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

ABSTRACT

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 (AADT) measures derived from the Highway Performance Monitoring System (HPMS). 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, incentivize energy-efficient routing, and estimate energy impact due to 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, utilizing vehicle probe speed and count data in conjunction with machine learning methods in real-time. The real-time pipeline is capable of delivering energy estimates within a couple seconds upon query to its interface. The proposed method was evaluated on the transportation network of the metropolitan area of Chattanooga, TN. The model results were validated with ground truth traffic volume data collected in the field and from AADT. Energy consumption was estimated and compared for three scenarios, including a COVID-19 period, free flow condition, and peak hour, to demonstrate the effectiveness of the proposed method, estimate energy reduction due to mitigation policies to slow COVID-19 spread, and measure energy loss due to congestion.