以优化城市多车型快速路交通系统拥堵和排放为目标, 综合考虑了走行时间(total time spent, TTS)、走行距离(total travel distance, TTD)、匝道排队、尾气排放和燃油消耗这5个性能指标, 改进了多车型快速路宏观交通流模型Multi-class METANET和多车型排放模型Multi-class VT-macro. 提出了一个新的高维多目标优化算法——模糊非支配排序遗传算法(fuzzy non-dominated sorting genetic algorithm, FNSGA-Ⅲ), 对快速路的匝道汇入率和主路的可变限速(variable speed limit, VSL)值进行了优化, 实现了缓解主路和匝道交通拥堵以及节能减排的目标. 提出的FNSGA-Ⅲ算法, 基于自适应模糊推理系统(adaptive network-base fuzzy inference system, ANFIS), 对下一时刻高维多目标优化的超平面进行预测, 能够有效引导算法在迭代过程中的进化方向, 提高算法的收敛速度. 基于上海市广中路实际路网进行仿真实验. 结果表明, 与现有的单目标遗传算法和高维多目标NSGA-Ⅲ算法相比, FNSGA-Ⅲ算法结合改进的多车型宏观交通流模型, 可以更合理地设置期望速度与匝道控制策略, 更为有效地环缓解快速路的交通拥堵和排放.

To minimise traffic congestion and emissions on urban multi-class expressways, five performance indicators were considered, such as total travel spent (TTS), total travel distance (TTD), ramp queuing, exhaust emissions, and fuel consumption. The macro traffic flow model (Multi-class METANET) and emission model (Multi-class VT-macro) were improved. A new high-dimensional multi-objective optimisation algorithm, fuzzy non-dominated sorting genetic algorithm (FNSGA-Ⅲ), was proposed to optimise the on-ramp inflow rate on expressways and the variable speed limit (VSL) on main roads. The new algorithm, FNSGA-Ⅲ, aimed to alleviate traffic congestion, save energy, and reduce emissions simultaneously. This proposed algorithm, which was based on an adaptive network-base fuzzy inference system (ANFIS), could effectively guide the evolution direction of the optimisation algorithm in the iteration process and improve the convergence speed. Based on the actual road network of Guangzhong Road in Shanghai, a simulation was performed. The results showed that the proposed FNSGA-Ⅲ algorithm and the improved multi-class macro traffic flow model could reasonably set the desired speed and ramp control strategy. Hence, compared with the existing single-objective genetic algorithm and multi-objective NSGA-Ⅲ, the proposed algorithm and improved model could effectively alleviate traffic congestion and reduce emissions.