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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Analyzing vehicle-pedestrian interactions: Combining data cube structure and predictive collision risk estimation

Byeongjoon Noh1, Hansaem Park2, Hwasoo Yeo2

  • 1Applied Science Research Institute, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseung-gu, Daejeon, Republic of Korea.

Accident; Analysis and Prevention
|December 20, 2021
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Summary
This summary is machine-generated.

This study introduces a novel pedestrian safety system using predictive collision risk (PCR) models and online analytical processing (OLAP) to assess crosswalk safety. The framework analyzes traffic video data to proactively identify and mitigate risks for vulnerable road users.

Keywords:
Data cube modelMulti-dimensional analysisPedestrian safetyPredictive collision riskTrajectory prediction

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Area of Science:

  • Traffic Safety Engineering
  • Computer Vision
  • Data Analytics

Background:

  • Road traffic accidents pose a significant threat, especially to vulnerable road users (VRUs) like pedestrians.
  • Proactive accident prevention and risk assessment are crucial for enhancing road safety.
  • Existing safety measurements often rely on post-accident analysis, necessitating real-time, predictive solutions.

Purpose of the Study:

  • To introduce a novel analytical framework for assessing crosswalk safety by analyzing vehicle and pedestrian behaviors.
  • To develop a system that can predict and categorize collision risks in real-time without actual collisions.
  • To provide decision-makers with valuable data for improving pedestrian safety and understanding traffic dynamics.

Main Methods:

  • Utilizing a data cube structure integrating Long Short-Term Memory (LSTM)-based predictive collision risk (PCR) estimation and online analytical processing (OLAP).
  • Automated extraction of behavioral features from traffic video footages.
  • Multi-dimensional analysis of vehicle and pedestrian movements, environmental features, and risk levels.

Main Results:

  • A four-level risk categorization system: "relatively safe," "caution," "warning," and "danger."
  • Analysis of movement patterns based on road environments and the correlation between risk levels and vehicle speeds.
  • Demonstrated feasibility and applicability through implementation on actual CCTV data in Osan City, Republic of Korea.

Conclusions:

  • The proposed framework effectively assesses crosswalk safety by analyzing behavioral features and predicting collision risks.
  • It provides actionable insights for urban planners and safety administrators to improve pedestrian safety and prevent future accidents.
  • The system enhances understanding of proactive behaviors of vehicles and pedestrians near crosswalks.