This thesis presents a prediction model for e-bikes and e-scooters, aimed at enhancing traffic safety and efficiency by sharing their intentions of future possible positions among road users. The research addresses the current automated vehicle technologies which lack communication between road users. The prediction model is based on and tested with a mobility model, adapted for modelling e-bikes and e-scooters in a simulator program primarily used for pedestrians. This implementation has produced the ability to predict future positions and further the development of intention-sharing capabilities in urban traffic scenarios. The model is built upon physical parameters and mathematical models for a controlled and regulated model. Polynomial regression was applied to predict positions based on historical data and the results were evaluated with RMSE metrics, demonstrating the prediction accuracy in different scenarios. The thesis also includes the integration of the prediction model into a hardware setup, a Raspberry Pi. Demonstrating the practical application and retaining the effectiveness of the model in a real-time environment. Gathered from the results, the model can reserve a predicted area every second but also has the capability to work during faster or slower time intervals, depending on the hardware used to enable the model in the protocol. With this, the research highlights the possibility of implementing this in CCAM systems. The results show promising accuracy with a simple controlled model using as little necessary data as possible. The project work contributes to the field of intelligent transport systems by providing a scalable solution to enhance the interaction between VRUs and vehicles, creating a step closer to achieving the Vision-Zero goal of having zero traffic-related accidents or fatalities.