@article{ref1,
title="Prediction of traffic accident likelihood on intercity expressway by convolutional neural network",
journal="Intelligence, Informatics and Infrastructure",
year="2020",
author="Tsubota, Takahiro and Yoshii, Toshio and Xing, Jian",
volume="1",
number="1",
pages="11-17",
abstract="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.<p /> <p>Language: en</p>",
language="en",
issn="2435-9262",
doi="10.11532/jsceiii.1.1_11",
url="http://dx.doi.org/10.11532/jsceiii.1.1_11"
}