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Real-Time Traffic Risk Detection Model Using Smart Mobile Device.

Soyoung Park1, Homin Han2, Byeong-Su Kim3

  • 1Department of Software, Konkuk University, Seoul 05029, Korea. soyoungpark@konkuk.ac.kr.

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|November 2, 2018
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Summary
This summary is machine-generated.

This study introduces a real-time traffic risk detection system using smart mobile devices. It analyzes driver deceleration patterns and vehicle headway to enhance road safety for all vehicles.

Keywords:
deceleration patternmachine learningreal-time servicesmart mobile devicetraffic risk detection

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Road Safety Engineering

Background:

  • Road safety is paramount, necessitating advanced technologies for real-time hazard identification.
  • Current systems often lack the ability to predict or rapidly communicate dangerous driving situations.

Purpose of the Study:

  • To develop a real-time, deceleration pattern-based traffic risk detection system using smart mobile devices.
  • To enhance road safety by enabling quick information sharing of dangerous situations among nearby vehicles.

Main Methods:

  • Utilized machine learning (neural network, random forest, clustering) for deceleration pattern analysis.
  • Developed a practical vehicle detection method using road shadows and taillights to estimate headway distance.
  • Proposed two decision models and three improvement techniques for continuous enhancement of the traffic risk detection system.

Main Results:

  • The system accurately detects dangerous driving situations by analyzing deceleration patterns and headway distance.
  • Performance analysis was conducted using real-world driving data from Seoul city and the Gyeongbu expressway.
  • Evaluated the effectiveness of different decision models and improvement techniques for optimal traffic risk detection.

Conclusions:

  • The proposed system offers a viable solution for real-time traffic risk detection using accessible smart mobile devices.
  • The machine learning approach effectively identifies hazardous driving scenarios, contributing to improved road safety.
  • Continuous enhancement strategies ensure the model's adaptability and sustained performance in diverse driving conditions.