论文标题
基于自动LSTM自定义和共享的大规模运输网络的分布式良好的交通速度预测
Distributed Fine-Grained Traffic Speed Prediction for Large-Scale Transportation Networks based on Automatic LSTM Customization and Sharing
论文作者
论文摘要
在过去的十年中，短期交通速度预测一直是一个重要的研究主题，并且已经引入了许多方法。但是，对于部署了许多交通探测器的大规模运输网络提供了细粒度，准确，高效的交通速度预测，尚未得到很好的研究。在本文中，我们提出了DISTPRE，这是大规模运输网络的分布式良好的交通速度预测方案。为了实现细粒度和准确的交通速度预测，DistPre用适合检测器的适当的超参数配置自定义了长期记忆（LSTM）模型。为了使这种自定义过程有效且适用于大规模运输网络，DistPre在一组计算节点上进行LSTM自定义，并允许在不同检测器之间共享任何受过训练的LSTM模型。如果检测器观察到与另一种类似的流量模式，则DISTPRE直接在两个检测器之间共享现有的LSTM模型，而不是每个检测器自定义LSTM模型。基于从加利福尼亚的高速公路I5-N收集的流量数据进行了实验，以评估DISTPRE的性能。结果表明，DISTPRE为大规模运输网络提供了时间效率的LSTM自定义和准确的精细粒度交通速度预测。
Short-term traffic speed prediction has been an important research topic in the past decade, and many approaches have been introduced. However, providing fine-grained, accurate, and efficient traffic-speed prediction for large-scale transportation networks where numerous traffic detectors are deployed has not been well studied. In this paper, we propose DistPre, which is a distributed fine-grained traffic speed prediction scheme for large-scale transportation networks. To achieve fine-grained and accurate traffic-speed prediction, DistPre customizes a Long Short-Term Memory (LSTM) model with an appropriate hyperparameter configuration for a detector. To make such customization process efficient and applicable for large-scale transportation networks, DistPre conducts LSTM customization on a cluster of computation nodes and allows any trained LSTM model to be shared between different detectors. If a detector observes a similar traffic pattern to another one, DistPre directly shares the existing LSTM model between the two detectors rather than customizing an LSTM model per detector. Experiments based on traffic data collected from freeway I5-N in California are conducted to evaluate the performance of DistPre. The results demonstrate that DistPre provides time-efficient LSTM customization and accurate fine-grained traffic-speed prediction for large-scale transportation networks.
