A new screening measure to identify potential carbon monoxide hotspots
A new screening measure to identify potential carbon monoxide hotspots
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DOI：
10.1080/10473289.2000.10464005
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发表时间：
2000-02-01
影响因子：
2.7
通讯作者：
Niemeier, DA
中科院分区：
文献类型：
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作者：
Meng, Y;Niemeier, DA
为了证明交通项目符合国家环境空气质量标准，根据国家实施计划，美国环境保护署（EPA）使用交叉口服务水平（LOS）作为筛选潜在一氧化碳（CO）热点的主要标准之一。虽然交叉口的服务水平是交通量、信号配时以及相关拥堵和延误的度量，但分配的水平仅反映交叉口处每辆车的计算平均停车延误（ASD）。因此，交叉口通常可以在相同的服务水平下运行，但会产生截然不同的预测CO浓度水平。例如，一个两车道的方法在D级服务水平将产生非常不同的水平的CO比五车道的方法也在D级服务水平。本研究探讨了有效性的D级服务水平标准作为一个屏幕识别潜在的CO热点。研究结果表明，与EPA推荐的微尺度模型CAL 3QHCr产生的结果相比，LOS是潜在CO热点的较差预测因子。为了更一致地筛选出那些交叉口，将不会被识别为CO热点使用微尺度模型，一个新的标准，等效红灯时间车辆（ERTV），介绍。使用ERTV的建模结果表明，它是一个更强大的措施，用于识别潜在的CO热点，相反，筛选出那些不太可能被识别为热点的交叉口，使用微尺度模拟结果。
To demonstrate conformity of transportation projects to National Ambient Air Quality Standards in accordance with State Implementation Plans, the U.S. Environmental Protection Agency (EPA) uses intersection level of service (LOS) as one of its major criteria for screening for potential carbon monoxide (CO) hotspots. Although intersection LOS is a measure of traffic volume, signal timing, and related congestion and delay, the assigned level reflects only the computed averaged stopped delay (ASD) per vehicle at the intersection. Thus, intersections can often operate at the same LOS but produce vastly different levels of predicted CO concentrations. For example, a two-lane approach operating at LOS D will produce very different levels of CO than a five-lane approach also operating at LOS D.This study explores the effectiveness of the LOS D criterion as a screen for identifying potential CO hotspots. The study results indicate that LOS is a poor predictor of potential CO hotspots when compared to results generated with the EPA-recommended microscale model CAL3QHCr. To more consistently screen out those intersections that will not be identified as CO hotspots using the micro-scale models, a new criterion, equivalent red-time vehicles (ERTV), is introduced. The modeling results using ERTV suggest that it is a more robust measure for identifying potential CO hotspots, and conversely, screening out those intersections that are not likely to be identified as hotspots using microscale simulation results.