ARIMA models for bus travel time prediction

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Main Authors: Suwardo, Madzlan, Napiah, Ibrahim, Kamaruddin
Other Authors: suwardo@yahoo.com
Format: Article
Language:English
Published: The Institution of Engineers, Malaysia 2011
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Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/13714
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spelling my.unimap-137142011-09-10T15:50:18Z ARIMA models for bus travel time prediction Suwardo Madzlan, Napiah Ibrahim, Kamaruddin suwardo@yahoo.com Autoregressive Integrated Moving Average Bus travel time Mean absolute percentage prediction error Mean absolute relative error Time series model Link to publisher's homepage at http://www.myiem.org.my/ In this paper, the time series model, Autoregressive Integrated Moving Average (ARIMA) is used to predict bus travel time. ARIMA model is simpler used for predicting bus travel time based on travel time series data (historic data) compared to regression method as the factors affecting bus travel time are not available in detail such as delay at link, bus stop, intersection, etc. Bus travel time prediction is an important aspect to bus operator in providing timetable for bus operation management and user information. The study aims at finding appropriate time series model for predicting bus travel time by evaluating the minimum of mean absolute relative error (MARE) and mean absolute percentage prediction error (MAPPE). In this case, data set was collected from the bus service operated on a divided 4-lane 2-way highway in Ipoh-Lumut corridor, Perak, Malaysia. The estimated parameters, appropriate model, and measures of model performance evaluation are presented. The analysis of both Ipoh to Lumut and Lumut to Ipoh directions is separately performed. The results show that the predicted travel times by using the moving average, MA(2) and MA(1) model, clearly fit with the observed values for both directions, respectively. These appropriate models are indicated by the minimum MARE and MAPPE values among the tentative models. It is concluded that MA(2) and MA(1) models are able to be appropriately applied in this case, and those models can be used for bus travel time prediction which helping in the timetable design or setup. 2011-09-10T15:50:18Z 2011-09-10T15:50:18Z 2010-06 Article The Journal of the Institution of Engineers, Malaysia, vol. 71(2), 2010, pages 49-58 0126-513X http://www.myiem.org.my/content/iem_journal_2010-181.aspx http://hdl.handle.net/123456789/13714 en The Institution of Engineers, Malaysia
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Autoregressive Integrated Moving Average
Bus travel time
Mean absolute percentage prediction error
Mean absolute relative error
Time series model
spellingShingle Autoregressive Integrated Moving Average
Bus travel time
Mean absolute percentage prediction error
Mean absolute relative error
Time series model
Suwardo
Madzlan, Napiah
Ibrahim, Kamaruddin
ARIMA models for bus travel time prediction
description Link to publisher's homepage at http://www.myiem.org.my/
author2 suwardo@yahoo.com
author_facet suwardo@yahoo.com
Suwardo
Madzlan, Napiah
Ibrahim, Kamaruddin
format Article
author Suwardo
Madzlan, Napiah
Ibrahim, Kamaruddin
author_sort Suwardo
title ARIMA models for bus travel time prediction
title_short ARIMA models for bus travel time prediction
title_full ARIMA models for bus travel time prediction
title_fullStr ARIMA models for bus travel time prediction
title_full_unstemmed ARIMA models for bus travel time prediction
title_sort arima models for bus travel time prediction
publisher The Institution of Engineers, Malaysia
publishDate 2011
url http://dspace.unimap.edu.my/xmlui/handle/123456789/13714
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score 13.222552