Department of Industrial and Systems Engineering, San Jose State University, San Jose, CA 95192, USA.
Sensors (Basel). 2022 Oct 20;22(20):7994. doi: 10.3390/s22207994.
Accurate predictive modeling of traffic flow is critically important as it allows transportation users to make wise decisions to circumvent traffic congestion regions. The advanced development of sensing technology makes big data more affordable and accessible, meaning that data-driven methods have been increasingly adopted for traffic flow prediction. Although numerous data-driven methods have been introduced for traffic flow predictions, existing data-driven methods cannot consider the correlation of the extracted high-dimensional features and cannot use the most relevant part of the traffic flow data to make predictions. To address these issues, this work proposes a decoder convolutional LSTM network, where the convolutional operation is used to consider the correlation of the high-dimensional features, and the LSTM network is used to consider the temporal correlation of traffic flow data. Moreover, the multi-head attention mechanism is introduced to use the most relevant portion of the traffic data to make predictions so that the prediction performance can be improved. A traffic flow dataset collected from the Caltrans Performance Measurement System (PeMS) database is used to demonstrate the effectiveness of the proposed method.
准确的交通流预测建模至关重要，因为它可以让交通用户做出明智的决策，避开交通拥堵区域。先进的传感技术发展使得大数据更加实惠和易得，这意味着数据驱动的方法已越来越多地被应用于交通流预测。尽管已经引入了许多数据驱动的方法来进行交通流预测，但现有的数据驱动方法不能考虑提取的高维特征的相关性，也不能使用交通流数据中最相关的部分进行预测。为了解决这些问题，本工作提出了一种解码器卷积长短期记忆网络，其中卷积操作用于考虑高维特征的相关性，而长短期记忆网络用于考虑交通流数据的时间相关性。此外，引入多头注意力机制来使用交通数据中最相关的部分进行预测，从而提高预测性能。使用从加利福尼亚州交通厅性能测量系统 (PeMS) 数据库中收集的交通流数据集来验证所提出方法的有效性。
