Given the increasing painful repercussions of air pollution caused by massive vehicular emissions,the integration of traffic models with emissions estimation models has been investigated in depth to evaluate the performance of the pre-determined environmentally-friendly transportation policies.Nevertheless,there is a lack of the optimization approach to design the traffic control strategy combining the coupled models.Therefore,an optimization framework is developed to optimize the traffic signal timing strategies to mitigate the vehicular emissions in the considered network.The proposed framework integrates a macroscopic analytical queueing network model with a macroscopic emissions estimation model to account for the vehicle emissions metrics,and then a genetic algorithm(GA)is employed.The optimization solution is generated by iterative computations of the GA-based traffic signal optimizer within a well-established max generation.A congested urban road network in Xian city that consists of two signalized intersections is selected for a case study.The evalution results from the microscopic simulation software AIMSUN indicate that the proposed optimal traffic signal control scheme improves expected vehicle trip travel time and vehicle emissions of four sorts of pollutants in the area under consideration.This optimization framework helps traffic operators to design reasonable environmental-friendly traffic signal control strategy that can be used in congested urban area.