  Traffic control optimization is a challenging task for various traffic
centres in the world and majority of approaches focus only on applying adaptive
methods under normal (recurrent) traffic conditions. But optimizing the control
plans when severe incidents occur still remains a hard topic to address,
especially if a high number of lanes or entire intersections are affected. This
paper aims at tackling this problem and presents a novel methodology for
optimizing the traffic signal timings in signalized urban intersections, under
non-recurrent traffic incidents. The approach relies on deploying genetic
algorithms (GA) by considering the phase durations as decision variables and
the objective function to minimize as the total travel time in the network.
Firstly, we develop the GA algorithm on a signalized testbed network under
recurrent traffic conditions, with the purpose of fine-tuning the algorithm for
crossover, mutation, fitness calculation, and obtain the optimal phase
durations. Secondly, we apply the optimal signal timings previously found under
severe incidents affecting the traffic flow in the network but without any
further optimization. Lastly, we further apply the GA optimization under
incident conditions and show that our approach improved the total travel time
by almost 40.76%.
