01764416

Component

https://doi.org/10.1177/03611981211039163

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.

Joseph Severino https://orcid.org/0000-0002-7419-7841 © National Academy of Sciences: Transportation Research Board 2021.

TRBAM-21-03287

English

0000-0002-7419-7841

0000-0002-8173-0923

0000-0001-5174-4673

0000-0002-4384-1171

0000-0003-0170-1178

0000-0002-8911-2951

0000-0002-3955-9608

pp 213-226

2022-2

Transportation Research Record: Journal of the Transportation Research Board

Digital/other

Figures; References (40) ; Tables

COVID-19; Energy consumption; Estimating; Free flow speeds; Machine learning; Metropolitan areas; Motor vehicles; Peak hour traffic; Probe vehicles; Real time information; Validation

Chattanooga (Tennessee)

Data and Information Technology; Energy; Highways; Vehicles and Equipment

TRIS, TRB, ATRI

Dec 23 2020 11:25AM