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Smart Data Driven and Adaptive Modeling Framework for Quantifying Dynamic TAZ-based Household Travel Carbon Emissions

Yao, Zhuo
http://orcid.org/0000-0002-6478-8971
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1460731814

Abstract Details

2016, PhD, University of Cincinnati, Engineering and Applied Science: Civil Engineering.
The conventional carbon emission-modeling framework focuses on the link-based emissions and then aggregate to regional inventory. This approach is incapable of tracing emissions back to its geographical origin and providing information on areas where adaptive planning policies and strategies are needed. Recent studies also indicate potential deficiencies in converting four-step travel demand outputs into the inputs of emission models. Emission models often rely on four-step models for vehicle activity inputs. However, these models are mostly calibrated and validated using aggregated daily traffic data. No data sources are available to validate the models at the hourly or the most desired second-by-second level for emission estimates. The recent advancement of mobile device sensors and data transmitting technologies provide travel trajectories (e.g., latitude, longitude, speed, acceleration, altitude) collected from the users of smart phones or other GPS-enabled devices. The availability of such data sources will actually provide new opportunities of enhancing our understanding and modeling of the dynamics between land use pattern, travel behavior, and the associated environmental impacts. These trends call for the emergence of quick-response modeling framework that could be supported by the smart data source. In this research, a research question is proposed to well direct the proposed research: is it positively possible to use the Smart-Data structured data sets to unveil the sophisticated dynamics between land use changes and its associated carbon emission impacts, if a smart data adaptive modeling framework for this attempt is well developed? The answer to the research question will benefit the integration of the actual and scenario-based land use visioning and planning, demographic changes, transportation emission analysis, and computer forecasting and evaluation of future scenarios. This research makes it possible to assess the household travel carbon footprint and provides supportive models, and data sets for possible carbon emission mitigation through land use policies and adaptation. The quick response modeling framework using GPS survey data simulated Smart Data provides connections among land use, household socioeconomic and their travel carbon emissions. It is a practical tool for Metropolitan Planning Organizations (MPO) and other planning agencies to compare alternative planning scenarios with spatial details. The responsiveness, or sensitivity, of the model to changes in key inputs indicates whether the model can reasonably estimate the expected change in carbon emissions resulting from the changes in the socioeconomic characteristics.
Heng Wei, Ph.D. (Committee Chair)
Andrew S. Rohne, M.ENG. (Committee Member)
Steven Buchberger, Ph.D. (Committee Member)
Xinhao Wang, Ph.D. (Committee Member)
112 p.
Transportation
Household Travel Carbon Emissions; Sustainable Development; Spatial Modeling

Recommended Citations

Citations

  • Yao, Z. (2016). Smart Data Driven and Adaptive Modeling Framework for Quantifying Dynamic TAZ-based Household Travel Carbon Emissions [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1460731814

    APA Style (7th edition)

  • Yao, Zhuo. Smart Data Driven and Adaptive Modeling Framework for Quantifying Dynamic TAZ-based Household Travel Carbon Emissions. 2016. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1460731814.

    MLA Style (8th edition)

  • Yao, Zhuo. "Smart Data Driven and Adaptive Modeling Framework for Quantifying Dynamic TAZ-based Household Travel Carbon Emissions." Doctoral dissertation, University of Cincinnati, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1460731814

    Chicago Manual of Style (17th edition)

ucin1460731814
563
© 2016, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.
Release 3.2.12