Traffic congestion has several adverse effects on urban traffic networks. Increased travel times of vehicles, with the addition of excessive greenhouse emissions, can be listed as harmful effects. To address these issues, transportation engineers aim to reduce private car usage, reduce travel times through different control strategies, and mitigate harmful effects on urban networks. In this study, we introduce an innovative approach to optimizing traffic signal control settings. This methodology takes into account both pedestrian delays and vehicular emissions. Non-dominated sorting genetic algorithm-II and Multi-objective Artificial Bee Colony algorithms are adopted to solve the multi-objective optimization problem. The vehicular emissions are modeled through the MOVES3 emission model and integrated into the utilized microsimulation environment. Initially, the proposed framework is tested on a hypothetical test network, followed by a real-world case study. Results indicate a significant improvement in pedestrian delays and lower emissions.

交通拥堵对城市交通网络有多种不利影响。车辆行驶时间的增加，再加上过量的温室气体排放，都可以被列为有害影响。为了解决这些问题，交通工程师的目标是减少私家车的使用，通过不同的控制策略减少出行时间，并减轻对城市网络的有害影响。在这项研究中，我们介绍了一种优化交通信号控制设置的创新方法。该方法考虑了行人延误和车辆排放。采用非支配排序遗传算法-II和多目标人工蜂群算法来解决多目标优化问题。车辆排放通过 MOVES3 排放模型进行建模，并集成到所使用的微观模拟环境中。最初，所提出的框架在假设的测试网络上进行测试，然后进行实际案例研究。结果表明，行人延误情况显着改善，排放量降低。