Smart mobility improvement

classifying commuter satisfaction in Sydney, Australia

The Danh Phan*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

This paper attempts to derive useful insights from commuter feedback data. It investigates transportation mode, commuting density and peak hours in Sydney, Australia. Machine Learning techniques are then applied to analyse traveler satisfaction to discover useful models for classification. Experiments demonstrate that each method has its competitive advantages over others, and no approach completely outperform other methods in terms of accuracy, performance, and interpretability. It is suggested that one could use Support Vector Machine to classify satisfied commuters, and/or utilize Neural Network to classify unsatisfied travelers.

Original languageEnglish
Title of host publicationICMLSC 2019 - Proceedings of the 3rd International Conference on Machine Learning and Soft Computing
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery (ACM)
Pages16-20
Number of pages5
ISBN (Electronic)9781450366120
DOIs
Publication statusPublished - 2019
Event3rd International Conference on Machine Learning and Soft Computing, ICMLSC 2019 - Da Lat, Viet Nam
Duration: 25 Jan 201928 Jan 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference3rd International Conference on Machine Learning and Soft Computing, ICMLSC 2019
CountryViet Nam
CityDa Lat
Period25/01/1928/01/19

Keywords

  • Commuter satisfaction
  • Decision tree
  • Neural Network
  • Support vector machine

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  • Cite this

    Phan, T. D. (2019). Smart mobility improvement: classifying commuter satisfaction in Sydney, Australia. In ICMLSC 2019 - Proceedings of the 3rd International Conference on Machine Learning and Soft Computing (pp. 16-20). (ACM International Conference Proceeding Series). New York, NY: Association for Computing Machinery (ACM). https://doi.org/10.1145/3310986.3311021