A novel approach to estimate emissions from large transportation networks: Hierarchical clustering-based link-driving-schedules for EPA-MOVES using dynamic time warping measures
Open accessAbstract
EPA-MOVES (Motor Vehicle Emission Simulator) is often integrated with traffic simulators to assess emission levels of large-scale urban networks with signalized intersections. High variations in speed profiles exist in the context of congested urban networks with signalized intersections. The traditional average-speed-based emission estimation technique with EPA-MOVES provides faster execution while underestimates the emissions in most cases because of ignoring the speed variation at congested networks with signalized intersections. In contrast, the atomic second-by-second speed profile (i.e., the trajectory of each vehicle)-based technique provides accurate emissions at the cost of excessive computational power and time. We addressed this issue by developing a novel method to determine the link-driving-schedules (LDSs) for the EPA-MOVES tool. Our research developed a hierarchical clustering technique with dynamic time warping similarity measures (HC-DTW) to find the LDS for EPA-MOVES that is capable of producing emission estimates better than the average-speed-based technique with execution time faster than the atomic speed profile approach. We applied the HC-DTW on a sample data from a signalized corridor and found that HC-DTW can significantly reduce computational time without compromising the accuracy. The developed technique in this research can substantially contribute to the EPA-MOVES-based emission estimation process for large-scale urban transportation network by reducing the computational time with reasonably accurate estimates. This method is highly appropriate for transportation networks with higher variation in speed such as signalized intersections. Experimental results show error difference ranging from 2% to 8% for most pollutants except .
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ML methods can help impute missing data for precise bottom-up estimation of GHG emissions and are applied in simulation models of vehicle emissions.
“Similarly, ML methods can help with imputing missing data for precise bottom-up estimation of GHG emissions [583] and they are also applied in simulation models of vehicle emissions [46].”
From Tackling climate change with machine learning (2023) · cites this paper as [46]
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Aziz, H. M. A., & Ukkusuri, S. V.
- Affiliations
- Oak Ridge National Laboratory (US)
- Purdue University West Lafayette (US)
- Corresponding author
- Aziz, H. M. Abdul
- Venue
- International Journal of Sustainable Transportation
- Publisher
- Informa UK Limited
- Published
- 2017-08-09
- Volume
- 12
- Issue
- 3
- Pages
- 192-204
- ISSN
- 1556-8318 (ISSN-L)
- 1556-8334
- OpenAlex
- W2732316106
- Cited by
- 15
- References
- 24
- Citations by year
- 2025: 1 · 2024: 1 · 2022: 5 · 2021: 4 · 2020: 3 · 2019: 1
- FWCI
- 0.69
- Type
- journal article
- Language
- en
- Open access
- green
- www.osti.gov
- Locations
- International Journal of Sustainable Transportation (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Vehicle emissions and performance
- Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
- Social Sciences › Transportation › Transportation Planning and Optimization
- Keywords
- vehicle emissions estimation, hierarchical clustering, dynamic time warping, signalized intersections, traffic congestion, urban transportation networks, computational efficiency
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