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Modeling Transportation Emissions Us...

Yu, Lang.

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Modeling Transportation Emissions Using Radar-based Vehicle Detection Data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modeling Transportation Emissions Using Radar-based Vehicle Detection Data./
作者:	Yu, Lang.
面頁冊數:	146 p.
附註:	Source: Dissertation Abstracts International, Volume: 78-05(E), Section: A.
Contained By:	Dissertation Abstracts International78-05A(E).
標題:	Transportation. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10251393
ISBN:	9781369442878

Modeling Transportation Emissions Using Radar-based Vehicle Detection Data.
Yu, Lang.

Modeling Transportation Emissions Using Radar-based Vehicle Detection Data. - 146 p.

Source: Dissertation Abstracts International, Volume: 78-05(E), Section: A.

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2016.

This dissertation introduces a new and novel methodology for estimating vehicle emissions at signalized intersections. Radar based vehicle detection systems, when placed at intersection approaches, is able to track vehicle operational characteristics at very high frequency, thus provides an ideal data source for emission estimation. By combining radar based vehicle detection data and MOVES project level analysis operating mode distribution approach, a real-time emission estimation system for signalized intersections is proposed. The Emission Computation Tool for Radar Data is developed to facilitate the automatic and continuous computation of operating mode distribution and emissions. The emission rates computed can also be integrated with existing air dispersion models in order to be used for air quality conformity and hot spot analysis.

ISBN: 9781369442878Subjects--Topical Terms:

555912
Transportation.

Modeling Transportation Emissions Using Radar-based Vehicle Detection Data.
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245 1 0 $a Modeling Transportation Emissions Using Radar-based Vehicle Detection Data.
300 $a 146 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-05(E), Section: A.
500 $a Adviser: David Alan Noyce.
502 $a Thesis (Ph.D.)--The University of Wisconsin - Madison, 2016.
520 $a This dissertation introduces a new and novel methodology for estimating vehicle emissions at signalized intersections. Radar based vehicle detection systems, when placed at intersection approaches, is able to track vehicle operational characteristics at very high frequency, thus provides an ideal data source for emission estimation. By combining radar based vehicle detection data and MOVES project level analysis operating mode distribution approach, a real-time emission estimation system for signalized intersections is proposed. The Emission Computation Tool for Radar Data is developed to facilitate the automatic and continuous computation of operating mode distribution and emissions. The emission rates computed can also be integrated with existing air dispersion models in order to be used for air quality conformity and hot spot analysis.
520 $a A case study is conducted to test the feasibility and validity of the proposed real-time emission estimation system. The results showed that the data collected should be used for computing a variety of parameters, including traffic volume, average speed, operating mode distribution, total emissions and emission rates for various pollutants. With emission rates, existing pollutant dispersion models such as AERMOD are applied, yielding pollutant concentrations at various locations, providing additional functionalities to the system. Evaluation results showed that the traffic volume and emission rates computed matches closely with AADT data and EPA's emission standards.
520 $a Finally, an operating mode based macroscopic emission model is developed by using both empirical data from the case study as well as incorporating existing traffic flow dynamics model. This predictive model is based on estimating total time spent in each operating mode directly from traffic demand and other variables. Total time idling is modeled using kinematic wave theory and queuing theory, while others are modeled using empirical data. The validation results showed that the model is able to achieve a high degree of accuracy, within approximately 10 percent of emission results computed using the radar data. In conclusion, both the proposed real-time emission estimation system at signalized intersections and the emission model developed showed to yield highly accurate and detailed results, and are applicable in real world intersection locations.
590 $a School code: 0262.
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