Real-Time Highly Resolved Spatial-Temporal Vehicle Energy Consumption Estimation Using Machine Learning and Probe Data
Real-time highly resolved spatial-temporal vehicle energy consumption is a key missing dimension in transportation data. Most roadway link-level vehicle energy consumption data are estimated using average annual daily traffic measures derived from the Highway Performance Monitoring System; however, this method does not reflect day-to-day energy consumption fluctuations. As transportation planners and operators are becoming more environmentally attentive, they need accurate real-time link-level vehicle energy consumption data to assess energy and emissions; to incentivize energy-efficient routing; and to estimate energy impact caused by congestion, major events, and severe weather. This paper presents a computational workflow to automate the estimation of time-resolved vehicle energy consumption for each link in a road network of interest using vehicle probe speed and count data in conjunction with machine learning methods in real time. The real-time pipeline can deliver energy estimates within a couple seconds on query to its interface. The proposed method was evaluated on the transportation network of the metropolitan area of Chattanooga, Tennessee. The volume estimation results were validated with ground truth traffic volume data collected in the field. To demonstrate the effectiveness of the proposed method, the energy consumption pipeline was applied to real-world data to quantify road transportation-related energy reduction because of mitigation policies to slow the spread of COVID-19 and to measure energy loss resulting from congestion.
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/03611981
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Supplemental Notes:
- Joseph Severino https://orcid.org/0000-0002-7419-7841 © National Academy of Sciences: Transportation Research Board 2021.
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Authors:
- Severino, Joseph
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0000-0002-7419-7841
- Hou, Yi
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0000-0002-8173-0923
- Nag, Ambarish
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0000-0001-5174-4673
- Holden, Jacob
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0000-0002-4384-1171
- Zhu, Lei
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0000-0003-0170-1178
- Ugirurmurera, Juliette
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0000-0002-8911-2951
- Young, Stanley
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0000-0002-3955-9608
- Jones, Wesley
- Sanyal, Jibonananda
- Publication Date: 2022-2
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References; Tables;
- Pagination: pp 213-226
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Serial:
- Transportation Research Record: Journal of the Transportation Research Board
- Volume: 2676
- Issue Number: 2
- Publisher: Sage Publications, Incorporated
- ISSN: 0361-1981
- EISSN: 2169-4052
- Serial URL: http://journals.sagepub.com/home/trr
Subject/Index Terms
- TRT Terms: COVID-19; Energy consumption; Estimating; Free flow speeds; Machine learning; Metropolitan areas; Motor vehicles; Peak hour traffic; Probe vehicles; Real time information; Validation
- Geographic Terms: Chattanooga (Tennessee)
- Subject Areas: Data and Information Technology; Energy; Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01764416
- Record Type: Publication
- Report/Paper Numbers: TRBAM-21-03287
- Files: TRIS, TRB, ATRI
- Created Date: Feb 4 2021 4:48PM