期刊
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT
卷 69, 期 -, 页码 107-122出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.trd.2019.01.028
关键词
Road grade; Speed and acceleration joint distribution (SAJD); Vehicle energy use and emissions; Microscale pollutant dispersion modeling
资金
- USDOT's University Transportation Center program via the National Center for Sustainable Transportation
This study explores how grade impacts vehicle operations, emissions, and pollutant exposures along freeways. Vehicle speed trajectories from freeways are classified by average speed, indicating the traffic conditions experienced by the vehicle involved. The study describes how the shape characteristics of the speed-acceleration joint distribution (SAJD) is changed in response to road grade. Under uncongested conditions, operations are sensitive to grade changes, with vehicles observed to operate more gently (lower acceleration rates) as grade increases. Given clear descriptions of grade impact on operations on freeways, the study explores the impact of integrating grade, as well as the grade-SAJD correlation on modeling of emissions. A case study of a 9.5-mile freeway corridor was conducted to explore the potential impacts on near-road PM2.5 dispersion modeling. For comparative purposes, emissions are estimated for scenarios that incorporate both grade and the correlated changes in operating conditions (observed conditions), ignoring grade but employing observed operating conditions, and including grade but ignoring changes in operating conditions resulting from grade. Comparison the dispersion results with the National Ambient Air Quality Standards (NAAQS) indicates that the bias caused by ignoring road grade is non-negligible. In contrast, the bias caused by applying actual grade, but ignoring grade-SAJD correlation is much less significant. The study confirms the integrating road grade may be critical for transportation conformity and PM2.5 hotspot analysis. While ignoring grade-SAJD correlation did not seem to cause significant bias in near-road air quality modeling that would require serious attention, unless concentrations are close to a NAAQS limit.
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