Association between Transportation Infrastructure, and its environmental health exposures : Developing a Comprehensive Machine Learning Algorithm
Transportation infrastructure plays a vital role in urban life by providing mobility and accessibility for people and goods, but also bring externalities such as air pollution. Research has been devoted to study negative health outcomes and environmental injustice due to transportation externalities. New methods, however, are still required to overcome the data scarcity and improve the accuracy of existing data which are mostly based on estimations rather than actual exposure effects. Furthermore, while different transportation-related factors are commonly considered individually in estimating exposure impacts, new and innovative approaches are critically needed to predict collective exposures to correlated factors in urban environments. With such more comprehensive and interdisciplinary understanding of the complexity of transportation-related exposures, we also need to move beyond top-down planning approaches, to ones that incorporate the involvement and behavioral reactions of stakeholders to changes in their living environments. This research proposal aims to develop a new exposure modeling based on artificial intelligence. The deep learning algorithm estimates exposure to different aspects of the transportation system by detecting the features in publicly available data such as Google Street View and aerial images. The air quality, active transportation infrastructure, and green spaces are a transportation system aspects that this proposal will estimate population exposure to cover a long range of exposure indices. The research team will implement their modeling framework in two case studies in Dallas Texas and Washington DC with different urban forms and transportation system patterns so the team can compare the outcomes under different conditions. The exposure modeling platform then will be used to launch a user interface that will enable the public user to evaluate their exposure to the transportation system.
Language
- English
Project
- Status: Terminated
- Funding: $283,828
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Contract Numbers:
CTEDD 019-20
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Sponsor Organizations:
Center for Transportation Equity, Decisions and Dollars (CTEDD)
University of Texas at Arlington
Arlington, TX United States 76019University of Texas at Arlington
Box 19308
Arlington, TX United States 76019-0308Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Managing Organizations:
Center for Transportation Equity, Decisions and Dollars (CTEDD)
University of Texas at Arlington
Arlington, TX United States 76019University of Texas at Arlington
Box 19308
Arlington, TX United States 76019-0308 -
Performing Organizations:
University of Texas at Arlington
Box 19308
Arlington, TX United States 76019-0308Cornell University
Department of City and Regional Planning
Ithaca, NY United States -
Principal Investigators:
Hamidi, Shima
Tayarani, Mohammad
Shahmoradi, Amir
- Start Date: 20190701
- Expected Completion Date: 20210130
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers
Subject/Index Terms
- TRT Terms: Air quality; Algorithms; Artificial intelligence; Case studies; Environmental impacts; Health; Infrastructure; Machine learning; Urban areas
- Geographic Terms: Dallas (Texas); Washington (District of Columbia)
- Subject Areas: Environment; Planning and Forecasting; Safety and Human Factors; Transportation (General);
Filing Info
- Accession Number: 01710421
- Record Type: Research project
- Source Agency: Center for Transportation Equity, Decisions and Dollars (CTEDD)
- Contract Numbers: CTEDD 019-20
- Files: UTC, RIP
- Created Date: Jul 4 2019 11:29AM