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Prediction of bicyc...
Prediction of bicycle counter data using regression
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- Holmgren, Johan (författare)
- Malmö högskola,Fakulteten för teknik och samhälle (TS),Internet of Things and People (IOTAP),K2, Swedish Knowledge Centre for Public Transport
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- Aspegren, Sebastian (författare)
- Malmö högskola,Fakulteten för teknik och samhälle (TS)
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- Dahlström, Jonas (författare)
- Malmö högskola,Fakulteten för teknik och samhälle (TS)
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(creator_code:org_t)
- Elsevier, 2017
- 2017
- Engelska.
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Ingår i: Procedia Computer Science. - : Elsevier. - 1877-0509. ; 113, s. 502-507
- Relaterad länk:
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https://doi.org/10.1...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- We present a study, where we used regression in order to predict the number of bicycles registered by a bicycle counter (located in Malmö, Sweden). In particular, we compared two regression problems, differing only in their target variables (one using the absolute number of bicycles as target variable and the other one using the deviation from a long-term trend estimate of the expected number of bicycles as target variable). Our results show that using the trend curve deviation as target variable has potential to improve the prediction accuracy (compared to using the absolute number of bicycles as target variable). The results also show that support vector regression (using 2nd and 3rd degree polynomial kernels) and regression trees perform best for our problem.
Nyckelord
- Bicycle counter
- regression
- trend curve
- regression algorithm comparison
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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