# An Emergency Message Routing Protocol for Improved Congestion Management in Hybrid RF/VLC VANETs
## 5 Simulation and Performance Analysis
Over time, without any active congestion management strategies, the traffic conditions continued to worsen. Next, in Figure 4c, we see a more extensive spreading of the node clusters representing vehicles, indicating congestion has now impacted a much larger stretch of the highway. We also see a highly irregular pattern with multiple dense pockets forming. This underscores the non-uniform and dynamic nature of how congestion can propagate. The peak node concentration, marked with a black circle, is now situated at a different point on the road than the red circle highlighting the original accident location. This suggests congestion tendencies can shift spatially over time without interventions. Areas around the peak node cluster see heavily impeded flows with vehicles barely inching forward, represented by the tightly packed nodes. Upstream flows have also worsened leading to this bottleneck region. Figure 4d now depicts the impact of applying our active congestion management scheme to the troubled highway network. The node clusters representing vehicles appear significantly more sparse and uniformly spaced throughout the modeled stretch. This indicates that the congestion levels have been dramatically reduced after implementing the proposed management plan. Figure 5 shows a comparison of the probability density distributions for clearance times in two different traffic disruption scenarios--an accident versus congestion. The y-axis represents the probability density, which indicates the likelihood of a given clearance time range. For both scenarios, the x-axis charts the time in minutes since the incident occurred. The probability densities were estimated based on historical traffic data recording clearance times for accidents vs. congestion incidents. The data contains a sample of recorded clearance time durations for each scenario. A probability density function could then be fit to each data sample, defining the likelihood of various clearance time ranges. Figure 4: Evolution of traffic conditions following an accident and the impact of congestion management strategies. (**a**) A collision is reported at the red circle while vehicles are smoothly moving; (**b**) Initial congestion status due to the accident. (**c**) Congestion after some time without congestion management. (**d**) Congestion alleviation with effective congestion management. In the case of an accident, the peak density is higher at 3.75 within the first few minutes following the incident. This indicates there is a greater chance of the accident being cleared relatively quickly during this initial period. However, for congestion, the peak density is lower at around 2.2. This suggests incidents of congestion typically take longer to initially clear compared to accidents. As time progresses, both probability density curves gradually decrease. But the accident curve shows a steeper fall-off, meaning the likelihood of extended clearance duration is lower for accidents versus congestion cases. Figure 5 reveals that while accidents tend to have a higher initial clearance rate, congestion is generally more persistent over longer durations from when it first arises on the road network. This gives important insights into how different incident types impact clearance time dynamics. Table 1 shows the effect of various factors on clearance time based on results obtained from our algorithm. The times reported represent the average/typical cases. It provides estimated percentages for the relative impact of each factor based on traffic modeling. The number of vehicles involved has the largest effect for both, reflecting how more cars result in greater disruption and longer clearance efforts. However, its impact is larger for congestion. More blocked lanes extend clearance as fewer lanes are available for traffic diversion. Lane type and location have moderate impacts, as certain lanes (e.g., fast vs. slow lanes) affect response abilities more than others. Accidents see a greater location effect due to varied emergency access.