A Fuel Saving Green Light Speed Advisor for Signalized Intersections using V2I Communication
This study introduces a green light speed advisory strategy in a connected vehicle environment, which helps vehicles avoid unnecessary stopping before intersections and improves fuel consumption efficiency. A number of existing studies have worked on Green Light Optimal Speed Advisory (GLOSA) but most of them focus on the macro level, ignoring the process of speed changes. Rakha et al. considered the acceleration process in his research, but there is no simulation and field data to support his results, and fuel consumption was considered in terms of total consumption in the time period, which is not reasonable. When vehicles approach intersections at different speeds, the fuel consumption will be different. This paper formulates explicit fuel consumption objective functions based on the VT Micro model to measure fuel consumption for different speed profiles. A strategy is developed to reduce fuel consumption. Over 100,000 runs of the experiment have been conducted in MATLAB and the results show that the strategy can save up to 56% of fuel if the vehicle follows the system's speed advice. The average fuel savings for green light signals is 56% and for red signal scenarios it is 21% An android app has also been developed to collect field data in the connected vehicle environment. The results from the field show that vehicles with GLOSA reduce fuel consumption by 13% compared to vehicles without GLOSA.