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Short-Term Traffic Forecasting Using Self-Adjusting k-Nearest Neighbours
Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.ORCID iD: 0000-0001-5824-425X
Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.ORCID iD: 0000-0003-0891-2859
Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
2018 (English)In: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 12, no 1, p. 41-48Article in journal (Refereed) Published
Abstract [en]

Short-term traffic forecasting is becoming more important in intelligent transportation systems. The k-nearest neighbours (kNN) method is widely used for short-term traffic forecasting.However, kNN parameters self-adjustment has been a problem due to dynamic traffic characteristics. This paper proposes a fully automatic dynamic procedure kNN (DP-kNN) that makes the kNN parameters self-adjustable and robust without predefined models or training. We used realworld data with more than one-year traffic records to conduct experiments. The results show that DP-kNN can perform better than manually adjusted kNN and other benchmarking methods with regards to accuracy on average. This study also discusses the difference between holiday and workday traffic prediction as well as the usage of neighbour distance measurement.

Place, publisher, year, edition, pages
Institution of Engineering and Technology, 2018. Vol. 12, no 1, p. 41-48
Keyword [en]
intelligent transportation systems; short-term traffic forecasting; road traffic; DP-kNN; dynamic procedure kNN; self-adjusting k-nearest neighbours
National Category
Computer Sciences Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:bth-15727DOI: 10.1049/iet-its.2016.0263ISI: 000426045200006OAI: oai:DiVA.org:bth-15727DiVA, id: diva2:1172050
Available from: 2018-01-09 Created: 2018-01-09 Last updated: 2018-03-23Bibliographically approved

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Sun, BinCheng, WeiGoswami, PrashantBai, Guohua
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