Bus Travel Time Prediction: A Comparative Study of Linear and Non-Linear Machine Learning Models Article Swipe
B P Ashwini
,
R. Sumathi
,
H. S. Sudhira
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2161/1/012053
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2161/1/012053
Congested roads are a global problem, and increased usage of private vehicles is one of the main reasons for congestion. Public transit modes of travel are a sustainable and eco-friendly alternative for private vehicle usage, but attracting commuters towards public transit mode is a mammoth task. Commuters expect the public transit service to be reliable, and to provide a reliable service it is necessary to fine-tune the transit operations and provide well-timed necessary information to commuters. In this context, the public transit travel time is predicted in Tumakuru, a tier-2 city of Karnataka, India. As this is one of the initial studies in the city, the performance comparison of eight Machines Learning models including four linear namely, Linear Regression, Ridge Regression, Least Absolute Shrinkage and Selection Operator Regression, and Support Vector Regression; and four non-linear models namely, k-Nearest Neighbors, Regression Trees, Random Forest Regression, and Gradient Boosting Regression Trees is conducted to identify a suitable model for travel time predictions. The data logs of one month (November 2020) of the Tumakuru city service, provided by Tumakuru Smart City Limited are used for the study. The time-of-the-day (trip start time), day-of-the-week, and direction of travel are used for the prediction. Travel time for both upstream and downstream are predicted, and the results are evaluated based on the performance metrics. The results suggest that the performance of non-linear models is superior to linear models for predicting travel times, and Random Forest Regression was found to be a better model as compared to other models.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1742-6596/2161/1/012053
- OA Status
- diamond
- Cited By
- 16
- References
- 21
- Related Works
- 10
- OpenAlex ID
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- OpenAlex ID
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https://openalex.org/W4205286101Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/1742-6596/2161/1/012053Digital Object Identifier
- Title
-
Bus Travel Time Prediction: A Comparative Study of Linear and Non-Linear Machine Learning ModelsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-01-01Full publication date if available
- Authors
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B P Ashwini, R. Sumathi, H. S. SudhiraList of authors in order
- Landing page
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https://doi.org/10.1088/1742-6596/2161/1/012053Publisher landing page
- Open access
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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://doi.org/10.1088/1742-6596/2161/1/012053Direct OA link when available
- Concepts
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Random forest, Linear regression, Public transport, Computer science, Regression analysis, Linear model, Context (archaeology), Gradient boosting, Support vector machine, Decision tree, Transport engineering, Engineering, Geography, Machine learning, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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16Total citation count in OpenAlex
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2025: 4, 2024: 4, 2023: 4, 2022: 4Per-year citation counts (last 5 years)
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21Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.main | 17 |
| abstract_inverted_index.mode | 42 |
| abstract_inverted_index.that | 222 |
| abstract_inverted_index.this | 78, 96 |
| abstract_inverted_index.time | 84, 159, 201 |
| abstract_inverted_index.used | 181, 196 |
| abstract_inverted_index.(trip | 187 |
| abstract_inverted_index.2020) | 168 |
| abstract_inverted_index.Least | 122 |
| abstract_inverted_index.Ridge | 120 |
| abstract_inverted_index.Smart | 177 |
| abstract_inverted_index.Trees | 149 |
| abstract_inverted_index.based | 214 |
| abstract_inverted_index.city, | 105 |
| abstract_inverted_index.eight | 110 |
| abstract_inverted_index.found | 242 |
| abstract_inverted_index.model | 156, 247 |
| abstract_inverted_index.modes | 23 |
| abstract_inverted_index.month | 166 |
| abstract_inverted_index.other | 251 |
| abstract_inverted_index.roads | 2 |
