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- W2935760944 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)." @default.
- W2935760944 created "2019-04-25" @default.
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- W2935760944 date "2019-04-01" @default.
- W2935760944 modified "2023-10-17" @default.
- W2935760944 title "Real-Time Crash Risk Prediction using Long Short-Term Memory Recurrent Neural Network" @default.
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- W2935760944 doi "https://doi.org/10.1177/0361198119840611" @default.
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