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Predicting Personal Taxi Destinations Using Artificial Neural Networks
Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Taxi Stockholm is a Swedish taxi company which would like to improve their mobile phone application with a destination prediction feature. This thesis has created an algo- rithm which predicts a destination to which a taxi customer would like to go. The problem is approached using the KDD process and data mining methods. A dataset consisting of previous taxi rides is cleaned, transformed, and then used to evaluate the performance of three machine learning models. More specifically a neural network model paired with K- Means clustering, a random forest model, and a k-nearest neighbour model. The results show that the models that were developed in this thesis could be used as a first step in a destination prediction system. The results also show that personal data increase the accu- racy of the neural network model and that there exists a threshold for how much personal information is needed to increase the performance.

Place, publisher, year, edition, pages
2018. , p. 54
Keywords [en]
Machine Learning, Neural Networks, Destination prediction, Data mining
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:liu:diva-148427ISRN: LIU-IDA/LITH-EX-A--18/003--SEOAI: oai:DiVA.org:liu-148427DiVA, id: diva2:1215899
External cooperation
Bontouch
Subject / course
Computer Engineering
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Available from: 2018-06-20 Created: 2018-06-10 Last updated: 2018-06-20Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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  • de-DE
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  • en-US
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  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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