Li Hao, Chen Linbing
School of Civil Engineering Architecture and the Environment, Hubei University of Technology, Wuhan, China.
Key Laboratory of intelligent Health Perception and ecological restoration of rivers and Lakes, Ministry of education, Hubei University of Technology, Wuhan, China.
PLoS One. 2025 May 2;20(5):e0320656. doi: 10.1371/journal.pone.0320656. eCollection 2025.
With the acceleration of urbanization and the increase in traffic volume, frequent traffic accidents have significantly impacted public safety and socio-economic conditions. Traditional methods for predicting traffic accidents often overlook spatiotemporal features and the complexity of traffic networks, leading to insufficient prediction accuracy in complex traffic environments. To address this, this paper proposes a deep learning model that combines Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Graph Neural Networks (GNN) for traffic accident risk prediction using vehicle spatiotemporal trajectory data. The model extracts spatial features such as vehicle speed, acceleration, and lane-changing distance through CNN, captures temporal dependencies in trajectories using LSTM, and effectively models the complex spatial structure of traffic networks with GNN, thereby improving prediction accuracy.The main contributions of this paper are as follows: First, an innovative combined model is proposed, which comprehensively considers spatiotemporal features and road network relationships, significantly improving prediction accuracy. Second, the model's strong generalization ability across multiple traffic scenarios is validated, enhancing the accuracy of traditional prediction methods. Finally, a new technical approach is provided, offering theoretical support for the implementation of real-time traffic accident warning systems. Experimental results demonstrate that the model can effectively predict accident risks in various complex traffic scenarios, providing robust support for intelligent traffic management and public safety.
随着城市化进程的加速和交通流量的增加，频繁发生的交通事故对公共安全和社会经济状况产生了重大影响。传统的交通事故预测方法往往忽略了时空特征和交通网络的复杂性，导致在复杂交通环境下预测准确率不足。为了解决这一问题，本文提出了一种深度学习模型，该模型结合卷积神经网络（CNN）、长短期记忆网络（LSTM）和图神经网络（GNN），利用车辆时空轨迹数据进行交通事故风险预测。该模型通过CNN提取车辆速度、加速度和变道距离等空间特征，使用LSTM捕捉轨迹中的时间依赖性，并利用GNN有效地对交通网络的复杂空间结构进行建模，从而提高预测准确率。本文的主要贡献如下：第一，提出了一种创新的组合模型，该模型综合考虑了时空特征和道路网络关系，显著提高了预测准确率。第二，验证了该模型在多种交通场景下的强泛化能力，提高了传统预测方法的准确性。最后，提供了一种新的技术方法，为实时交通事故预警系统的实施提供了理论支持。实验结果表明，该模型能够有效地预测各种复杂交通场景下的事故风险，为智能交通管理和公共安全提供有力支持。