| abstract_inverted_index.start | 188 |
| abstract_inverted_index.task. | 46 |
| abstract_inverted_index.usage | 9 |
| abstract_inverted_index.Forest | 143, 239 |
| abstract_inverted_index.India. | 94 |
| abstract_inverted_index.Linear | 118 |
| abstract_inverted_index.Public | 21 |
| abstract_inverted_index.Random | 142, 238 |
| abstract_inverted_index.Travel | 200 |
| abstract_inverted_index.Trees, | 141 |
| abstract_inverted_index.Vector | 131 |
| abstract_inverted_index.better | 246 |
| abstract_inverted_index.expect | 48 |
| abstract_inverted_index.global | 5 |
| abstract_inverted_index.linear | 116, 231 |
| abstract_inverted_index.models | 113, 136, 227, 232 |
| abstract_inverted_index.public | 40, 50, 81 |
| abstract_inverted_index.study. | 184 |
| abstract_inverted_index.tier-2 | 90 |
| abstract_inverted_index.time), | 189 |
| abstract_inverted_index.times, | 236 |
| abstract_inverted_index.travel | 25, 83, 158, 194, 235 |
| abstract_inverted_index.usage, | 35 |
| abstract_inverted_index.Limited | 179 |
| abstract_inverted_index.Support | 130 |
| abstract_inverted_index.initial | 101 |
| abstract_inverted_index.mammoth | 45 |
| abstract_inverted_index.models. | 252 |
| abstract_inverted_index.namely, | 117, 137 |
| abstract_inverted_index.private | 11, 33 |
| abstract_inverted_index.provide | 58, 71 |
| abstract_inverted_index.reasons | 18 |
| abstract_inverted_index.results | 211, 220 |
| abstract_inverted_index.service | 52, 61 |
| abstract_inverted_index.studies | 102 |
| abstract_inverted_index.suggest | 221 |
| abstract_inverted_index.towards | 39 |
| abstract_inverted_index.transit | 22, 41, 51, 68, 82 |
| abstract_inverted_index.vehicle | 34 |
| abstract_inverted_index.Absolute | 123 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Boosting | 147 |
| abstract_inverted_index.Gradient | 146 |
| abstract_inverted_index.Learning | 112 |
| abstract_inverted_index.Machines | 111 |
| abstract_inverted_index.Operator | 127 |
| abstract_inverted_index.Tumakuru | 171, 176 |
| abstract_inverted_index.compared | 249 |
| abstract_inverted_index.context, | 79 |
| abstract_inverted_index.identify | 153 |
| abstract_inverted_index.metrics. | 218 |
| abstract_inverted_index.problem, | 6 |
| abstract_inverted_index.provided | 174 |
| abstract_inverted_index.reliable | 60 |
| abstract_inverted_index.service, | 173 |
| abstract_inverted_index.suitable | 155 |
| abstract_inverted_index.superior | 229 |
| abstract_inverted_index.upstream | 204 |
| abstract_inverted_index.vehicles | 12 |
| abstract_inverted_index.(November | 167 |
| abstract_inverted_index.Commuters | 47 |
| abstract_inverted_index.Congested | 1 |
| abstract_inverted_index.Selection | 126 |
| abstract_inverted_index.Shrinkage | 124 |
| abstract_inverted_index.Tumakuru, | 88 |
| abstract_inverted_index.commuters | 38 |
| abstract_inverted_index.conducted | 151 |
| abstract_inverted_index.direction | 192 |
| abstract_inverted_index.evaluated | 213 |
| abstract_inverted_index.fine-tune | 66 |
| abstract_inverted_index.including | 114 |
| abstract_inverted_index.increased | 8 |
| abstract_inverted_index.k-Nearest | 138 |
| abstract_inverted_index.necessary | 64, 73 |
| abstract_inverted_index.predicted | 86 |
| abstract_inverted_index.reliable, | 55 |
| abstract_inverted_index.Karnataka, | 93 |
| abstract_inverted_index.Neighbors, | 139 |
| abstract_inverted_index.Regression | 140, 148, 240 |
| abstract_inverted_index.attracting | 37 |
| abstract_inverted_index.commuters. | 76 |
| abstract_inverted_index.comparison | 108 |
| abstract_inverted_index.downstream | 206 |
| abstract_inverted_index.non-linear | 135, 226 |
| abstract_inverted_index.operations | 69 |
| abstract_inverted_index.predicted, | 208 |
| abstract_inverted_index.predicting | 234 |
| abstract_inverted_index.well-timed | 72 |
| abstract_inverted_index.Regression, | 119, 121, 128, 144 |
| abstract_inverted_index.Regression; | 132 |
| abstract_inverted_index.alternative | 31 |
| abstract_inverted_index.congestion. | 20 |
| abstract_inverted_index.information | 74 |
| abstract_inverted_index.performance | 107, 217, 224 |
| abstract_inverted_index.prediction. | 199 |
| abstract_inverted_index.sustainable | 28 |
| abstract_inverted_index.eco-friendly | 30 |
| abstract_inverted_index.predictions. | 160 |
| abstract_inverted_index.time-of-the-day | 186 |
| abstract_inverted_index.day-of-the-week, | 190 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.5199999809265137 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.8237568 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |