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Bus Arrival Prediction – to Ensure Users not to Miss the Bus

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 5 No. 2 (2015)

DOI
10.11591/ijece.v5i2.pp333-339
ISSN
2088-8708, 2088-8708
Published
2015-04-02
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

Predicting arrival times of buses is a key challenge in the context of building intelligent public transportation systems. The bus arrival time is the primary information for providing passengers with an accurate information system that can reduce passenger waiting times. In this paper, we used the normal distribution method to the random of travel times data in a bus line number 243 in Taipei area. In developing the models, data were collected from Taipei Bus Company. A normal distribution method used for predicting the bus arrival time in bus stop to ensure users not to miss the bus, and compare the result with the existing application. The result of our experiment showed that our proposed method has a better prediction than existing application, with the probability user not to miss the bus in peak time is 93% and in normal time is 85%, greater than from the existing application with the 65% probability in peak time, and 70% in normal time.

Collaboration network of the authors

34 authors · 64 co-authorship links · 11 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • University of Brawijaya
  • National Central University
  • Politeknik Elektronika Negeri Surabaya
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Adam Hendra BrataUniversity of Brawijaya4
Komang Candra BrataUniversity of Brawijaya4
Sholeh Hadi PramonoUniversity of Brawijaya3
Sabriansyah Rizqika AkbarUniversity of Brawijaya2
Fitra Abdurrachman BachtiarUniversity of Brawijaya2
Eko SetiawanUniversity of Brawijaya2
Barlian HenryranuUniversity of Brawijaya1
Maystia Tri HandonoAffiliation unknown1
Fandisya RahmanUniversity of Brawijaya1
Ismiarta AknurandaUniversity of Brawijaya1
Bayu SutawijayaUniversity of Brawijaya1
Khafidzun FadliUniversity of Brawijaya1
Sulandri SulandriUniversity of Brawijaya1
Muhammad Jainal ArifinPoliteknik Elektronika Negeri Surabaya1
Bima Sena Bayu DewantaraPoliteknik Elektronika Negeri Surabaya1
Hawwin Purnama AkbarUniversity of Brawijaya1
Adhitya BhawiyugaUniversity of Brawijaya1
Muhammad Naufal DzakieUniversity of Brawijaya1
Kasyful AmronUniversity of Brawijaya1
Primantara Hari TrisnawanUniversity of Brawijaya1
Muhammad Fajar AlfathUniversity of Brawijaya1
Agi Putra KharismaUniversity of Brawijaya1
Dava Mohammad HamkaUniversity of Brawijaya1
Muhamad Fauzan WahyudiAffiliation unknown1
Muhamad Fauzan WahyudiUniversity of Brawijaya1
Maystya Tri HandonoUniversity of Brawijaya1
Ika Kusumaning PutriUniversity of Brawijaya1
Rahmadwati RahmadwatiUniversity of Brawijaya1
Widhi YahyaUniversity of Brawijaya1
Jehn Ruey JiangNational Central University1
Mukhammad Sharif HidayatullohUniversity of Brawijaya1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Bus Arrival Prediction – to Ensure Users not to Miss the Bus</dc:title><dc:creator>Fanani, Lutfi</dc:creator><dc:creator>Basuki, Achmad</dc:creator><dc:creator>Liang, Deron</dc:creator><dc:description>&lt;jats:p&gt;Predicting arrival times of buses is a key challenge in the context of building intelligent public transportation systems. The bus arrival time is the primary information for providing passengers with an accurate information system that can reduce passenger waiting times. In this paper, we used the normal distribution method to the random of travel times data in a bus line number 243 in Taipei area. In developing the models, data were collected from Taipei Bus Company. A normal distribution method used for predicting the bus arrival time in bus stop to ensure users not to miss the bus, and compare the result with the existing application. The result of our experiment showed that our proposed method has a better prediction than existing application, with the probability user not to miss the bus in peak time is 93% and in normal time is 85%, greater than from the existing application with the 65% probability in peak time, and 70% in normal time.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2015-04-02</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v5i2.pp333-339</dc:identifier><dc:identifier>10.11591/ijece.v5i2.pp333-339</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 5 No 2 (2015); 333</dc:source><dc:source>2088-8708</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/5701/4762</dc:relation><dc:rights>http://creativecommons.org/licenses/by-nc/4.0</dc:rights></oai_dc:dc>