Thesis Open Access
HABTU REDA
<?xml version='1.0' encoding='utf-8'?> <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:adms="http://www.w3.org/ns/adms#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dct="http://purl.org/dc/terms/" xmlns:dctype="http://purl.org/dc/dcmitype/" xmlns:dcat="http://www.w3.org/ns/dcat#" xmlns:duv="http://www.w3.org/ns/duv#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:frapo="http://purl.org/cerif/frapo/" xmlns:geo="http://www.w3.org/2003/01/geo/wgs84_pos#" xmlns:gsp="http://www.opengis.net/ont/geosparql#" xmlns:locn="http://www.w3.org/ns/locn#" xmlns:org="http://www.w3.org/ns/org#" xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:prov="http://www.w3.org/ns/prov#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:schema="http://schema.org/" xmlns:skos="http://www.w3.org/2004/02/skos/core#" xmlns:vcard="http://www.w3.org/2006/vcard/ns#" xmlns:wdrs="http://www.w3.org/2007/05/powder-s#"> <rdf:Description rdf:about="https://doi.org/10.20372/nadre:4654"> <rdf:type rdf:resource="http://www.w3.org/ns/dcat#Dataset"/> <dct:type rdf:resource="http://purl.org/dc/dcmitype/Text"/> <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#anyURI">https://doi.org/10.20372/nadre:4654</dct:identifier> <foaf:page rdf:resource="https://doi.org/10.20372/nadre:4654"/> <dct:creator> <rdf:Description> <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/> <foaf:name>HABTU REDA</foaf:name> </rdf:Description> </dct:creator> <dct:title>PUBLIC BUS ARRIVAL TIME PREDICTION USING MACHINE LEARNING: IN CASE OF ADDIS ABABA</dct:title> <dct:publisher> <foaf:Agent> <foaf:name>Zenodo</foaf:name> </foaf:Agent> </dct:publisher> <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2021</dct:issued> <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2021-10-01</dct:issued> <owl:sameAs rdf:resource="https://nadre.ethernet.edu.et/record/4654"/> <adms:identifier> <adms:Identifier> <skos:notation rdf:datatype="http://www.w3.org/2001/XMLSchema#anyURI">https://nadre.ethernet.edu.et/record/4654</skos:notation> <adms:schemeAgency>url</adms:schemeAgency> </adms:Identifier> </adms:identifier> <dct:isVersionOf rdf:resource="https://doi.org/10.20372/nadre:4653"/> <dct:isPartOf rdf:resource="https://nadre.ethernet.edu.et/communities/aastu"/> <dct:isPartOf rdf:resource="https://nadre.ethernet.edu.et/communities/zenodo"/> <dct:description><p>Estimating public bus arrival times and delivering accurate arrival time information to<br> passengers are critical for making public transportation more user-friendly and thereby<br> increasing its competitiveness among various forms of transportation. However public bus<br> arrival time prediction remains major bottlenecks With traffic heterogeneity in composition and<br> diversity of vehicles, as well as a big pedestrian population combined with inadequate lane use,<br> predicting the arrival time of public buses at stations is a severe concern.. The main objective of<br> this study is to apply machine learning algorithms to predict bus arrival time. The data was<br> collected from Addis Ababa Sheger Public Bus Transport. Random Forest, Gradient Boosting,<br> Artificial Neural Network, K-Nearest Neighbors and Support Vector Machine algorithms are<br> applied to build the models and to compare and choose the best model to predict the bus arrival<br> time. After selecting the features and algorithms, different data preprocessing tasks like checking<br> outliers, missing values and data reduction are done. Finally, 140,000 instances of dataset are<br> used to train and build the model. The prepared dataset is partitioned into 90% training and 10%<br> testing set. Beginning Date, Beginning Time, End Date, Time Range, Mileage, Duration, Initial<br> latitude, Initial longitude, Final latitude, Final longitude, and End Time were used as input<br> features for developing the model. Based on the experiment result the Random Forest algorithm<br> achieved a better performance with R-squared score of 0.994, MAE of 0.812, RMSE of 3.780<br> and MSE of 14.28.</p></dct:description> <dct:accessRights rdf:resource="http://publications.europa.eu/resource/authority/access-right/PUBLIC"/> <dct:accessRights> <dct:RightsStatement rdf:about="info:eu-repo/semantics/openAccess"> <rdfs:label>Open Access</rdfs:label> </dct:RightsStatement> </dct:accessRights> <dcat:distribution> <dcat:Distribution> <dct:rights> <dct:RightsStatement rdf:about="http://www.opendefinition.org/licenses/cc-by"> <rdfs:label>Creative Commons Attribution</rdfs:label> </dct:RightsStatement> </dct:rights> <dcat:accessURL rdf:resource="https://doi.org/10.20372/nadre:4654"/> </dcat:Distribution> </dcat:distribution> <dcat:distribution> <dcat:Distribution> <dcat:accessURL rdf:resource="https://doi.org/10.20372/nadre:4654"/> <dcat:byteSize>647925</dcat:byteSize> <dcat:downloadURL rdf:resource="https://nadre.ethernet.edu.et/record/4654/files/f1042664640.pdf"/> <dcat:mediaType>application/pdf</dcat:mediaType> </dcat:Distribution> </dcat:distribution> </rdf:Description> </rdf:RDF>
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