## 3 Results
### Rq01  
The prediction models use the following data sources: vehicle data, driver's data, weather conditions, light conditions, traffic accidents, traffic flow, traffic events, road infrastructure, taxi trips, points of interest, and population. The most common data sources are weather conditions, traffic accidents, traffic flow, and road infrastructure. Meanwhile, driver's data, light conditions, and taxi trips are the least common. Based on Table 2, the attributes contained in each data source are presented as follows.  
* Vehicle data: identifier, time, location, type, speed, condition, seat belt, pick up and pick off time;
* Driver's data: age, gender, education level, collision factors (sleepiness and boredom), and involvement of alcohol and drugs;
* Weather conditions: sun, cloud, rain, snow, fog, sleet, crosswind, sand, dawn, dusk, visibility, temperature, precipitation, snowfall, pressure, wind speed, humidity, hail, storm, wind direction, and dew point;
* Light conditions: headlights, streetlights, sunlight, and night light;
* Traffic accidents: vehicles involved, collision type, collision description, the direction of the road, number of killed or injured people, severity, human situation, number of property damage only collisions, number of collisions with casualties and dead, presence of traffic objects, road segment, event type, security level, collision month, vehicle failure, police report, and origin of the collision;
* Traffic flow: vehicle speed according to radar, number of vehicles, occupancy, average speed, annual average daily traffic, driving direction, and lane identifier;  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multicolumn{6}{c}{**Evaluation Metrics**} & \multicolumn{6}{c}{**Percentage of Data**} \\ \cline{2-11}  & **MAE** & **MRE** & **RMSE** & **MSE** & **PAR** & **FPR** & **TPR** & **F1** & **AUC** & **Train.** & **Valid.** & **Test.** & **Compared with** \\  & & & **\%** & **\%** & **\%** & **\%** & **\%** & **\%** & **\%** & **\%** & **\%** \\ \hline PS29 & 0.40 & 0.16 & 88.89 & & & & 87.0 & 13.0 & \begin{tabular}{l} Auto-Regressive Integrated Moving Average, and Convolutional Long Short-Term Memory Neural Network \\ Network \\ \end{tabular} \\ \hline PS30 & & & & 81.3 & & & 80.0 & 20.0 & \begin{tabular}{l} Support Vector Machine with linear \\ \end{tabular} \\ \hline PS31 & 0.0082 & 0.0131 & 0.0001 & & & 67.0 & 11.0 & 22.0 & \begin{tabular}{l} Linear Regression, Long Short-Term Memory Neural Network, Denoising Auto-Encoder, XGBoost, and Seq2 Seq \\ \end{tabular} \\ \hline PS32 & 0.444 & 0.723 & 0.773 & 0.736 & 70.0 & 10.0 & 20.0 & \begin{tabular}{l} Logistic Regression, Least Absolute Shrinkage and Selection Operator, Support Vector Machine, and Decision Tree \\ \end{tabular} \\ \hline PS33 & & 94.0 & & & 70.0 & 20.0 & 10.0 & \
\begin{tabular}{l} Multilayer Perceptron and Support \\ Vector Machine with poly kernel \\ \end{tabular} \\ \hline PS34 & & 87.54 & 0.814 & 0.837 & 80.0 & 20.0 &
\begin{tabular}{l} Logistic Regression, Decision \\ Tree, Random Forest, and Naive \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._Traffic events: closures, constructions, broken vehicles, collisions, congestion, and blocked lanes;
* Road infrastructure: geometric characteristics (road length, road shape, road alignment, road type, number of lanes, horizontal curve radius, width of shoulder, slope, tunnel, imperfections, intersections, entrance and exit ramp, and speed limits), and road signs (warning, priority, information, facilities, and service);
* Taxi trips: pick-up and drop-off timestamp, pick-up and drop-off location, trip distance, payment information, taxi zones, and taxi speed;
* Points of interest: place, category, and location;
* Population: *not shown;
* Other: topographic map, digital elevation map, land use, satellite images, the area size of census blocks, special calendar dates, geographical area, trip survey, and bike trip.