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Short-Term Traffic Prediction Using Long Short-Term Memory Neural Networks
KTH Royal Institute of Technology, Sweden.
RISE - Research Institutes of Sweden, ICT, SICS.ORCID iD: 0000-0002-9546-4937
RISE - Research Institutes of Sweden, ICT, SICS. KTH Royal Institute of Technology, Sweden.ORCID iD: 0000-0003-4516-7317
KTH Royal Institute of Technology, Sweden.
2018 (English)In: Proceedings - 2018 IEEE International Congress on Big Data, BigData Congress 2018 - Part of the 2018 IEEE World Congress on Services, Institute of Electrical and Electronics Engineers Inc. , 2018, p. 57-65Conference 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
Institute of Electrical and Electronics Engineers Inc. , 2018. p. 57-65
Keywords [en]
LSTM, neural networks, traffic prediction, Big data, Brain, Forecasting, Intelligent systems, Mean square error, Roads and streets, Traffic control, Evaluation results, Input sensor, Intelligent transport systems, Prediction accuracy, Radar sensors, Root mean square errors, Long short-term memory
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:ri:diva-37285DOI: 10.1109/BigDataCongress.2018.00015Scopus ID: 2-s2.0-85054887478ISBN: 9781538672327 OAI: oai:DiVA.org:ri-37285DiVA, id: diva2:1280353
Conference
7th IEEE International Congress on Big Data, BigData Congress 2018, 2 July 2018 through 7 July 2018
Note

Funding details: Fellowships Fund Incorporated, FFI; Funding details: VINNOVA; Funding details: 20140221; Funding details: European Commission, EC, FPA 2012-0030; Funding details: Education, Audiovisual and Culture Executive Agency, EACEA; Funding details: Directorate-General for Research and Innovation, 2015-00677; Funding text 1: ACKNOWLEDGMENT This work was supported by the project BADA: Big Automotive Data Analytics in the funding program FFI: Strategic Vehicle Research and Innovation (grant 2015-00677) administrated by VINNOVA the Swedish government agency for innovation systems, by the project BIDAF: Big Data Analytics Framework for a Smart Society (grant 20140221) funded by KKS the Swedish Knowledge Foundation, and by the Erasmus Mundus Joint Doctorate in Distributed Computing (EMJD-DC) programme funded by the Education, Audiovisual and Culture Executive Agency (EACEA) of the European Commission under FPA 2012-0030.

Available from: 2019-01-18 Created: 2019-01-18 Last updated: 2019-03-29Bibliographically approved

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Al-Shishtawy, AhmadGirdzijauskas, Sarunas

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