## Appendix A Quality instrument.
\begin{tabular}{l c c c c c c} \hline \hline
**Title** & **AQ01** & **AQ02** & **AQ03** & **AQ04** & **AQ05** & **Total** \\ \hline A model of traffic accident prediction based on...[21] & 0.5 & 1.0 & 0.5 & 1.0 & 0.0 & **3.0** \\ \hline A New Framework of Vehicle Collision...[22] & 0.5 & 1.0 & 0.5 & 1.0 & 0.0 & **3.0** \\ \hline A novel variable selection method based on...[9] & 1.0 & 1.0 & 0.5 & 1.0 & 1.0 & **4.5** \\ \hline A real-time autonomous highway accident...[23] & 1.0 & 0.5 & 1.0 & 1.0 & 0.0 & **3.5** \\ \hline A real-time explainable traffic collision inference...[24] & 1.0 & 1.0 & 1.0 & 1.0 & 0.0 & **4.0** \\ \hline A rear-end collision prediction scheme based on...[55] & 0.5 & 0.5 & 0.5 & 0.0 & 0.0 & 1.5 \\ \hline A semantic-based classification and regression...[25] & 1.0 & 1.0 & 0.5 & 1.0 & 0.0 & **3.5** \\ \hline A spatiotemporal deep learning approach for...[26] & 1.0 & 1.0 & 1.0 & 1.0 & 0.0 & **4.0** \\ \hline Accident risk prediction based on heterogeneous...[27] & 1.0 & 1.0 & 1.0 & 1.0 & 0.0 & **4.0** \\ \hline Crash prediction based on random effect...[28] & 1.0 & 1.0 & 0.5 & 1.0 & 0.0 & **3.5** \\ \hline Data integration and clustering for real time crash...[5] & 0.0 & 1.0 & 0.5 & 1.0 & 0.0 & **2.5** \\ \hline Deep dynamic fusion network for traffic accident...[29] & 1.0 & 1.0 & 1.0 & 1.0 & 0.0 & **4.0** \\ \hline Evaluating the Performance of Explainable...[30] & 1.0 & 0.5 & 1.0 & 1.0 & 0.0 & **3.5** \\ \hline Hetero-ConvLSTM: A deep learning approach to...[31] & 1.0 & 1.0 & 1.0 & 1.0 & 0.0 & **4.0** \\ \hline Highway crash detection and risk estimation...[32] & 1.0 & 1.0 & 0.5 & 1.0 & 1.0 & **4.5** \\ \hline Highway traffic accident prediction using VDS...[33] & 1.0 & 1.0 & 1.0 & 1.0 & 0.0 & **4.0** \\ \hline Intelligent algorithm in a smart wearable device...[56] & 0.0 & 1.0 & 0.5 & 0.0 & 0.0 & 1.5 \\ \hline Learning deep representation from big and...[34] & 1.0 & 1.0 & 1.0 & 1.0 & 0.0 & **4.0** \\ \hline Operational forecasting of road traffic accidents...[35] & 0.5 & 1.0 & 0.5 & 1.0 & 0.0 & **3.0** \\ \hline Predicting crashes on expressway ramps with...[36] & 1.0 & 1.0 & 0.5 & 1.0 & 0.0 & **3.5** \\ \hline Predicting motor vehicle crashes using Support...[52] & 0.0 & 0.5 & 0.5 & 1.0 & 0.0 & 2.0 \\ \hline Predicting traffic accidents through...[37] & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 & **5.0** \\ \hline Prediction of Crash Severity on Two-Lane, Two...[38] & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 & **5.0** \\ \hline Real-time crash prediction for expressway...[8] & 1.0 & 1.0 & 0.5 & 1.0 & 0.0 & **3.5** \\ \hline Real-time crash prediction in an urban...[6] & 0.5 & 1.0 & 1.0 & 1.0 & 0.0 & **3.5** \\ \hline RiskCast: Social sensing based traffic risk...[39] & 0.5 & 1.0 & 1.0 & 0.0 & 0.0 & **2.5** \\ \hline Real-time estimation of accident likelihood for...[57] & 0.0 & 1.0 & 0.5 & 0.0 & 0.0 & 1.5 \\ \hline Road traffic accidents prediction modelling: An...[58] & 1.0 & 1.0 & 0.0 & 0.0 & 0.0 & 2.0 \\ \hline Road Traffic Injury Prevention Using DBSCAN...[59] & 0.0 & 0.5 & 0.5 & 1.0 & 0.0 & 2.0 \\ \hline SDCAE: Stack Denoising Convolutional...[40] & 1.0 & 1.0 & 1.0 & 1.0 & 0.0 & **4.0** \\ \hline Stack ResNet for Short-term Accident Risk...[41] & 1.0 & 1.0 & 1.0 & 1.0 & 0.0 & **4.0** \\ \hline Support vector machine in crash prediction at...[42] & 1.0 & 1.0 & 0.5 & 0.0 & 0.0 & **2.5** \\ \hline TA-STAN: A Deep Spatial-Temporal...[43] & 1.0 & 1.0 & 1.0 & 0.0 & 0.0 & **3.0** \\ \hline Traffic accident prediction based on deep...[44] & 1.0 & 1.0 & 1.0 & 1.0 & 0.0 & **4.0** \\ \hline Traffic accident prediction model using...[45] & 0.5 & 1.0 & 1.0 & 0.0 & 0.0 & **2.5** \\ \hline Traffic accident prediction using 3-D...[60] & 0.0 & 0.5 & 0.5 & 0.0 & 0.0 & 1.0 \\ \hline Utilizing Machine Learning Models to Predict...[46] & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 & **5.0** \\ \hline \hline \end{tabular}