LAUSR

Abstract Given the increasing painful repercussions of air pollution caused by massive vehicular emissions and the unsustainable development of transport sectors, the integration of traffic models with emission estimation models has been investigated in depth to evaluate the performance of the pre-determined environment-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 control strategies to mitigate the vehicular emissions in the considered network. The proposed framework integrates a macroscopic analytical traffic model with a macroscopic emissions estimation model to account for the vehicle emissions, and then genetic algorithm (GA) is employed to minimize vehicle emissions. A congested urban area in Xi’an city that consists of two signalized intersections is selected for a case study. The performance of the signal plans derived by the proposed framework is compared with the currently in use signal plans via the microscopic simulator AIMSUN. It indicates that the proposed traffic control scheme reduces expected vehicle trip travel time and vehicle emissions of four sorts of pollutants for the considered area. This optimization framework helps traffic operators to design environment-friendly traffic signal control strategy.