Machine Learning Predictive Model: Prioritizing Expenditures in Public Transportation Based on Transportation Emissions
Publication: Construction Research Congress 2024
ABSTRACT
The United States (US) is the largest Greenhouse Gas (GHG) emitter globally, with its transportation sector being a major carbon dioxide contributor. This is a matter of significant concern, given the impact of these emissions on climate change. Prioritizing emissions-reducing transportation projects is crucial to address this problem. Currently, such policies are generally discussed at the national level. However, since individual states have varying infrastructure and priorities, this paper proposes studying carbon dioxide emissions at the state level of granularity. To achieve this, ensemble methods such as Random Forest, Extremely Randomized Trees, AdaBoost, and Gradient Boosted Decision Trees, along with a meta-estimator, are used to predict statewide carbon dioxide emissions using variables related to statewide transportation systems. Based on this analysis, we suggest that states should discourage car dependency in a way that is feasible for local characteristics, as well as promote public transit to reduce carbon dioxide emissions.
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REFERENCES
Arroyo, V., Zyla, K., and Pacyniak, G. (2017). “New strategies for reducing transportation emissions and preparing for climate impacts.” Fordham Urb. LJ, 44, 919.
Asensio, O. I., Mi, X., and Dharur, S. (2020). “Using machine learning techniques to aid environmental policy analysis: a teaching case regarding big data and electric vehicle charging infrastructure.” Case Studies in the Environment, 4(1), 961302.
Banister, D., Pucher, J., Lee-Gosselin, M., and Lee, M. (2007). “Making sustainable transport politically and publicly acceptable: Lessons from the EU, USA and Canada.” Institutions and sustainable transport: Regulatory reform in advanced economies, 17–50.
Bleviss, D. L. (2021). “Transportation is critical to reducing greenhouse gas emissions in the United States.” Wiley Interdisciplinary Reviews: Energy and Environment, 10(2), e390.
Brathwaite, T., Vij, A., and Walker, J. L. (2017). “Machine learning meets microeconomics: The case of decision trees and discrete choice.”.
Burrows, M., Burd, C., and McKenzie, B. (2021). “Commuting by Public Transportation in the United States: 2019.”.
Clark, S., Harper, S., and Weber, B. (2022). “Growing Up in Rural America.” RSF: The Russell Sage Foundation Journal of the Social Sciences, 8(4), 1–47.
Davis, J. (2019). “Trends in Per Capita VMT.”, Eno Center for Transportation.
Geurts, P., Ernst, D., and Wehenkel, L. (2006). “Extremely randomized trees.” Machine learning, 63, 3–42.
Hastie, T., Tibshirani, R., and Friedman, J. (2009). The elements of statistical learning. Cited on, 33.
Ko, Y. D. (2019). “An efficient integration of the genetic algorithm and the reinforcement learning for optimal deployment of the wireless charging electric tram system.” Computers & Industrial Engineering, 128, 851–860.
Kurt, N., Ozturk, O., and Beken, M. “Estimation of gas emission values on highways in Turkey with machine learning.” Proc., 2021 10th International Conference on Renewable Energy Research and Application (ICRERA), IEEE, 443–446.
Lee, C., and Miller, J. S. (2017). “A probability-based indicator for measuring the degree of multimodality in transportation investments.” Transportation Research Part A: Policy and Practice, 103, 377–390.
Magazzino, C., and Mele, M. (2021). “On the relationship between transportation infrastructure and economic development in China.” Research in Transportation Economics, 88, 100947.
Marcy, C., and Sanchez, B. (2017). Power sector carbon dioxide emissions fall below transportation sector emissions. Energy Information Administration. Washington, DC.
Matute, J. M., and Chester, M. V. (2015). “Cost-effectiveness of reductions in greenhouse gas emissions from High-Speed Rail and urban transportation projects in California.” Transportation Research Part D: Transport and Environment, 40, 104–113.
Rad, P. F. (2001). Project estimating and cost management, Berrett-Koehler Publishers.
Tayefeh Hashemi, S., Ebadati, O. M., and Kaur, H. (2020). “Cost estimation and prediction in construction projects: A systematic review on machine learning techniques.” SN Applied Sciences, 2, 1–27.
Tsakalidis, A., van Balen, M., Gkoumas, K., and Pekar, F. (2020). “Catalyzing sustainable transport innovation through policy support and monitoring: The case of TRIMIS and the European green deal.” Sustainability, 12(8), 3171.
Wang, Y., and Boggio-Marzet, A. (2018). “Evaluation of eco-driving training for fuel efficiency and emissions reduction according to road type.” Sustainability, 10(11), 3891.
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Published online: Mar 18, 2024
ASCE Technical Topics:
- [Inorganic compounds]
- Air pollution
- Artificial intelligence and machine learning
- Carbon compounds
- Carbon dioxide
- Chemicals
- Chemistry
- Computer models
- Computer programming
- Computing in civil engineering
- Ecosystems
- Emissions
- Engineering fundamentals
- Environmental engineering
- Infrastructure
- Models (by type)
- Organic compounds
- Pollution
- Public transportation
- Thermal pollution
- Transportation engineering
- Trees
- Vegetation
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