Traffic signal timing optimization for isolated urban intersections considering environmental problems and non-motorized vehicles by using constrained optimization solutions

Abstract

In optimizing traffic signal timing schemes, various researchers provided their own suggested model to optimize timing plans at isolated urban intersections. Those model formulations were reputed and qualified for their case studies and could apply in different particular circumstances; however, most of the mentioned studies underestimated improving the current traffic control system by advancing traffic efficiency, enhancing traffic safety, and protecting the urban environment simultaneously. Moreover, several stochastic solutions demonstrated taking advantage of searching global optimization results and overcoming the deterministic methods in multi-objective traffic optimization in various pieces of research. In addition, the environmental factor in optimizing traffic lights is also eliminated because current studies mainly focus on traffic efficiency and traffic safety. To limit the shortcomings of the above studies, this study aims to provide an adequate and flexible model formulation to handle the optimal traffic signal timing issue at an isolated signalized intersection in urban areas considering the vehicle exhaust emissions and the other effective objective functions simultaneously by applying the enhanced stochastic solutions. The provided model formulations followed some steps. First, a fitness function based on the simultaneous optimization condition of traffic efficiency functions, traffic safety functions, and protected environment functions was established. Then, the constrained function according to the reliable theoretical basis was generated to improve the search capacity of the suggested model formulation. Next to several enhanced stochastic methods are used to analyze the fitness function constrained genetic algorithm (GA) and constrained particle swarm optimization (PSO). Henceforward, some effective comparisons were made among stochastic solutions, between existing timing plans and suggested timing plans, and between a traditional well-known method and the suggested model. Finally, several robust tools in traffic simulation models were utilized to validate the usefulness of hypothesis model formulation. The empirical outcomes demonstrated that the providing hypothesis model formulation applying stochastic optimization methods in traffic signal optimization was suitable to cope with the traffic signal timing optimization schemes. Besides, the provided comprehensive model could support traffic engineers decrease time calculation by applying suitable operators of GA and PSO.