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Short-Term Traffic Prediction Using Long Short-Term Memory Neural Networks
KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.ORCID iD: 0000-0001-6171-9586
RISE SICS, Stockholm, Sweden.
KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS. RISE SICS, Stockholm, Sweden..ORCID iD: 0000-0003-4516-7317
KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.ORCID iD: 0000-0002-6779-7435
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Short-term traffic prediction allows Intelligent Transport Systems to proactively respond to events before they happen. With the rapid increase in the amount, quality, and detail of traffic data, new techniques are required that can exploit the information in the data in order to provide better results while being able to scale and cope with increasing amounts of data and growing cities. We propose and compare three models for short-term road traffic density prediction based on Long Short-Term Memory (LSTM) neural networks. We have trained the models using real traffic data collected by Motorway Control System in Stockholm that monitors highways and collects flow and speed data per lane every minute from radar sensors. In order to deal with the challenge of scale and to improve prediction accuracy, we propose to partition the road network into road stretches and junctions, and to model each of the partitions with one or more LSTM neural networks. Our evaluation results show that partitioning of roads improves the prediction accuracy by reducing the root mean square error by the factor of 5. We show that we can reduce the complexity of LSTM network by limiting the number of input sensors, on average to 35% of the original number, without compromising the prediction accuracy.

Place, publisher, year, edition, pages
IEEE Computer Society Digital Library, 2018. p. 57-65
Keywords [en]
LSTM, neural networks, traffic prediction
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-236055DOI: 10.1109/BigDataCongress.2018.00015ISI: 000450160400008Scopus ID: 2-s2.0-85054887478OAI: oai:DiVA.org:kth-236055DiVA, id: diva2:1255663
Conference
2018 IEEE International Congress on Big Data (BigData Congress)
Note

QC 20181015

Available from: 2018-10-14 Created: 2018-10-14 Last updated: 2019-03-18Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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  • ieee
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Output format
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