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Traffic accident duration prediction using multi-mode data and ensemble deep learning.

Jiaona Chen¹, Weijun Tao¹, Zhang Jing¹

¹Xi'an Shiyou University School of Electronic Engineering, Xi'an, 710065, China.

|February 21, 2024

Summary

This summary is machine-generated.

Predicting traffic accident duration on expressways is improved by using multi-modal data. A heterogeneous deep learning model, Word2Vec-BiGRU-CNN, effectively utilizes text features, enhancing prediction accuracy.

Keywords:

BiGRU-CNN Feature fusion Multi-mode data Pre-trained model Traffic accident duration Traffic safety

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Area of Science:

Traffic management
Artificial Intelligence
Data Science

Background:

Accurate prediction of traffic accident duration is crucial for effective traffic management and emergency response on expressways.
Existing research often overlooks the potential of multi-modal data, limiting the quantitative analysis of its impact on prediction performance.
Traffic accident data is inherently multi-modal, encompassing both structured and text-based information.

Purpose of the Study:

To propose a heterogeneous deep learning architecture that leverages multi-modal features for improved traffic accident duration prediction on expressways.
To quantitatively analyze the influence of multi-modal data on the performance of prediction models.
To identify optimal models and feature extraction techniques for traffic accident duration prediction.

Main Methods:

Extraction of six unique data modes from structured and text data.
Application of a hybrid deep learning approach to build classification models.
Rigorous analysis of multi-modal data influence using various deep learning models, including Word2Vec-BiGRU-CNN.
Evaluation using survey data from an expressway monitoring system in Shaanxi Province, China.

Main Results:

The Word2Vec-BiGRU-CNN model, utilizing text features, achieved a suitable and improved F1-score of 0.3648 for traffic accident duration prediction.
Extracted structured features from text data significantly enhanced the prediction performance of deep learning algorithms.
Conversely, these newly extracted text features negatively impacted the performance of conventional machine learning algorithms.

Conclusions:

Multi-modal data, particularly structured features derived from text, substantially improves deep learning-based traffic accident duration prediction.
The Word2Vec-BiGRU-CNN model demonstrates effectiveness in leveraging text features for this task.
Feature processing and extraction from text data remain complex challenges in traffic accident duration prediction.