# Enhancing Route Guidance with Integrated V2X Communication and Transportation Systems: A Review
## 5 Related Work
developed advanced driver assistance systems (ADASs) that focused on avoiding accidents involving motorcyclists and cyclists using V2V communications in order to increase safety [110]. The simulation employed the MotoWarn application, a motorcycle detection grid. Model testing has been carried out at the University Institute of Automobile Research (INSIA) of the Technical University of Madrid (UPM). Nevertheless, as mentioned before, the application focused on the detection of vulnerable road users; it did not provide route guidance instructions or optimal path recommendations to road users such as vehicles and pedestrians. Predictive congestion minimization in combination with an A*-based router (PCMA *) algorithm was introduced in 2017 [111]. It proposed and analyzed congestion prediction and vehicle rerouting for saving time and fuel consumption. The simulation used two different configurations and was compared to a simple reactive rerouting mechanism and the Flow-Balanced KSP (FBkSP) algorithm. The results showed that for the artificial scenario, fuel consumption decreased by up to 47.3%, and travel time dropped by up to 71.8% compared to disabled rerouting. Furthermore, the PCMA algorithm caused a reduction in travel duration by 36.4% compared to the FBkSP algorithm and saved 16.9% fuel. The study focused more on microscopic traffic modeling. In addition, V2X communication quality is crucial to the proposed algorithm. However, wireless communication networks are vulnerable to disturbance, and data transmission may not always be possible as requested in real environments. This has not been articulated in the study; therefore, there is a need for detailed modeling of the communication environment at the link level to investigate the performance of PCMA. An emergency vehicle system based on VANETs that combine V2V and V2I technologies was discussed by researchers [112]. The suggested system provides a communication architecture for automatic traffic management by assisting emergency vehicles, such as ambulances and fire trucks, to choose the least congested route based on sensor data, sharing the necessary information regarding traffic congestion, sending the route status updates to the vehicle display for the driver during any issue, traffic jam, or complication. In 2019, a large-scale feedback-based eco-routing (FB-ECO) navigation system considering vehicular communication impacts was investigated for different market penetration levels [113]. The model was implemented within the integration software with modification of eco-routing behavior to adapt to the communication parameters, packet drop probability and packet delay. The results for realistic modeling showed that an increase in the market penetration level results in an increase in the fuel consumption because of the routing errors that occur due to increased packet drop rates. In addition, for both the ideal and realistic communication cases, FB-ECO works properly for market penetration rates between 20% and 30%. However, changing the setting of the simulation to a dynamic eco-routing environment would significantly affect the performance, as packet drop and delay rates of the VANET network can be significantly affected by dynamic and highly congested networks. In addition, we believe that the model performance would differ with different communication settings, for instance, different ranges, packet generation rates, and packet sizes. A real-time route planning model that considers traffic signal timing and phase information was investigated in order to improve the capacity of urban transport and optimize land-use planning [114]. The study was based on V2X communication. Results have shown that the average travel times calculated by the proposed optimal route are 8.97, 12.54, and 21.85 s, much shorter than traditional navigation routes results. However, the study focused more on the traffic assignment algorithm rather than the communication part.