Reduction of Red Light Waiting Times Using An Adaptive Traffic Optimization Model (ATOM)

https://doi.org/10.65161/recn4s1Ql2ruipAn0

A scalable and self-configurable model has been proposed to optimize the red light waiting times for any traffic signal in any high-density area. The Adaptive Traffic Optimization Model (ATOM) uses a combination of heuristic and exhaustive algorithms that provide a cost-effective and practical way of optimizing the traffic signal control system. This paper outlines the methodology used to generate years of synthetic data that mimics high-resolution suburban arterial traffic patterns. In addition, the new methodology incorporates more than 10 key variables of traffic patterns. The second part of the paper evaluates the theoretical performance of a proposed volume-responsive signal optimization algorithm using the synthetic data as a benchmark. The detailed simulation results are presented to demonstrate the effectiveness and adaptability of the proposed model. The key takeaway from the result is the reduction of red light wait-time delay by approximately 15% compared to a fixed-time baseline system. The optimized wait-time can easily be translated into cost reduction (fuel savings and vehicle maintenance), lower carbon footprints, and improved travel times.