## 3 Methodology  
### Generating Dataset Using Simulation Scenarios  
In this paper, the following five variables were considered: the number of vehicles (\(v\)), data rate (\(dr\)), DSRC transmission power (\(tp_{DSRC}\)), LTE transmission power (\(tp_{LTE}\)), and LTE bandwidth (\(b\)). Based on the studied literature related to ETSI and the Wireless Access in Vehicular Environments (WAVE) standardization, these parameters have the most effect on network congestion problem [11, 28, 29, 30, 31, 32, 33]. Because no data sets from real HetVNET are available today, we have generated a data set that contains data records of these parameters [8]. We used OpenStreetMap (OSM) [34] to have a map that is similar to the real environment in terms of intersections, streets, buildings, etc. Figure 1 shows the boroughs of downtown Montreal that we used to generate the simulation scenario. Besides, to generate the road traffic and vehicle movements on the map, we used Simulation of Urban Mobility (SUMO) 0.26.0. [35]. SUMO requires \".osm\" file created by OSM to generate the vehicle traffic on the map. Simultaneously, Veins LTE version 1.3 [36] simulator was used to generate heterogeneous vehicular network using IEEE 802.11p for V2V communications and LTE for Vehicle-to-Infrastructure (V2I) communications. SUMO and Veins LTE worked on Ubuntu 16.04. The simulation time was 1000 s and, in order to generate a high load of data, we defined a road accident with a 70 s time of running simulation scenario. Because we considered the downtown of Montreal city, the vehicles maintained the maximum speed limit of 40 km/h assigned to these boroughs. Table 1 shows the parameters and their corresponding values used for simulating HetVNET scenarios. In each run, the values of the five attributes were changed based on the information presented in Table 1.  
Information was extracted after running simulation scenarios of HetVNET. The amount of network throughput over generated data rate can give us a vision of the network performance, as explained in [8]. Indeed, based on this value, we can find out how many of the  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **IEEE 802.11 p** & **LTE** \\ \hline Number of Base Station & & 1 \\ Number of Resource Blocks & & 25, 50, 100 \\ Bandwidth & 10 MHz & 5 MHz, 10 MHz, 20 MHz \\ Transmission power & 30 dBm (Maximally) & 43 dBm, 46 dBm \\ Transmission data rate & 6–27 Mbps & \\ Modulation techniques & QPSK, 16-QAM, 64-QAM & \\ Simulation time & & 1000 s \\ Simulation runs & & 500 \\ Number of vehicles & & 50, 100, 150, 200 \\ Simulation area & & 1000 m \(\times\) 1000 m \\ Number of lanes & & 4 (two in each direction) \\ Maximum speed & & 40 km/h \\ Propagation model & & Nakagami \\ Size of message & & 400 bytes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters and corresponding values.  
Figure 1: Simulated area is shown by a red square.  
generated data were successfully received at destination points per unit of time. Therefore, for each run of simulation scenario, the amount of network throughput over load per a unit of time is extracted. This value can be equal to one maximally in the desired case, and equal to zero minimally in the worst case of network performance. Network throughput is the calculated sum of the value of throughput in DSRC and the value of throughput in LTE. To meet the paper objectives, we used information of successful experiences (simulation runs). Simulation scenarios with a network throughput value over the generated data rate of more than 0.6 are selected as a successful scenario [37]. In fact, we need to extract the best scenarios in HetVNET and augment these successful scenarios. Subsequently, we put the information of these scenarios in the dataset. As shown in Figure 2, the data records in the dataset are classified based on the number of vehicles. Based on the five vehicle densities considered, there are five different classes of vehicles in this paper: 30, 50, 100, 150, and 200 vehicles.