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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Functional Objects in Urban Walking Environments and Pedestrian Trajectory Modelling.

Andrew Kwok Fai Lui1, Yin Hei Chan1, Kevin Hung1

  • 1School of Science and Technology, Hong Kong Metropolitan University, Hong Kong SAR, China.

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|July 11, 2023
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Summary
This summary is machine-generated.

Researchers developed a new method to predict pedestrian movement by considering how people interact with urban functional objects. This approach improves trajectory prediction accuracy in complex city environments.

Keywords:
deep learningfunctional objectspedestrian movement modellingpedestrian trajectoryrecurrent neural networksurban environments

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Area of Science:

  • Urban planning and design
  • Computer vision and artificial intelligence
  • Human-computer interaction

Background:

  • Urban environments feature functional objects like shops and kiosks that influence pedestrian activity.
  • Pedestrian trajectory modeling is complex due to social dynamics and object interactions.
  • Existing data-driven methods rarely incorporate functional objects into movement predictions.

Purpose of the Study:

  • To address the knowledge gap in pedestrian trajectory modeling by highlighting the significance of pedestrian-object relations.
  • To introduce a novel method that integrates functional objects into urban movement prediction.
  • To empirically demonstrate the impact of considering these relations on prediction accuracy.

Main Methods:

  • Proposed the pedestrian-object relation guided trajectory prediction (PORTP) method.
  • Utilized a dual-layer architecture comprising a pedestrian-object relation predictor and specialized trajectory prediction models.
  • Focused on modeling the diverse interactions between pedestrians and functional objects in urban settings.

Main Results:

  • The inclusion of pedestrian-object relations significantly enhanced the accuracy of pedestrian trajectory predictions.
  • The PORTP method demonstrated superior performance compared to existing approaches that do not consider object interactions.
  • Experimental findings validated the importance of incorporating object-centric information in movement modeling.

Conclusions:

  • Pedestrian-object relations are crucial for accurate urban pedestrian trajectory modeling.
  • The PORTP method offers a robust baseline for future research in context-aware movement prediction.
  • This study establishes an empirical foundation for understanding the role of functional objects in shaping urban mobility patterns.