Towards the Design of Smart Vehicular Traffic Flow Prediction

A. Boukerche, Jiahao Wang
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引用次数: 4

Abstract

Thanks to the fast development of computing hardware and Machine Learning-based (ML) model, many impressive prediction models have been proposed under the topic of traffic flow prediction. While ML models highly improve the accuracy of the prediction system, it has higher time consumption on the training phase when being applied to a large traffic network, compared to traditional time-series models. The other thing we should consider when predicting the traffic flow in a large traffic network is to utilize the spatial correlation among the detectors. To solve above problems, we will provide a traffic flow prediction solution in this paper. The solution has three parts: a hybrid prediction model based on Graph Convolutional Network (GCN) and Recurrent Neural Network (RNN), which can extract spatial-temporal features from dataset; a prediction strategy for multi-step prediction; an efficient training strategy for prediction on large-scale network.
面向智能车辆交通流预测的设计
由于计算硬件和基于机器学习的模型的快速发展,在交通流量预测的主题下提出了许多令人印象深刻的预测模型。虽然ML模型极大地提高了预测系统的准确性,但与传统的时间序列模型相比,在应用于大型交通网络时,它在训练阶段的时间消耗更高。在预测大型交通网络中的交通流时,我们应该考虑的另一件事是利用检测器之间的空间相关性。为了解决上述问题,本文将提供一种交通流预测方案。该方案包括三个部分:基于图卷积网络(GCN)和递归神经网络(RNN)的混合预测模型,该模型可以从数据集中提取时空特征;多步预测的预测策略一种有效的大规模网络预测训练策略。
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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