Two-stage procedure for transportation mode detection based on sighting data

Huey Kuo Chen, Hsiao Chingki Ho, Luo Yu Wu, Ian Lee, Huey Wen Chou

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


The data required for transportation applications can be retrieved from mobile phones without the necessity of additional infrastructure. Thus, we propose a procedure that involves two stages–data preprocessing and transportation mode detection–for detecting the transportation mode (i.e., car and bus) on the basis of sighting data. In the data preprocessing stage, two detection rules are used for eliminating oscillations that occur when a mobile phone intermittently switches between cell towers instead of connecting to the nearest cell tower. In the transportation mode detection stage, two supervised machine learning methods, namely support vector machine (SVM) and a deep neural network (DNN), are used to detect transportation modes. Experimental results indicated SVM achieved a higher accuracy (96.49%) in transport mode detection than did the DNN (69.65%) during peak hours. Moreover, travel time and starting time of a trip were identified as critical features affecting the accuracy of transportation mode detection.

Original languageEnglish
JournalTransportmetrica A: Transport Science
StateAccepted/In press - 2022


  • deep learning
  • Mobile phone data
  • oscillation phenomenon
  • support vector machine
  • transportation mode detection


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