Short-term forecasting of bicycle traffic using structural time series models

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Title: Short-term forecasting of bicycle traffic using structural time series models
Authors: Doorley, Ronan
Pakrashi, Vikram
Caulfield, Brian
Ghosh, Bidisha
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Date: 20-Nov-2014
Online since: 2019-05-13T11:46:20Z
Abstract: Short term forecasting algorithms are widely used for prediction of vehicular traffic flows for adaptive traffic management. However, despite the increasing interest in the promotion of cycling in cities, little research has been carried out into the use of traffic forecasting algorithms for bicycle traffic. Structural time series models allow the various components of a time series such as level, seasonal and regression effects to be modelled separately to allow analysis of previous trends and forecasting. In this paper, a case study at a segregated bicycle lane in Dublin, Ireland was performed to test the forecasting accuracy of structural time series models applied to continuous observations of cyclist traffic volumes. It has been shown that the proposed models can produce accurate peak period forecasts of cyclist traffic volumes at both 1 hour and fifteen minute resolution and that the percentage errors are lower for hourly forecasts. The inclusion of weather metrics as explanatory variables had varying effects on the forecasting accuracies of the models. These results directly aid the design of traffic signal control systems accommodating cyclists.
Funding Details: Environmental Protection Agency
Type of material: Conference Publication
Publisher: IEEE
Copyright (published version): 2014 IEEE
Keywords: ForecastingTime series analysisPredictive modelsTemperature measurementComputational modelingLoad modelingCities and towns
DOI: 10.1109/ITSC.2014.6957948
Language: en
Status of Item: Peer reviewed
Is part of: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC)
Conference Details: 2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014, Qingdao, China, 8-11 October 2014
ISBN: 978-1-4799-6078-1
Appears in Collections:Mechanical & Materials Engineering Research Collection

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