… This study uses the provincial road traffic crashes data in Iran for 93 months. “The benefit of
using provincial units lies in the interpretation of the model results and possible evaluation of …

… Also, looking into the prediction accuracy only is misleading. … f1-loss function to predict the
severity of traffic crashes. Underlying … include a sample of traffic crashes that occurred at work …

… In summary, traffic crash prediction in the short term is a spatiotemporal data prediction issue.
As it involves spatial and temporal information, the traditional LSTM model, which captures …

… to improve traffic crash prediction. Considering the probability of traffic crash occurrence vary
due … information for the short-term crash prediction, named as Deep Spatiotemporal Hybrid …

… for traffic crashes in traffic crash risk prediction and evaluation, especially when crash data
are … used in the active prevention and control of traffic crash risk, thus reducing traffic crashes. …

… -time crash prediction models that will potentially be employed within traffic management …
Methods: In this study, two highly optimized data-driven models for crash occurrence prediction …

… model proposed in this research is a dichotomous prediction of crash and non-crash traffic
flow, so only two kinds of prediction results with or without risk can be obtained. In order to …

… GAN for over sampling traffic data of crash events to balance the crash prediction data. …
traffic crash prediction. To specify, the study contributes the literature for real-time crash prediction …

… crash occurrence time (TIM) have significant effects on the traffic crash prediction model; the
prediction … of this research is traffic crash severity prediction; since the occurrence of traffic …

… State Departments of Transportation (DOTs) are encouraged to collect local data to calibrate
the HSM models and develop jurisdictional-specific crash prediction models. Although …