Intelligent Speed Advisory System for Optimal Energy Efficiency Based on Ambient Temperature Leveraging Communication Technology and Fuzzy Logic

Achieving sustainable transportation poses a variety of challenges and difficulties, such as economic, social, and environmental dimensions. These challenges slow down the transition towards a transportation system that minimizes environmental impact and fuel consumption. The focus has shifted towards increasing efficiency and promoting greener alternatives by integrating innovative solutions like intelligent transportation systems, communication technologies, and other approaches to address these challenges and pursue sustainable and energy-efficient transportation. This study proposes a novel approach that integrates Python, SUMO (Simulation of Urban MObility), Vehicle-to-Infrastructure (V2I) communication, and Fuzzy Logic (FL) to estimate the optimal speed for vehicles based on vehicle velocity, road speed limits, and ambient temperature. In the simulation scenarios, we considered different temperature changes to evaluate the effectiveness of the proposed approach. By using SUMO to retrieve the road speed limit through V2I communication and incorporating it into fuzzy logic, we could estimate the optimal speed for vehicles in real time. The simulation results showed a significant reduction in energy consumption and pollutant emissions compared to the unequipped vehicles with V2I and Fuzzy Logic Systems. Specifically, the study found that adopting this approach led to an average reduction in fuel consumption and CO2 emissions of around 9%. These findings highlight the potential of integrating V2I communication and Fuzzy Logic to achieve more sustainable and efficient transportation energy use.