## 3 Proposed Smart Route: IoV-Based Congestion Detection and Avoidance (CDA) Using Rerouting
### IoV-Based Congestion Avoidance (IoV-Based CA)
Figure 7: Effect of various congestion levels on IoV-based CDA and MCDP path cost (previous MCDP scheme [20]). ## 5 Conclusions

In an urban city, road intersection points or crossroads are highly vulnerable regions for massive traffic congestion. Traffic jam is a serious issue presently to be addressed. This paper discusses an enormous traffic jam detection and avoidance on a particular AOI (i.e., Alawaha, Riyadh) using an IoV-based CDA scheme. The proposed scheme uses two essential parameters for congestion detection (i.e., occupancy rate and occupancy time) and modified EG-Dijkstra for alternate best route planning. From the temporal variations in the results, we determine that the RSU at each entrance point assesses the traffic periodically and assign the routes to the drivers dynamically. The time-lapse of alternate route discovery depends on the arrival of the vehicle to the route segment. Moreover, the rerouting algorithm in the IoV-based CDA scheme assesses the current occupancy rate of the alternative route before selection to prevent congestion migration. Our results also determine that the proposed IoV-based CDA outperforms the previous MCDP [20] scheme in terms of low-cost, less travel time, and high-speed route selection. Lastly, we also concluded that our proposed scheme performs very well under various congestion levels, i.e., low, medium, high, and massive, as compared to the previous MCDP scheme. The conceptualization, methodology, and original draft writing was done by Z.K. The supervision, validation, writing-review, and editing were done by the A.K. Formal analysis and proofread were performed by H.F. All authors have read and agreed to the published version of the manuscript. The APC of this paper was funded by Prince Sultan University, Riyadh, Kingdom of Saudi Arabia. This work was supported by the Robotics and Internet-of-Things Lab of Prince Sultan University, Riyadh, Kingdom of Saudi Arabia. This work was also supported by the RIC seed project (number 6). The authors declare no conflict of interest. ## References

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