In my graduate research, I conducted a comprehensive simulation study that evaluated the performance of intersection traffic control methods. This study compared and contrasted traditional pre-timed intersections, actuated intersections, and my novel geolocation-based phasing algorithm. The results revealed that the geolocation-based approach significantly outperformed the others, reducing car stop times by 59.91% in "randomized demand" scenarios and 55.61% in "full-demand" situations. These findings underscore the potential of geolocation-enabled algorithms to revolutionize intersection management and warrant further exploration for practical implementation in real-world traffic systems.
Below is an example of just 1 of 360 test cases demonstrating how my geolocation-enabled phasing algorithm processes vehicles through an intersection. The simulation software was purpose built and written by me to collect data for the study. The software supports placing vehicles with gaps in between each other(vehicle density), as well as increasing or lowering demand(total vehicles placed).
Presented here are the key findings of the study:
In tabular format:
In the paper that is associated with this project, I dive deeper into the meaning behind the numbers and why the algorithms in the given intersection performed the way they did across differing densities and demand levels.