城市快速路车辆排放估算中纳入当地道路等级和时段交通的车辆功率分析

Heng Wei
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摘要

车辆比功率(VSP)通常被定义为代表车辆发动机的瞬时功率。车辆工况对排放和能耗估算的影响已被广泛应用,在车辆逐秒运行的基础上,将其与车速、道路坡度和车辆加减速联系起来。因此,VSP作为一个关键因素纳入车辆排放模型在移动。然而,在实际应用中,通过收集和使用本地化的等级和时间交通数据来准确地描绘VSP分布总是很麻烦的。因此,有必要明确这些因素对公路车辆排放估算的影响。本文在前人研究的基础上,深入研究了不同高速公路等级和时段交通因素对VSP分布特征及其影响。利用从辛辛那提地区I-75州际高速公路段收集的全球定位系统(GPS)数据,通过拟合优度检验方法确定了具有箱范围的统计分布模型。数据是在上午、下午和中午时段选定的30公里城市高速公路上收集的。然后从GPS轨迹数据中提取代表车辆运行条件的数据集,用于VSP计算。分布拟合结果表明,在较高的速度变化条件下,尾流分布能够捕捉到VSP在所有等级箱上的大部分分布特征,而在-4% ~ 4%的速度变化条件下,广义logistic分布对样本数据的拟合效果较好。此外,在时间差异背后的速度变化也被认为是城市高速公路VSP分布的一个贡献因素。该研究增强了对VSP按道路坡度分布的理解和建模,有助于编制MOVES车辆运行模式分布输入。
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Incorporating Local Road Grades and Times-of-Day Traffic into Vehicle Specific Power Profiling for Urban Freeway Vehicle Emission Estimation
Vehicle Specific Power (VSP) is conventionally defined to represent the instantaneous vehicle engine power. It has been widely utilized that the impact of vehicle operating conditions on emission and energy consumption estimation is associated with vehicle speed, roadway grade and vehicle acceleration or deceleration on the basis of the second-by-second vehicle operation. VSP is hence incorporated as a key contributing factor into the vehicle emission models in MOVES. For practical application, however, it is always cumbersome to accurately profile VSP distribution by collecting and using localized grade and times-of-day traffic data. Therefore, it is necessary to clarify the impacts of these factors on highway vehicle emission estimation. This paper presents a study in which previous studies are extended by deeply investigating the characteristics of VSP distributions and their impacts due to varying freeway grades, as well as time-of-day traffic factors. Statistical distribution models with a scope of bins is identified through a goodness of fit testing approach by using the Global Positioning System (GPS) data collected from the interstate freeway I-75 segments in the Cincinnati area. The data was collected at a selected length of 30 km urban freeway for AM, PM and Mid-day periods. The datasets representing the vehicle operating conditions for the VSP calculation were then extracted from the GPS trajectory data. The results of distribution fitting show that the Wake by distribution is able to capture most distribution characteristics of VSP at all grade bins under a higher speed variation condition, and the generalized logistic distribution fits the sample data better at grade bins between -4% and 4%when the speed variation is lower. In addition, the speed variation lying behind the times-of-day differences is also identified to be a contributing factor of urban freeway VSP distribution. The enhanced understanding and modelling of VSP distribution by roadway grade provided by the study can facilitate the preparation of MOVES vehicle operating mode distribution inputs.
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