论文标题
DALC：分布式自动LSTM自定义，用于精细颗粒的交通速度预测
DALC: Distributed Automatic LSTM Customization for Fine-Grained Traffic Speed Prediction
论文作者
论文摘要
在过去的十年中，已经引入了几种用于短期交通预测的方法。但是，为大规模运输网络提供细粒度的流量预测，在该网络上，在地理上部署了许多探测器以收集流量数据仍然是一个空旷的问题。为了解决这个问题，在本文中，我们制定了将单个检测器定制为有限的马尔可夫决策过程的LSTM模型的问题，然后引入自动LSTM自定义（ALC）算法以自动自动定制单个检测器的LSTM模型，以使相应的预测准确性尽可能令人满意，并且可以尽可能地消耗时光。基于ALC算法，我们引入了一种称为分布式自动LSTM自定义（DALC）的分布式方法，以自定义大规模运输网络中每个检测器的LSTM模型。我们的实验表明，与Apache Spark Mllib提供的几种方法相比，DALC提供了更高的预测准确性。
Over the past decade, several approaches have been introduced for short-term traffic prediction. However, providing fine-grained traffic prediction for large-scale transportation networks where numerous detectors are geographically deployed to collect traffic data is still an open issue. To address this issue, in this paper, we formulate the problem of customizing an LSTM model for a single detector into a finite Markov decision process and then introduce an Automatic LSTM Customization (ALC) algorithm to automatically customize an LSTM model for a single detector such that the corresponding prediction accuracy can be as satisfactory as possible and the time consumption can be as low as possible. Based on the ALC algorithm, we introduce a distributed approach called Distributed Automatic LSTM Customization (DALC) to customize an LSTM model for every detector in large-scale transportation networks. Our experiment demonstrates that the DALC provides higher prediction accuracy than several approaches provided by Apache Spark MLlib.
