TRAVEL MODE CLASSIFICATION BASED ON CELLULAR DATA

Yu Chiun Chiou, Ying Chen Lai, Chih Wei Hsieh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Cellular data could be one of the primary data sources of transportation planning because of the rapid growth in triangulation and location techniques. This study aims to develop a classification algorithm to accurately predict the mode used by the mobile user by using genetic fuzzy logic control (GFLC) model. Four state variables are chosen, including trip length, travel speed, bus trajectory similarity, and rail trajectory similarity. The consequent part is the mode classification among four modes: bus, rail, private vehicle, and non-motorized vehicle. To facilitate the training and validation of the proposed model, this study invites 50 volunteers to join a 30-day travel diary survey. The accuracy rate based on 5-fold cross-validation is between 74% to 86%. For the prediction performance of the proposed model, the highest accuracy ratio is rail, followed by private vehicles and non-motorized modes. The correct prediction rate of bus is the lowest.

Original languageEnglish
Title of host publicationProceedings of the 25th International Conference of Hong Kong Society for Transportation Studies, HKSTS 2021
Subtitle of host publicationSustainable Mobility
EditorsRyan C.P. Wong, Jiangping Zhou, W.Y. Szeto
PublisherHong Kong Society for Transportation Studies Limited
Pages393-400
Number of pages8
ISBN (Electronic)9789881581495
StatePublished - 2021
Event25th International Conference of Hong Kong Society for Transportation Studies: Sustainable Mobility, HKSTS 2021 - Hong Kong, Hong Kong
Duration: 9 Dec 202110 Dec 2021

Publication series

NameProceedings of the 25th International Conference of Hong Kong Society for Transportation Studies, HKSTS 2021: Sustainable Mobility

Conference

Conference25th International Conference of Hong Kong Society for Transportation Studies: Sustainable Mobility, HKSTS 2021
Country/TerritoryHong Kong
CityHong Kong
Period9/12/2110/12/21

Keywords

  • Cellular data
  • Genetic fuzzy logic control
  • Mode classification

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