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Are average speed emission functions scale-free?

机译:平均速度排放函数是否无垢?

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摘要

Although emission models have been designed using vehicle data over driving cycles of a few minutes, they are often applied at large scale to estimate total emission (inventories). In between, there is a range of scales in use in traffic and environmental studies (road sections, sub-areas, etc.). Coupling a traffic microsimulation with COPERT emission factors at different scales reveals scaling biases. We compare network fuel consumption (FC) and nitrogen oxide (NOx) emissions resulting from emission calculations based on different spatial decompositions. The results show that for an area of Paris covering 3 km(2), the differences due to the aggregation scale for emissions range from 5 to 17% depending on the pollutant, spatial partitioning and traffic conditions. These discrepancies can be reduced using a distance-weighted mean speed, which is not a scale-consistent definition of mean travel speed. They can almost be cancelled by using a correction term derived analytically in this paper, thus consistency can be guaranteed between emissions assessed at different scales. Finally, a case study shows that it is possible to evaluate FC and NOx emissions on a large-scale network from a sample of traffic data (probes), and obtain the corrective term to be applied to remove scaling bias. The most critical step is the accurate estimation of the total travel distance. The gaps were successfully reduced to a maximum of 8% in congestion for a penetration rate of about 20%.
机译:尽管在几分钟的驱动周期上使用了车辆数据设计了发射模型,但它们通常以大规模应用以估计总排放(库存)。在其间,交通和环境研究中使用的一系列比例(道路段,子区域等)。耦合不同尺度的Copert排放因子的流量微疗显示缩放偏差。我们基于不同空间分解的发射计算,比较网络燃料消耗(Fc)和氮氧化物(NOx)排放。结果表明,对于覆盖3公里(2)的巴黎区域,由于污染物,空间分区和交通状况,排放量的聚集量表导致的差异为5%至17%。可以使用距离加权平均速度来减少这些差异,这不是平均行程速度的比例一致的定义。它们几乎可以通过在本文中使用分析衍生的校正项来取消,因此可以在不同尺度评估的排放之间保证一致性。最后,案例研究表明,可以从交通数据(探测器)样本中评估大规模网络上的Fc和NOx排放,并获得应用纠正术语以删除缩放偏置。最关键的步骤是准确估计总行程距离。对于渗透率约为20%,空隙成功降至最高8%。

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