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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

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Traffic accident detection and condition analysis based on social networking data.

Farman Ali¹, Amjad Ali², Muhammad Imran³

¹Department of Software, Sejong University, Seoul, South Korea.

Accident; Analysis and Prevention

|January 18, 2021

Summary

This summary is machine-generated.

This study introduces a new framework for real-time traffic accident detection and condition analysis using ontology and latent Dirichlet allocation (OLDA) and bidirectional long short-term memory (Bi-LSTM). The system achieves 97% accuracy, outperforming existing methods for improved traffic safety.

Keywords:

Bi-LSTM Ontology Traffic accident analysis Traffic accident detection Traffic monitoring system

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Area of Science:

Artificial Intelligence
Data Science
Transportation Engineering

Background:

Accurate traffic accident detection and condition analysis are crucial for traffic flow restoration and reducing casualties.
Existing sensor-based systems have limitations including long detection times and high false-alarm rates.
Social networking data presents challenges due to its unstructured nature and dynamic topics, hindering traditional machine learning approaches.

Purpose of the Study:

To propose a novel social network-based framework for real-time traffic accident detection and condition analysis.
To leverage ontology and latent Dirichlet allocation (OLDA) for topic modeling and event recognition.
To enhance traffic event condition analysis using sentiment analysis and bidirectional long short-term memory (Bi-LSTM).

Main Methods:

A query-based search engine collects social network data, followed by a data preprocessing module for structuring.
The proposed OLDA method automatically labels sentences for traffic information identification and ontology-based event recognition detects traffic events.
Sentiment analysis determines traffic event polarity, and FastText with Bi-LSTM and softmax regression are trained for detection and analysis.

Main Results:

The proposed framework effectively detects traffic events and analyzes their conditions from social network data.
The OLDA and Bi-LSTM models demonstrated superior performance compared to existing topic modeling and classification methods.
The system achieved a high accuracy of 97% in traffic event detection and condition analysis.

Conclusions:

The developed social network-based framework offers a more efficient and accurate solution for real-time traffic accident detection and condition analysis.
The integration of OLDA and Bi-LSTM significantly improves the extraction of valuable information from unstructured social media data.
This approach provides a robust foundation for enhancing traffic safety and management systems.