QC 20190625

Tilgjengelig fra: 2019-06-24 Laget: 2019-06-24 Sist oppdatert: 2019-06-25bibliografisk kontrollert

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Traffic_Prediction_Framework(3660 kB)33 nedlastinger
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Fil FULLTEXT01.pdfFilstørrelse 3660 kBChecksum SHA-512
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Type fulltextMimetype application/pdf

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