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Area of Science:

  • Transportation Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Station delays significantly reduce public transport commercial speed.
  • Manual data collection for passenger flow is time-consuming and prone to errors.
  • Existing video surveillance systems offer potential for automated analysis.

Purpose of the Study:

  • To develop an automated method for collecting and analyzing passenger data at public transport stations.
  • To improve the accuracy and efficiency of data used for traffic modeling and experimentation.
  • To leverage intelligent sensing and deep learning for enhanced public transport operations.

Main Methods:

  • Utilized a new public video dataset from real-scale laboratory recordings.
  • Annotated video data with head locations to create a ground-truth dataset.
  • Trained and evaluated deep learning detection and tracking algorithms for passenger counting.

Main Results:

  • Achieved a mean accuracy of 92% in counting passengers boarding and alighting.
  • Demonstrated a standard deviation of less than 0.15% on unseen video sequences.
  • Successfully exploited intelligent sensing for automated passenger flow analysis.

Conclusions:

  • Automated passenger counting using deep learning significantly enhances data collection for transport analysis.
  • The developed method offers a scalable solution to reduce station delays and improve public transport efficiency.
  • Intelligent sensing provides a viable approach to overcome limitations of traditional data collection methods.