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

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.

• Record URL:
  • https://doi.org/10.1080/23249935.2022.2118558
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/23249935
• Supplemental Notes:
  • © 2022 Hong Kong Society for Transportation Studies Limited 2022. Abstract reprinted with permission of Taylor & Francis.
• Authors:
  • Chen, Huey-Kuo
  • Ho, Hsiao-Ching
  • Wu, Luo-Yu
  • Lee, Ian
  • Chou, Huey-Wen
• Publication Date: 2024-1

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: 2118558
• Serial:
  • Transportmetrica A: Transport Science
  • Volume: 20
  • Issue Number: 1
  • Publisher: Taylor & Francis
  • ISSN: 2324-9935
  • EISSN: 2324-9943
  • Serial URL: http://www.tandfonline.com/loi/ttra21

Subject/Index Terms

• TRT Terms: Cellular networks; Machine learning; Peak hour traffic; Vector analysis; Vehicle detectors
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01905810
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 26 2024 10:02AM