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Innovative method for traffic data imputation based on convolutional neural network

机译：基于卷积神经网络的交通数据贷款创新方法

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摘要

The quality of traffic data is crucial for modern transportation planning and operations. However, data could be missing for various reasons. Hence, the data imputation approaches which aim at predicting/replacing the missing data or bad data have been considered very important. The traditional traffic data imputation approaches mainly focus on using different probability models or regression methods to impute data, and they only take limited temporal or spatial information as inputs. Thus, they are not very accurate especially for data with a high missing ratio. To overcome the weaknesses of previous approaches, this study proposes an innovative traffic data imputation method, which first transforms the raw data into spatial-temporal images and then implements a deep-learning method on the images. The key idea of this approach is developing a convolutional neural network (CNN)-based context encoder to reconstruct the complete image from the missing source. To the best of the authors' knowledge, this is the first time a CNN method has been incorporated for traffic data imputation. Experiments are conducted on three months of data from 256 loop detectors. Through comparison with two state-of-the-art approaches, the results indicate that this new approach increases the imputation accuracy greatly and has a stable error distribution.
机译：交通数据的质量对于现代运输规划和运营至关重要。但是，由于各种原因，可以缺少数据。因此，目的旨在预测/替换缺失的数据或坏数据的数据载体方法被认为非常重要。传统的交通数据归纳方法主要专注于使用不同的概率模型或回归方法来赋予数据，并且它们仅将时间或空间信息作为输入进行有限。因此，它们对具有高缺失比率的数据不是非常准确的。为了克服先前方法的弱点，本研究提出了一种创新的交通数据载体方法，它首先将原始数据转换为空间图像，然后在图像上实现深学习方法。这种方法的关键思想正在开发卷积神经网络（CNN）基础的上下文编码器，以重建来自缺失源的完整图像。据作者的知识中，这是第一次已向交通数据归档融入CNN方法。实验在256个环路探测器的三个月内进行。通过与两个最先进的方法进行比较，结果表明，这种新方法大大增加了归属精度并具有稳定的错误分布。

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