October 1, 2021 Thesis Open Access

HABTU REDA

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  <identifier identifierType="DOI">10.20372/nadre:4654</identifier>
  <creators>
    <creator>
      <creatorName>HABTU REDA</creatorName>
    </creator>
  </creators>
  <titles>
    <title>PUBLIC BUS ARRIVAL TIME PREDICTION USING MACHINE LEARNING: IN CASE OF ADDIS ABABA</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <dates>
    <date dateType="Issued">2021-10-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Thesis</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://nadre.ethernet.edu.et/record/4654</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.20372/nadre:4653</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://nadre.ethernet.edu.et/communities/aastu</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://nadre.ethernet.edu.et/communities/zenodo</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Estimating public bus arrival times and delivering accurate arrival time information to&lt;br&gt;
passengers are critical for making public transportation more user-friendly and thereby&lt;br&gt;
increasing its competitiveness among various forms of transportation. However public bus&lt;br&gt;
arrival time prediction remains major bottlenecks With traffic heterogeneity in composition and&lt;br&gt;
diversity of vehicles, as well as a big pedestrian population combined with inadequate lane use,&lt;br&gt;
predicting the arrival time of public buses at stations is a severe concern.. The main objective of&lt;br&gt;
this study is to apply machine learning algorithms to predict bus arrival time. The data was&lt;br&gt;
collected from Addis Ababa Sheger Public Bus Transport. Random Forest, Gradient Boosting,&lt;br&gt;
Artificial Neural Network, K-Nearest Neighbors and Support Vector Machine algorithms are&lt;br&gt;
applied to build the models and to compare and choose the best model to predict the bus arrival&lt;br&gt;
time. After selecting the features and algorithms, different data preprocessing tasks like checking&lt;br&gt;
outliers, missing values and data reduction are done. Finally, 140,000 instances of dataset are&lt;br&gt;
used to train and build the model. The prepared dataset is partitioned into 90% training and 10%&lt;br&gt;
testing set. Beginning Date, Beginning Time, End Date, Time Range, Mileage, Duration, Initial&lt;br&gt;
latitude, Initial longitude, Final latitude, Final longitude, and End Time were used as input&lt;br&gt;
features for developing the model. Based on the experiment result the Random Forest algorithm&lt;br&gt;
achieved a better performance with R-squared score of 0.994, MAE of 0.812, RMSE of 3.780&lt;br&gt;
and MSE of 14.28.&lt;/p&gt;</description>
  </descriptions>
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