## 3 Results
The throughput variation between city and highway scenarios is a critical metric to assess the stability of each protocol under different mobility conditions. AODV:TOM shows an average throughput variation of 0.004 Mbps between the two scenarios, indicating that the TOM profile maintains throughput stability regardless of speed changes. In contrast, AODV:DEM shows a lower variation of below 0.003 Mbps for  
speeds under 100 km/h, but this variation increases to over 0.004 Mbps when vehicle speeds exceed 100 km/h. This indicates that the DEM profile's performance is more sensitive to higher speeds, especially in highway conditions. Similarly, GPSR:TOM demonstrates superior stability, maintaining a throughput variation of below 0.005 Mbps across all speeds, making it a reliable option for high-speed vehicular networks. In contrast, GPSR:DEM exhibits a higher average variation of 0.02 Mbps, showing that DEM profiles are more prone to environmental variations at different speeds. The linear throughput trends across different speeds highlight that TOM profiles are more efficient in handling varying mobility conditions. For example, at 50 km/h, GPSR:TOM achieves a throughput of 63 Mbps in the city and 65 Mbps on the highway, with a minimal difference of 2 Mbps, whereas GPSR:DEM records 60 Mbps in the city and 62 Mbps on the highway, showing a larger throughput gap. As the speed increases to 120 km/h, GPSR:TOM maintains throughput at 68 Mbps, while GPSR:DEM shows a slight drop to 66 Mbps, reflecting the TOM profile's better adaptability to high-speed mobility scenarios. The results of this study align with findings in the literature. According to [PERSON] et al. (2018), vehicular protocols must efficiently adapt to dynamic mobility conditions to maintain high throughput, especially in high-speed environments. The study by ([PERSON] et al., 2018) reports that TOM-based optimization modules enhance routing decisions and maintain consistent throughput across varying mobility speeds, which aligns with the observed performance of AODV:TOM and GPSR:TOM in this study. Various literature highlighted that DEM profiles improve throughput at moderate speeds but may experience larger variations at higher speeds, consistent with the performance of AODV:DEM and GPSR:DEM in highway scenarios. In summary, TOM profiles of both AODV and GPSR achieve higher throughput and exhibit better stability across different mobility speeds compared to DEM profiles. AODV:TOM shows minimal throughput variation between city and highway, making it a reliable choice for both environments. Meanwhile, GPSR:TOM maintains consistent throughput across all speeds, highlighting its adaptability and robustness in handling varying mobility conditions. These results reveal the importance of incorporating TOM profiles to enhance the performance of vehicular protocols in dynamic, real-world vehicular ad hoc network (VANET) environments. Figure 9: Throughput and variation comparison between AODV, AODV:TOM, and AODV:DEM in city and highway scenarios. Figures 15 and 16 show the observed throughputs of AODV, AODV:TOM, AODV:DEM, GPSR, GPSR:TOM, and GPSR:DEM against different pause times in city and highway scenarios. The observed throughputs of all protocols vary according to pause time except for GPSR:DEM, which shows a linear observation for all pause times. AODV:TOM, GPSR:TOM, and AODV:DEM managed to improve the throughput for all pause times; however, GPSR:DEM obtained results better than GPSR for earlier pause times and less for pause times above 200 s. AODV:TOM, in its variation between city and highway, increases proportionally with pause time, while AODV:DEM showed variation decreases proportional to pause time. Both GPSR:TOM and GPSR:DEM exhibit small variations in throughput, similar to findings in [33] with Fisheye State Routing (FSR) and Dynamic Source Routing (DSR).