## 5 Experiments
### Knowledge Generation Process and Distribution  
Our proposed solution periodically shares the local information, between one-hop neighbours, through beacon packets to create the local knowledge base. In order to do that, two more pieces of information were added in the beacon package: the current EBM score and the aggregated information. The local knowledge base is built by aggregating the local information received from the neighbourhood, as well as the calculation of the weight of the roads. Once the local knowledge base is created, the next step is to share it with the most relevant neighbour vehicle, this is performed by following the selection criterion presented in Section 6.1. The following representation shows an example of the fusion of two aggregated values: \(A_{r}:=\partial(A_{1},A_{2})\), where \(\partial\) is the aggregation function that has two input values (\(A_{1}\) and \(A_{2}\)). These values are combined, resulting in a new aggregated value (\(A_{r}\)). As the main goal of the proposed study is the generation and distribution of knowledge about the traffic condition, the aggregation function is given as follows:  
\[v_{agg_{i}}^{avg}=\frac{v_{1}n_{1}+v_{2}n_{2}}{n_{1}+n_{2}} \tag{8}\]  
where \(v_{agg_{i}}^{avg}\) represents the aggregate average speed of a given road \(i\). The parameters \(v_{1}\) and \(v_{2}\) are the two input values from \(i\). \(n_{i}\) indicates the amount of information that contributed to the generation of the new aggregated value. Thereby, the weight of the road \(i\) (\(w_{i}\)) is calculated as follows:  
\[w_{i}=\frac{v_{agg_{i}}^{avg}}{v_{sp_{i}}^{max}}, \begin{cases}\quad w_{i}: \text{weight of road }i\\ \quad v_{agg_{i}}^{avg}:\text{aggregate average speed of road }i\\ \quad v_{spe_{i}}^{max}:\text{maximum speed of road }i\end{cases}  \tag{9}\]  
After aggregating all the local information, the vehicle with the highest EBM score classifies the weight of the roads according to Table 5. The levels of service and traffic classification were based on the Highway Capacity Manual (HCM) [41]. After the classification step, if an event is identified (in our case, roads with the level of service D, E or F), a message (also known as knowledge), containing the identification of the roads in question is generated. Thereby, the knowledge distribution process in the service channel is started. Figure 16 shows the operation flowchart of the proposed solution. The flowchart is divided into two phases. The first one is the information aggregation and knowledge generation, and the second is the data dissemination. In the first phase, every time the vehicle receives the local information, it either \begin{table}
\begin{tabular}{c c c} \hline \hline
**Level of Service** & **Traffic Classification** & \(\mathbf{w_{i}}\) \\ \hline
**A** & Free flow & (1.0\(\sim\)0.9) \\
**B** & Reasonably free flow & (0.9\(\sim\)0.7) \\
**C** & Stable flow & (0.7\(\sim\)0.5) \\
**D** & Approaching unstable flow & (0.5\(\sim\)0.4) \\
**E** & Unstable flow & (0.4\(\sim\)0.33) \\
**F** & Forced or breakdown flow & (0.33\(\sim\)0.0) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Level of service and traffic classification [41]. inserts or aggregates the local information into the local knowledge base (Block 1). In the next step, it calculates the weight of roads according to Equation (9) (Legend (A)). After this step, the vehicle with the highest EBM score (Legend (B)) classifies the weight of roads according to Table 5 (Legend (C)). During this process, if the selected vehicle detects some congested traffic flow, the knowledge is generated and distributed in the network (Legend (D)). On the other hand, if the vehicle does not have the highest EBM score, it selects the next most relevant vehicle and sends the aggregated local information to it (Legend (E)). The second phase (data dissemination), is responsible for informing vehicles that are inside an area of interest (AoI; Legend (F)) according to the application requirements.