A Hybrid Model for Traffic Incident Detection based on Generative Adversarial Networks and Transformer Model

TL;DR

This paper introduces a hybrid model combining Transformer and GANs to improve traffic incident detection by expanding and balancing datasets, leading to enhanced detection performance in real-world scenarios.

Contribution

The paper proposes a novel hybrid model integrating Transformer and GANs to address dataset limitations and improve traffic incident detection accuracy.

Findings

Transformer outperforms baseline models in detection accuracy.

GANs effectively expand and balance datasets.

Hybrid model improves overall detection performance.

Abstract

In addition to enhancing traffic safety and facilitating prompt emergency response, traffic incident detection plays an indispensable role in intelligent transportation systems by providing real-time traffic status information. This enables the realization of intelligent traffic control and management. Previous research has identified that apart from employing advanced algorithmic models, the effectiveness of detection is also significantly influenced by challenges related to acquiring large datasets and addressing dataset imbalances. A hybrid model combining transformer and generative adversarial networks (GANs) is proposed to address these challenges. Experiments are conducted on four real datasets to validate the superiority of the transformer in traffic incident detection. Additionally, GANs are utilized to expand the dataset and achieve a balanced ratio of 1:4, 2:3, and 1:1. The proposed model is evaluated against the baseline model. The results demonstrate that the proposed model enhances the dataset size, balances the dataset, and improves the performance of traffic incident detection in various aspects.