Citation

Yuan J, Abdel-Aty M, Gong Y, Cai Q. Transp. Res. Rec. 2019; 2673(4): 314-326.

Copyright

(Copyright © 2019, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/0361198119840611

PMID

unavailable

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).

Language: en