PREDICTION OF TRAFFIC ACCIDENT LIKELIHOOD ON INTERCITY EXPRESSWAY BY CONVOLUTIONAL NEURAL NETWORK
PREDICTION OF TRAFFIC ACCIDENT LIKELIHOOD ON INTERCITY EXPRESSWAY BY CONVOLUTIONAL NEURAL NETWORK
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卷积神经网络预测城际高速公路交通事故概率
DOI：
10.11532/jsceiii.1.1_11
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Jian XING
中科院分区：
文献类型：
--
作者：
Takahiro TSUBOTA;Toshio YOSHII;Jian XING
本研究开发了一种卷积神经网络模型来预测城际高速公路发生事故的可能性。该模型利用过去1小时内交通状态的时空信息作为输入，预测从预测开始时间起提前2小时的事故发生。为了在事故发生之前有效地学习交通特征，输入数据被布置成三维张量形式，类似于图像数据。基于ROC曲线的结果表明，该模型能够识别事故发生具有良好的准确性。此外，除了二进制分类的能力，结果表明，在输出层计算的可能性可以解释为事故发生的概率。
This study develops a Convolutional Neural Network model to predict the likelihood of accident occurrence in an inter-city expressway. The model utilizes the temporal and spatial information of traffic states of past one hour as an input for predicting the accident occurrence in two hours ahead from the prediction start time. In order to efficiently learn the traffic features prior to the accident occurrence, the input data is arranged in three-dimensional tensor form, analogous to image data. The results based on the ROC curve showed that the proposed model was able to identify the accident occurrence with good accuracy. Further, in addition to the capability of binary classification, the result demonstrated that the likelihood calculated in the output layer could be interpreted as the probability of accident occurrence.