摘要：

实时仿真与评价信控策略的碳减排效果是策略调优的必要基础。在现有研究中，不耦合交通仿真或仅耦合宏中观交通仿真的碳排计算模型面临策略评价可控性不足、分辨率过低等问题，耦合微观交通仿真的碳排计算模型存在碳排模块与我国车型特征差异大的问题，且耦合模型存在参数标定的排放计算区段划分不合理、未考虑实际车辆工况特征的问题。因此，开展面向实时信控策略评价的交通-碳排耦合微观仿真模型研究。在建模方面，基于数据传输机制和车型匹配关系耦合微观交通流仿真模型与修正IVE(International Vehicle Emission)排放模型，并利用接口实现车辆运动与碳排同步推演与计算；在标定方面，设计两阶段交通-碳排参数耦合标定方法，阶段1开展考虑车辆运动特征的排放模型标定，阶段2开展考虑车辆行驶工况的交通流模型标定。实验结果表明：小型客车和公交车仿真排放因子与实测值相比，误差分别为10.1%和15.6%；实施“绿波”信控优化后车辆碳排显著降低。所提耦合模型能准确地以高分辨率测算交通碳排放，可以对交通减排措施进行有效的评价。

关键词: 智能交通, 碳减排, 微观交通仿真, 信控策略, 参数标定

Abstract:

Real-time simulation and evaluation of carbon reduction effects of signal control strategies is a necessary prerequisite for strategy optimization. In existing studies, carbon emission calculation models that do not couple with traffic simulation or only couple with macroscopic and mesoscopic traffic simulations are hindered by low-resolution or insufficient controllability for strategy evaluation. Models that couple with microscopic traffic simulation have the problems such as significant differences between the carbon emission module and the characteristics of Chinese car models, unreasonable division of emission calculation segments, and failure to consider actual vehicular operating conditions. Therefore, this article proposes a microscopic co-simulation model of traffic flow and carbon emission for real-time control strategy evaluation. Based on the data transmission mechanism and vehicle model matching relationship, a microscopic traffic flow simulation model and a modified International Vehicle Emission (IVE) model are coupled, and the interface is used to achieve synchronous deduction and calculation of vehicle motion and carbon emissions. In terms of calibration, a two-stage parameter calibration method is designed. In the first stage, the calibration of emission model is carried out with consideration of vehicular motion characteristics. In the second stage, the calibration of traffic simulation is carried out with consideration of actual vehicular travelling conditions. The experimental results show that compared with the measured values, the simulated emission factors of minibuses and buses have errors of 10.1% and 15.6%, respectively. After implementing the ″green wave″ signal control optimization, the carbon emissions of vehicles significantly decrease. The coupled model can accurately measure transportation carbon emissions with high resolution and effectively evaluate transportation emission reduction measures.

Key words: intelligent transportation, carbon emission reduction, microscopic traffic simulation, signal control strategy, parameter calibration