  Traffic control optimization is a challenging task for various traffic
centers around the world and the majority of existing approaches focus only on
developing adaptive methods under normal (recurrent) traffic conditions.
Optimizing the control plans when severe incidents occur still remains an open
problem, especially when 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. With the purpose of producing fast and
reliable decisions, we combine the fast running Machine Learning (ML)
algorithms and the reliable Genetic Algorithms (GA) into a single optimization
framework. As a benchmark, we first start with deploying a typical GA algorithm
by considering the phase duration as the decision variable and the objective
function to minimize the total travel time in the network. We fine tune the GA
for crossover, mutation, fitness calculation and obtain the optimal parameters.
Secondly, we train various machine learning regression models to predict the
total travel time of the studied traffic network, and select the best
performing regressor which we further hyper-tune to find the optimal training
parameters. Lastly, we propose a new algorithm BGA-ML combining the GA
algorithm and the extreme-gradient decision-tree, which is the best performing
regressor, together in a single optimization framework. Comparison and results
show that the new BGA-ML is much faster than the original GA algorithm and can
be successfully applied under non-recurrent incident conditions.
