Wen Wu, Xu Dongliang, Xia Yang
ChongQing Technology And Business Institute, ChongQing, China.
ChongQing Open University, ChongQing, China.
PeerJ Comput Sci. 2023 Jun 6;9:e1411. doi: 10.7717/peerj-cs.1411. eCollection 2023.
At present, China is moving towards the direction of "Industry 4.0". The development of the automobile industry, especially intelligent automobiles, is in full swing, which brings great convenience to people's life and travel. However, at the same time, urban traffic pressure is also increasingly prominent, and the situation of traffic congestion and traffic safety is not optimistic. In this context, the Internet of Vehicles (also known as "IoV") opens up a new way to relieve urban traffic pressure. Therefore, in order to further optimize the road network traffic conditions in the IoV environment, this research focuses on the traffic flow prediction algorithm on the basis of deep learning to enhance traffic efficiency and safety. First, the study investigates the short-time traffic flow prediction by combining the characteristics of the IoV environment. To address the issues that existing algorithms cannot automatically extract data features and the model expression capability is weak, the study chooses to build a deep neural network using GRU model in deep learning for short-time traffic flow prediction, thereby improving the prediction accuracy of algorithm. Secondly, a fine-grained traffic flow statistics approach suitable for the IoV situation is suggested in accordance with the deep learning model that was built. The algorithm sends the vehicle characteristic data obtained through GRU model training into the fine-grained traffic flow statistics algorithm, so as to realize the statistics of traffic information of various types of vehicles. The advantage of this algorithm is that it can well count the traffic flow of multiple lanes, so as to better predict the current traffic status and achieve traffic optimization. Finally, the IoV environment is constructed to confirm the effectiveness of the prediction model. The prediction results prove that the new algorithm has good performance in traffic flow statistics in different scenarios.
目前，中国正朝着“工业4.0”的方向发展。汽车工业，尤其是智能汽车的发展如火如荼，给人们的生活和出行带来了极大便利。然而，与此同时，城市交通压力也日益凸显，交通拥堵和交通安全状况不容乐观。在此背景下，车联网（也称为“IoV”）为缓解城市交通压力开辟了一条新途径。因此，为了进一步优化车联网环境下的路网交通状况，本研究聚焦于基于深度学习的交通流预测算法，以提高交通效率和安全性。首先，该研究结合车联网环境的特点对短时交通流预测进行了调查。为了解决现有算法无法自动提取数据特征以及模型表达能力较弱的问题，该研究选择在深度学习中使用GRU模型构建深度神经网络用于短时交通流预测，从而提高算法的预测精度。其次，根据所构建的深度学习模型，提出了一种适用于车联网情况的细粒度交通流统计方法。该算法将通过GRU模型训练得到的车辆特征数据送入细粒度交通流统计算法中，从而实现对各类车辆交通信息的统计。该算法的优点在于能够很好地统计多车道的交通流，从而更好地预测当前交通状况并实现交通优化。最后，构建车联网环境以验证预测模型的有效性。预测结果证明，新算法在不同场景下的交通流统计中具有良好的性能。
