论文标题
GC-GRU-N用于使用循环检测器数据的流量预测
GC-GRU-N for Traffic Prediction using Loop Detector Data
论文作者
论文摘要
由于流量特征显示随机非线性时空依赖性，因此流量预测是一项艰巨的任务。本文开发了一个图形卷积复发单元（GC GRU N）网络，以提取基本的时空特征。我们使用15分钟内汇总的西雅图环路检测器数据，并通过空间和时间重新构架问题。比较模型性能O基准模型；历史平均水平，长期内存（LSTM）和变压器。提出的模型以最快的推理时间和非常接近的第一名（变形金刚）排名第二。我们的模型还达到了一个比变形金刚快六倍的运行时间。最后，我们使用训练时间，推理时间，MAPE，MAE和RMSE等指标进行了对模型和可用基准的比较研究。还为每个训练有素的模型分析了空间和时间方面。
Because traffic characteristics display stochastic nonlinear spatiotemporal dependencies, traffic prediction is a challenging task. In this paper develop a graph convolution gated recurrent unit (GC GRU N) network to extract the essential Spatio temporal features. we use Seattle loop detector data aggregated over 15 minutes and reframe the problem through space and time. The model performance is compared o benchmark models; Historical Average, Long Short Term Memory (LSTM), and Transformers. The proposed model ranked second with the fastest inference time and a very close performance to first place (Transformers). Our model also achieves a running time that is six times faster than transformers. Finally, we present a comparative study of our model and the available benchmarks using metrics such as training time, inference time, MAPE, MAE and RMSE. Spatial and temporal aspects are also analyzed for each of the trained models.
