Using a Separable Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction

Arnold Loaiza, J. Herrera, Luis Mantilla
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引用次数: 3

Abstract

This paper proposes the reduction of the convergence time on a Convolutional Neural Network (CNN) method for traffic speed prediction, without reducing the performance of speed prediction method. The proposed method contains two procedures: The first one is to convert the traffic network data to images; in this case the speed variable will be transformed. The second step of the procedure presents a modification of the CNN method for speed prediction in which a separable convolution is used to reduce the number of parameters. This separable convolution helps to reducing the convergence time of speed predictions for large-scale transportation network. The proposal is evaluated with real data from the Caltrans Performance Measurement System (PeMS), obtained through sensors. The results show that Separable Convolutional Neural Network (SCNN) reduces convergence time of CNN method without losing the performance of the predictions of traffic speed in a large-scale transportation network.
基于可分离卷积神经网络的大规模交通网络速度预测
本文提出在不降低车速预测方法性能的前提下,缩短卷积神经网络(CNN)方法的收敛时间。该方法包括两个步骤:首先将交通网络数据转换为图像;在这种情况下,速度变量将被转换。该程序的第二步提出了对CNN速度预测方法的修改,其中使用可分离卷积来减少参数的数量。这种可分离卷积有助于减少大规模交通网络速度预测的收敛时间。通过传感器获得的Caltrans性能测量系统(PeMS)的真实数据对该方案进行了评估。结果表明,可分离卷积神经网络(SCNN)在不影响大规模交通网络中交通速度预测性能的前提下,缩短了CNN方法的收敛时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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