School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China.
Int J Environ Res Public Health. 2022 Sep 1;19(17):10903. doi: 10.3390/ijerph191710903.
Traffic accidents causing nonrecurrent congestion and road traffic injuries seriously affect public safety. It is helpful for traffic operation and management to predict the duration of traffic incidents. Most of the previous studies have been in a certain area with a single data source. This paper proposes a hybrid deep learning model based on multi-source incomplete data to predict the duration of countrywide traffic incidents in the U.S. The text data from the natural language description in the model were parsed by the latent Dirichlet allocation (LDA) topic model and input into the bidirectional long short-term memory (Bi-LSTM) and long short-term memory (LSTM) hybrid network together with sensor data for training. Compared with the four benchmark models and three state-of-the-art algorithms, the RMSE and MAE of the proposed method were the lowest. At the same time, the proposed model performed best for durations between 20 and 70 min. Finally, the data acquisition was defined as three phases, and a phased sequential prediction model was proposed under the condition of incomplete data. The results show that the model performance was better with the update of variables.
交通事故导致的非经常性拥堵和道路交通伤害严重影响公共安全。预测交通事故持续时间有助于交通运行和管理。以往的研究大多集中在某个具有单一数据源的区域。本文提出了一种基于多源不完整数据的混合深度学习模型，用于预测美国全国范围内交通事故的持续时间。模型中的自然语言描述的文本数据通过潜在狄利克雷分配（LDA）主题模型进行解析，并与传感器数据一起输入双向长短期记忆（Bi-LSTM）和长短期记忆（LSTM）混合网络进行训练。与四个基准模型和三种最先进的算法相比，所提出方法的均方根误差（RMSE）和平均绝对误差（MAE）最低。同时，所提出的模型在 20 到 70 分钟之间的持续时间内表现最佳。最后，定义了数据采集的三个阶段，并在不完整数据的条件下提出了一个分阶段的顺序预测模型。结果表明，随着变量的更新，模型性能更好。
