Real-Time Highly Resolved Spatial-Temporal Vehicle Energy Consumption Estimation Using Machine Learning and Probe Data

Severino, Joseph; Hou, Yi; Nag, Ambarish; Holden, Jacob; Zhu, Lei; Ugirumurera, Juliette; Young, Stanley; Jones, Wesley; Sanyal, Jibo

doi:10.1177/03611981211039163

Real-Time Highly Resolved Spatial-Temporal Vehicle Energy Consumption Estimation Using Machine Learning and Probe Data

Conference · Tue Feb 01 04:00:00 EST 2022

DOI:https://doi.org/10.1177/03611981211039163· OSTI ID:1884032

Severino, Joseph ^[1]; Hou, Yi ^[1]; Nag, Ambarish ^[1]; Holden, Jacob ^[1]; Zhu, Lei ^[1]; Ugirumurera, Juliette ^[1]; Young, Stanley ^[1]; Jones, Wesley ^[1]; ^[2]

National Renewable Energy Laboratory (NREL)
ORNL

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.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE; USDOE Office of Energy Efficiency and Renewable Energy (EERE)

DOE Contract Number:: AC05-00OR22725

OSTI ID:: 1884032

Country of Publication:: United States

Language:: English

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