# An Emergency Message Routing Protocol for Improved Congestion Management in Hybrid RF/VLC VANETs
## 4 Algorithm Description
### Congestion Management Algorithm
Specifically, it aims to quantify how much each considered strategy can reduce the emissions caused by the vehicle's idling and stop-and-go traffic conditions associated with congestion. The emissions score is calculated as:  
\[\text{Score}_{E}=\alpha\int(E_{\text{idling}}-E_{\text{freeflow}})dt \tag{13}\]  
where \(E_{idling}\) and \(E_{freeflow}\) are the emissions rates from the idling and free-flow traffic, and \(\alpha\) is a weighting coefficient for the emissions scoring, which allows assigning relative importance to the emission impact compared to other scores. It should be noted that the above-described travel time scoring and emissions scoring equations are meant to assume a gradual return to the free-flow conditions as the clearance proceeds. **User satisfaction/stress scoring:** This step is to evaluate how each potential congestion mitigation strategy may impact the subjective experiences and perceptions of the road's user. Specifically, it aims to score the strategies based on their estimated effects on three key factors that influence the user's satisfaction or stress, namely:  
* _Safety:_ This is estimated using metrics such as the speed differentials which correlate to the crash risk perceptions. * _Delay:_ This is measured as the differences from the free-flow travel times since longer delays do lower satisfaction. * _Predictability:_ This is captured by the variability or uncertainty in the travel times, as higher predictability increases satisfaction. The user's satisfaction score is calculated as:  
\[\text{Score}_{\text{U}}=\beta(U_{\text{safety}}+U_{\text{delay}}+U_{\text{ Predictability}}) \tag{14}\]  
where \(\beta\) is a weighting coefficient, and \(U_{\text{safety}}\), \(U_{\text{delay}}\), and \(U_{\text{predictability}}\) are parameters that influence the subjective user's perceptions. These parameters can be estimated based on objective traffic characteristics [28]. In our approach, (1) \(U_{\text{safety}}\) is estimated using characteristics like speed differentials between the vehicles. Higher speed differentials correspond to higher relative speeds, which correlate to drivers' perceptions of increased crash risk and lower feelings of safety; (2) \(U_{\text{delay}}\) is estimated by calculating the delays experienced by the vehicles compared to the free-flow travel time on that road. A higher \(U_{\text{delay}}\) means lower user satisfaction. Higher such delays correspond to longer travel times and lower user satisfaction; and (3) \(U_{\text{predictability}}\) is estimated by measuring the variability and uncertainty in the travel times using the standard deviation of these times. A higher variability or uncertainty in the travel times between the trips on the same route corresponds to a lower predictability or satisfaction for the users who value being able to reliably estimate their travel times. The above-discussed strategies aim to minimize the total travel time and emissions while increasing the user's satisfaction. All of these will be impacted positively as the traffic flows improve. Vehicles can receive messages via V2V and V2I communications using both RF and VLC links. RF is used for initial message broadcasting from the source and subsequent multi-hop relaying between vehicles.