Thesis Open Access
HABTU REDA
{ "files": [ { "links": { "self": "https://nadre.ethernet.edu.et/api/files/c561850c-991b-4ffd-92a1-df88f90a300b/f1042664640.pdf" }, "checksum": "md5:d45cbd5fd89e1d88df48cadeabb670f7", "bucket": "c561850c-991b-4ffd-92a1-df88f90a300b", "key": "f1042664640.pdf", "type": "pdf", "size": 647925 } ], "owners": [ 11 ], "doi": "10.20372/nadre:4654", "stats": {}, "links": { "doi": "https://doi.org/10.20372/nadre:4654", "conceptdoi": "https://doi.org/10.20372/nadre:4653", "bucket": "https://nadre.ethernet.edu.et/api/files/c561850c-991b-4ffd-92a1-df88f90a300b", "conceptbadge": "https://nadre.ethernet.edu.et/badge/doi/10.20372/nadre%3A4653.svg", "html": "https://nadre.ethernet.edu.et/record/4654", "latest_html": "https://nadre.ethernet.edu.et/record/4654", "badge": "https://nadre.ethernet.edu.et/badge/doi/10.20372/nadre%3A4654.svg", "latest": "https://nadre.ethernet.edu.et/api/records/4654" }, "conceptdoi": "10.20372/nadre:4653", "created": "2024-12-03T13:34:45.582458+00:00", "updated": "2024-12-03T13:34:47.776785+00:00", "conceptrecid": "4653", "revision": 3, "id": 4654, "metadata": { "access_right_category": "success", "doi": "10.20372/nadre:4654", "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. The prepared dataset is partitioned into 90% training and 10%<br>\ntesting set. Beginning Date, Beginning Time, End Date, Time Range, Mileage, Duration, Initial<br>\nlatitude, Initial longitude, Final latitude, Final longitude, and End Time were used as input<br>\nfeatures for developing the model. Based on the experiment result the Random Forest algorithm<br>\nachieved a better performance with R-squared score of 0.994, MAE of 0.812, RMSE of 3.780<br>\nand MSE of 14.28.</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": "4653" }, "is_last": true, "last_child": { "pid_type": "recid", "pid_value": "4654" } } ] }, "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:4653", "relation": "isVersionOf" } ] } }
All versions | This version | |
---|---|---|
Views | 0 | 0 |
Downloads | 0 | 0 |
Data volume | 0 Bytes | 0 Bytes |
Unique views | 0 | 0 |
Unique downloads | 0 | 0 |