Regression-based evaluation of bicycle flow trend estimates

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Regression-based evaluation of bicycle flow trend estimates

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Publication Article, peer reviewed scientific
Title Regression-based evaluation of bicycle flow trend estimates
Author Holmgren, Johan ; Moltubakk, Gabriel ; O'Neill, Jody
Date 2018
English abstract
It has been shown in previous research that regression modeling can be used in order to predict the number of bicycles registered by a bicycle counter. To improve the prediction accuracy, it has also been suggested that a long-term trend curve estimate can be incorporated in a regression problem formulation. A long-term trend curve estimate aims to capture those factors that are difficult, or even impossible, to explicitly model as input variables in the regression model. In the current paper, we present a regression-based approach for evaluating long-term trend curve estimates regarding their possibility to improve the regression prediction accuracy of bicycle counter data. We illustrate our approach by applying it on a time series recorded by a bicycle counter in Malmö, Sweden. For the considered data set, our experimental results indicate that a polynomial of degree two, which has been fitted to the time series, gives the best prediction.
Conference
The 9th International Conference on Ambient Systems, Networks and Technologies (8-11 Maj, 2018 : Porto, Portugal)
DOI https://doi.org/10.1016/j.procs.2018.04.073 (link to publisher's fulltext.)
Link https://www.sciencedirect.com/science/article/pii/S1877050918304356 .Icon
Publisher Elsevier
Host/Issue Procedia Computer Science;130
Language eng (iso)
Subject Bicycle counter
regression
trend curve
evaluation
Technology
Research Subject Categories::TECHNOLOGY
Handle http://hdl.handle.net/2043/26019 Permalink to this page
Link http://cs-conferences.acadiau.ca/ant-18/... (external link to related web page)
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