Hybrid travel time estimation model for public transit buses using limited datasets Article Swipe
B P Ashwini
,
R. Sumathi
,
H. S. Sudhira
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.11591/ijai.v12.i4.pp1755-1764
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.11591/ijai.v12.i4.pp1755-1764
A reliable transit service can motivate commuters to switch their travelingmode from private to public. Providing necessary information to passengerswill reduce the uncertainties encountered during their travel and improveservice reliability. This article addresses the challenge of predicting dynamictravel times in urban areas where real-time traffic flow information isunavailable. In this perspective, a hybrid travel time estimation model(HTTEM) is proposed to predict the dynamic travel time using the predictedtravel times of the machine learning model and the preceding trip details. Theproposed model is validated using the location data of public transit buses of,Tumakuru, India. From the numerical results through error metrics, it is foundthat HTTEM improves the prediction accuracy, finally, it is concluded that theproposed model is suitable for estimating travel time in urban areas withheterogeneous traffic and limited traffic infrastructure.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.11591/ijai.v12.i4.pp1755-1764
- https://ijai.iaescore.com/index.php/IJAI/article/download/22492/13750
- OA Status
- diamond
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385506283
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385506283Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.11591/ijai.v12.i4.pp1755-1764Digital Object Identifier
- Title
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Hybrid travel time estimation model for public transit buses using limited datasetsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-02Full publication date if available
- Authors
-
B P Ashwini, R. Sumathi, H. S. SudhiraList of authors in order
- Landing page
-
https://doi.org/10.11591/ijai.v12.i4.pp1755-1764Publisher landing page
- PDF URL
-
https://ijai.iaescore.com/index.php/IJAI/article/download/22492/13750Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://ijai.iaescore.com/index.php/IJAI/article/download/22492/13750Direct OA link when available
- Concepts
-
Public transport, Travel time, Computer science, Reliability (semiconductor), Transit (satellite), Estimation, Service (business), Transport engineering, Transit time, Economics, Engineering, Economy, Physics, Management, Power (physics), Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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28Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.The<br | 86 |
| abstract_inverted_index.accuracy, | 117 |
| abstract_inverted_index.addresses | 35 |
| abstract_inverted_index.challenge | 37 |
| abstract_inverted_index.commuters | 6 |
| abstract_inverted_index.concluded | 121 |
| abstract_inverted_index.necessary | 17 |
| abstract_inverted_index.numerical | 104 |
| abstract_inverted_index.of,<br | 99 |
| abstract_inverted_index.preceding | 83 |
| abstract_inverted_index.real-time | 47 |
| abstract_inverted_index.the<br | 123 |
| abstract_inverted_index.validated | 90 |
| abstract_inverted_index.<p>A | 0 |
| abstract_inverted_index.estimating | 129 |
| abstract_inverted_index.estimation | 60 |
| abstract_inverted_index.predicting | 39 |
| abstract_inverted_index.prediction | 116 |
| abstract_inverted_index.with<br | 135 |
| abstract_inverted_index./>travel | 41, 74 |
| abstract_inverted_index.encountered | 25 |
| abstract_inverted_index.found<br | 111 |
| abstract_inverted_index.information | 18, 50 |
| abstract_inverted_index.model<br | 61 |
| abstract_inverted_index./>(HTTEM) | 62 |
| abstract_inverted_index./>service | 31 |
| abstract_inverted_index.perspective, | 55 |
| abstract_inverted_index.reliability. | 32 |
| abstract_inverted_index./>proposed | 87, 124 |
| abstract_inverted_index.dynamic<br | 40 |
| abstract_inverted_index.improve<br | 30 |
| abstract_inverted_index.uncertainties | 24 |
| abstract_inverted_index./>Tumakuru, | 100 |
| abstract_inverted_index.predicted<br | 73 |
| abstract_inverted_index.traveling<br | 10 |
| abstract_inverted_index.passengers<br | 20 |
| abstract_inverted_index./>unavailable. | 52 |
| abstract_inverted_index./>heterogeneous | 136 |
| abstract_inverted_index.infrastructure.</p> | 141 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.4699999988079071 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.12294713 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |