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Real-time accident detection: Coping with imbalanced data.

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Summary
This summary is machine-generated.

This study compared Support Vector Machine (SVM) and Probabilistic Neural Network (PNN) for real-time accident detection. PNN showed a higher detection rate, crucial for rapid traffic incident response.

Keywords:
Accident detectionMachine learningProbabilistic neural networkReal-time dataSupport vector machine

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Area of Science:

  • Transportation Engineering
  • Artificial Intelligence
  • Traffic Management

Background:

  • Road accidents cause significant traffic delays and user inconvenience.
  • Rapid accident detection is essential for mitigating these impacts.

Purpose of the Study:

  • To compare the performance of Support Vector Machine (SVM) and Probabilistic Neural Network (PNN) for detecting traffic accidents.
  • To evaluate the effectiveness of these models using varying time windows after accident occurrence.

Main Methods:

  • Utilized traffic condition data (weather, accident, loop detector) from the Eisenhower expressway.
  • Trained and tested seven models for SVM and PNN using data from 1 to 7 minutes post-accident.
  • Employed Synthetic Minority Oversampling Technique (SMOTE) to address imbalanced data.

Main Results:

  • PNN demonstrated a superior Detection Rate (DR) compared to SVM, despite SVM's higher overall accuracy.
  • Both models performed optimally with data from 5 minutes post-accident.
  • Models trained on data from 3-4 minutes post-accident offered a good balance between rapid detection and performance.

Conclusions:

  • PNN is more effective for accident detection due to its higher DR.
  • Early detection (3-4 minutes) is feasible with acceptable performance.
  • Upstream-downstream speed difference is a key factor for PNN's Time-To-Detection (TTD).