@article{ref1,
title="Real-time crash risk prediction using long short-term memory recurrent neural network",
journal="Transportation research record",
year="2019",
author="Yuan, Jinghui and Abdel-Aty, Mohamed and Gong, Yaobang and Cai, Qing",
volume="2673",
number="4",
pages="314-326",
abstract="With the help of traffic detectors widely deployed along arterial roads and intersections, real-time traffic data are collected and updated in a very short time period, which makes it possible to conduct real-time analysis at signalized intersections. Among them, real-time crash risk prediction is one of the most promising and challenging research topics. This study attempts to predict real-time crash risk by considering time series dependency with the employment of a long short-term memory recurrent neural network (LSTM-RNN) algorithm. Also, the synthetic minority over-sampling technique (SMOTE) was utilized in this study to generate a balanced training dataset for algorithm training. In comparison, a conditional logistic model was developed based on matched case control design. Both models were evaluated based on the real-world unbalanced test dataset rather than an artificially balanced dataset. The comparison results indicate that the LSTM-RNN with SMOTE outperforms the conditional logistic model. The methods and findings of this study attempt to verify the feasibility of real-time crash risk prediction by using LSTM-RNN with over-sampled dataset (SMOTE).<p /> <p>Language: en</p>",
language="en",
issn="0361-1981",
doi="10.1177/0361198119840611",
url="http://dx.doi.org/10.1177/0361198119840611"
}