Trafffiic signal optimization using multiobjective linear programming for oversaturated trafffiic conditions

In this study, we present a framework designed to optimize signals at intersections experiencing oversaturated traffic conditions, utilizing mixed -integer linear programming (MILP) techniques. The proposed MILP solutions were developed with different objective functions, namely a reduction in the total remaining queue and fair distribution of the remaining queue after each signal cycle. Our framework contains two distinct stages. The initial stage applies two distinct MILP methodologies, while the subsequent stage employs a neighborhood search method to further reduce the delays associated with the green signal timings derived from the first stage. Ultimately, to evaluate their effectiveness across various intersections, we employed the HCM 2000 delay model for all the models we developed. Our experimental results show that the proposed approach reduces the delay significantly for various intersection designs.