… that resulted from drugs and alcohol are also deleted, since these kinds of crashes are usually
not attributed to real-time traffic and signal characteristics which are the focus of this study. …

… a traffic accident prediction model based on LSTM-GBRT (… ) and predicts traffic accident
safety level indicators by training traffic … Ihueze and Onwurah [8] analyzed road traffic crashes in …

… of crashes are usually not attributed to real-time traffic and … LSTM in real-time crash risk
prediction. Since this study is the first attempt at predicting real-time crash risk by using LSTM…

… the Convolutional LSTM and then dis… on traffic crash prediction on Type-3 region. Red circle
shows selected areas for comparison. Different color represents different level of traffic crash…

… To verify the effectiveness of the ConvLSTM model in predicting traffic crashes, we chose
traditional FC-LSTM network for comparison. We built an FC-LSTM model that maintained a …

… LSTM has been employed in several existing studies for crash risk prediction and traffic
forecasting. The LSTM model [47] has been developed to forecast short term …

… The focus of this study was on road traffic crash forecasting in … crash data. This specific
combination offers several novel aspects. While LSTM models have been used for traffic crash …

… Traffic crashes can be predicted in advance by traffic … of LSTM on crash detection, this
study proposes an LSTMDTR model for crash detection, an LSTM-based framework with traffic …

… f1-loss function to predict the severity of traffic crashes. Underlying … The data used in the
analysis include a sample of traffic crashes … The result of the LSTM model was compared with the …

… minutes into the future) traffic conditions plays an important … information systems, adaptive
traffic management systems, … traffic prediction using LSTM models, which used field traffic …