# Enhancing Route Guidance with Integrated V2X Communication and Transportation Systems: A Review
## 5 Related Work  
A study presents a smart transportation strategy designed to manage emergency autonomous vehicles and normal autonomous vehicles within a V2X communication framework [106]. Utilizing an autonomous intersection management system, the strategy prioritizes EAVs over normal AVs by employing a priority queue principle and FIFO rules to ensure smooth traffic flow and minimize collisions. Simulations were conducted using the SUMO platform on real-world routes from Pakistan. Results revealed that EAVscompleted routes in 8.50 s and normal AVs in 10.55 s on the M2 motorway, highlighting significant reductions in travel time delays. The system alleviates the burden on central control units by dynamically planning routes in real time, ensuring high-priority EVs reach their destinations swiftly while maintaining minimal disruptions for other vehicles. Future work aims to develop enhanced junction management systems for buses and emerging vehicle types, such as flying cars, to optimize traffic flow and vehicle trajectories further [106]. A new study addressed the challenges of congestion and energy efficiency in vehicular communication systems by proposing a Narrow Band of Vehicular Things Communication System (NBVTCS) based on NB-IoT technology [107]. The model leverages NB-IoT to achieve low-cost, high-range, and energy-efficient communication for vehicles, optimizing performance metrics such as energy efficiency, bit error rate, and system throughput. A Hybrid Pelican Beetle Swarm Optimization (HPBSO) approach is introduced to enhance these metrics, outperforming other optimization techniques like DOX, EFO, POA, and BSO. Simulation results demonstrated the proposed system's superior performance, achieving minimal energy consumption, a low error rate of 0.000142, and a high throughput of 1,388,930 bps. Compared to existing methods like DSRC and LoRa, the NBVTC model offers significant improvements in communication accuracy and efficiency. This supports intelligent mobility in urban and rural areas and opens the way for future autonomous vehicle integration into smart cities [107]. In addition, a study addressed key challenges in Vehicular Ad Hoc Networks (VANETs), such as data dissemination interruptions caused by high node density and lack of available next-hop nodes [108]. To overcome these issues, a Drone-Assisted Cooperative Routing (DACR) strategy is proposed, integrating IoT elements and Fixed Ad Hoc Networks (FANET) to enhance VANET performance. The DACR system aims to improve routing efficiency, packet delivery, and data exchange in dynamic, high-density environments. Performance evaluation using the ns-2.31 simulator demonstrates DACR's superiority over state-of-the-art protocols such as U2 RV and CRRA. Results show that DACR reduces control message overhead by 22% and 52%, improves end-to-end delay by 23% and 33%, and increases packet delivery ratio (PDR) by 14.5% and 44%, compared to U2 RV and CRRA, respectively. These findings highlight DACR's ability to significantly enhance data transmission reliability and efficiency in VANETs, making it a valuable contribution to the advancement of Intelligent Transportation Systems (ITS) [108]. In addition, an integrated inter-vehicle wireless communications and positioning system is presented to address the needs of cooperative intelligent transportation system (C-ITS) safety applications [109]. The study supports alternative positioning approaches where the communication stack uses DSRC that have been developed explicitly for wireless access in vehicular environments (WAVE), in addition to Global Navigation Satellite Systems (GNSS) for promoting rail and road safety. However, the number of GNSS satellites in view and the accessibility of the Internet are essential factors in how effectively the suggested integration technique performs. The study focused on positioning and the navigation system and has not considered route guidance or optimal route assignment. [PERSON], [PERSON] et al.