Smart mobility improvement: classifying commuter satisfaction in Sydney, Australia

The Danh Phan

doi:10.1145/3310986.3311021

Smart mobility improvement: classifying commuter satisfaction in Sydney, Australia

The Danh Phan^*

^*Corresponding author for this work

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

1 Citation (Scopus)

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 language	English
Title of host publication	ICMLSC 2019 - Proceedings of the 3rd International Conference on Machine Learning and Soft Computing
Place of Publication	New York, NY
Publisher	Association for Computing Machinery (ACM)
Pages	16-20
Number of pages	5
ISBN (Electronic)	9781450366120
DOIs	https://doi.org/10.1145/3310986.3311021
Publication status	Published - 2019
Event	3rd International Conference on Machine Learning and Soft Computing, ICMLSC 2019 - Da Lat, Viet Nam Duration: 25 Jan 2019 → 28 Jan 2019

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	3rd International Conference on Machine Learning and Soft Computing, ICMLSC 2019
Country/Territory	Viet Nam
City	Da Lat
Period	25/01/19 → 28/01/19

Keywords

Commuter satisfaction
Decision tree
Neural Network
Support vector machine

Access to Document

10.1145/3310986.3311021

Cite this

@inproceedings{e769292a6ea740db9e6adf056c31bdad,

title = "Smart mobility improvement: classifying commuter satisfaction in Sydney, Australia",

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.",

keywords = "Commuter satisfaction, Decision tree, Neural Network, Support vector machine",

author = "Phan, {The Danh}",

year = "2019",

doi = "10.1145/3310986.3311021",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery (ACM)",

pages = "16--20",

booktitle = "ICMLSC 2019 - Proceedings of the 3rd International Conference on Machine Learning and Soft Computing",

address = "United States",

note = "3rd International Conference on Machine Learning and Soft Computing, ICMLSC 2019 ; Conference date: 25-01-2019 Through 28-01-2019",

}

Phan, TD 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. ACM International Conference Proceeding Series, Association for Computing Machinery (ACM), New York, NY, pp. 16-20, 3rd International Conference on Machine Learning and Soft Computing, ICMLSC 2019, Da Lat, Viet Nam, 25/01/19. https://doi.org/10.1145/3310986.3311021

Smart mobility improvement: classifying commuter satisfaction in Sydney, Australia. / Phan, The Danh.
ICMLSC 2019 - Proceedings of the 3rd International Conference on Machine Learning and Soft Computing. New York, NY: Association for Computing Machinery (ACM), 2019. p. 16-20 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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AB - 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.

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KW - Decision tree

KW - Neural Network

KW - Support vector machine

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ER -

Smart mobility improvement: classifying commuter satisfaction in Sydney, Australia

Abstract

Publication series

Conference

Keywords

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