October 1, 2021 Thesis Open Access

HABTU REDA

JSON Export

{
  "files": [
    {
      "links": {
        "self": "https://nadre.ethernet.edu.et/api/files/eb8dc83f-1ba2-4b4f-8889-85326cfcef22/f1050029912.pdf"
      }, 
      "checksum": "md5:c09450d9656de07585958a1f351bd6aa", 
      "bucket": "eb8dc83f-1ba2-4b4f-8889-85326cfcef22", 
      "key": "f1050029912.pdf", 
      "type": "pdf", 
      "size": 1885429
    }
  ], 
  "owners": [
    11
  ], 
  "doi": "10.20372/nadre:2742", 
  "stats": {}, 
  "links": {
    "doi": "https://doi.org/10.20372/nadre:2742", 
    "conceptdoi": "https://doi.org/10.20372/nadre:2741", 
    "bucket": "https://nadre.ethernet.edu.et/api/files/eb8dc83f-1ba2-4b4f-8889-85326cfcef22", 
    "conceptbadge": "https://nadre.ethernet.edu.et/badge/doi/10.20372/nadre%3A2741.svg", 
    "html": "https://nadre.ethernet.edu.et/record/2742", 
    "latest_html": "https://nadre.ethernet.edu.et/record/2742", 
    "badge": "https://nadre.ethernet.edu.et/badge/doi/10.20372/nadre%3A2742.svg", 
    "latest": "https://nadre.ethernet.edu.et/api/records/2742"
  }, 
  "conceptdoi": "10.20372/nadre:2741", 
  "created": "2024-10-07T08:08:00.409490+00:00", 
  "updated": "2024-10-07T08:08:02.460794+00:00", 
  "conceptrecid": "2741", 
  "revision": 3, 
  "id": 2742, 
  "metadata": {
    "access_right_category": "success", 
    "doi": "10.20372/nadre:2742", 
    "description": "<p>Estimating public bus arrival times and delivering accurate arrival time information to<br>\npassengers are critical for making public transportation more user-friendly and thereby<br>\nincreasing its competitiveness among various forms of transportation. However public bus<br>\narrival time prediction remains major bottlenecks With traffic heterogeneity in composition and<br>\ndiversity of vehicles, as well as a big pedestrian population combined with inadequate lane use,<br>\npredicting the arrival time of public buses at stations is a severe concern.. The main objective of<br>\nthis study is to apply machine learning algorithms to predict bus arrival time. The data was<br>\ncollected from Addis Ababa Sheger Public Bus Transport. Random Forest, Gradient Boosting,<br>\nArtificial Neural Network, K-Nearest Neighbors and Support Vector Machine algorithms are<br>\napplied to build the models and to compare and choose the best model to predict the bus arrival<br>\ntime. After selecting the features and algorithms, different data preprocessing tasks like checking<br>\noutliers, missing values and data reduction are done. Finally, 140,000 instances of dataset are<br>\nused to train and build the model</p>", 
    "license": {
      "id": "cc-by"
    }, 
    "title": "PUBLIC BUS ARRIVAL TIME PREDICTION USING MACHINE LEARNING: IN CASE OF ADDIS ABABA", 
    "relations": {
      "version": [
        {
          "count": 1, 
          "index": 0, 
          "parent": {
            "pid_type": "recid", 
            "pid_value": "2741"
          }, 
          "is_last": true, 
          "last_child": {
            "pid_type": "recid", 
            "pid_value": "2742"
          }
        }
      ]
    }, 
    "communities": [
      {
        "id": "aastu"
      }, 
      {
        "id": "zenodo"
      }
    ], 
    "publication_date": "2021-10-01", 
    "creators": [
      {
        "name": "HABTU REDA"
      }
    ], 
    "access_right": "open", 
    "resource_type": {
      "subtype": "thesis", 
      "type": "publication", 
      "title": "Thesis"
    }, 
    "related_identifiers": [
      {
        "scheme": "doi", 
        "identifier": "10.20372/nadre:2741", 
        "relation": "isVersionOf"
      }
    ]
  }
}