Vehicle Energy Dataset (VED), A Large-scale Dataset for Vehicle Energy Consumption Research
| Title: | Vehicle Energy Dataset (VED), A Large-scale Dataset for Vehicle Energy Consumption Research |
|---|---|
| Authors: | Oh, G. S., Leblanc, David J., Peng, Huei |
| Publication Year: | 2019 |
| Collection: | Physics (Other) |
| Subject Terms: | Physics - Physics and Society |
| More Details: | We present Vehicle Energy Dataset (VED), a novel large-scale dataset of fuel and energy data collected from 383 personal cars in Ann Arbor, Michigan, USA. This open dataset captures GPS trajectories of vehicles along with their time-series data of fuel, energy, speed, and auxiliary power usage. A diverse fleet consisting of 264 gasoline vehicles, 92 HEVs, and 27 PHEV/EVs drove in real-world from Nov, 2017 to Nov, 2018, where the data were collected through onboard OBD-II loggers. Driving scenarios range from highways to traffic-dense downtown area in various driving conditions and seasons. In total, VED accumulates approximately 374,000 miles. We discuss participant privacy protection and develop a method to de-identify personally identifiable information while preserving the quality of the data. After the de-identification, we conducted case studies on the dataset to investigate the impacts of factors known to affect fuel economy and identify energy-saving opportunities that hybrid-electric vehicles and eco-driving techniques can provide. The case studies are supplemented with a number of examples to demonstrate how VED can be utilized for vehicle energy and behavior studies. Potential research opportunities include data-driven vehicle energy consumption modeling, driver behavior modeling, machine and deep learning, calibration of traffic simulators, optimal route choice modeling, prediction of human driver behaviors, and decision making of self-driving cars. We believe that VED can be an instrumental asset to the development of future automotive technologies. The dataset can be accessed at https://github.com/gsoh/VED. Comment: 11 pages, 15 figures |
| Document Type: | Working Paper |
| Access URL: | http://arxiv.org/abs/1905.02081 |
| Accession Number: | edsarx.1905.02081 |
| Database: | arXiv |
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| Items | – Name: Title Label: Title Group: Ti Data: Vehicle Energy Dataset (VED), A Large-scale Dataset for Vehicle Energy Consumption Research – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Oh%2C+G%2E+S%2E%22">Oh, G. S.</searchLink><br /><searchLink fieldCode="AR" term="%22Leblanc%2C+David+J%2E%22">Leblanc, David J.</searchLink><br /><searchLink fieldCode="AR" term="%22Peng%2C+Huei%22">Peng, Huei</searchLink> – Name: DatePubCY Label: Publication Year Group: Date Data: 2019 – Name: Subset Label: Collection Group: HoldingsInfo Data: Physics (Other) – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Physics+-+Physics+and+Society%22">Physics - Physics and Society</searchLink> – Name: Abstract Label: Description Group: Ab Data: We present Vehicle Energy Dataset (VED), a novel large-scale dataset of fuel and energy data collected from 383 personal cars in Ann Arbor, Michigan, USA. This open dataset captures GPS trajectories of vehicles along with their time-series data of fuel, energy, speed, and auxiliary power usage. A diverse fleet consisting of 264 gasoline vehicles, 92 HEVs, and 27 PHEV/EVs drove in real-world from Nov, 2017 to Nov, 2018, where the data were collected through onboard OBD-II loggers. Driving scenarios range from highways to traffic-dense downtown area in various driving conditions and seasons. In total, VED accumulates approximately 374,000 miles. We discuss participant privacy protection and develop a method to de-identify personally identifiable information while preserving the quality of the data. After the de-identification, we conducted case studies on the dataset to investigate the impacts of factors known to affect fuel economy and identify energy-saving opportunities that hybrid-electric vehicles and eco-driving techniques can provide. The case studies are supplemented with a number of examples to demonstrate how VED can be utilized for vehicle energy and behavior studies. Potential research opportunities include data-driven vehicle energy consumption modeling, driver behavior modeling, machine and deep learning, calibration of traffic simulators, optimal route choice modeling, prediction of human driver behaviors, and decision making of self-driving cars. We believe that VED can be an instrumental asset to the development of future automotive technologies. The dataset can be accessed at https://github.com/gsoh/VED.<br />Comment: 11 pages, 15 figures – Name: TypeDocument Label: Document Type Group: TypDoc Data: Working Paper – Name: URL Label: Access URL Group: URL Data: <link linkTarget="URL" linkTerm="http://arxiv.org/abs/1905.02081" linkWindow="_blank">http://arxiv.org/abs/1905.02081</link> – Name: AN Label: Accession Number Group: ID Data: edsarx.1905.02081 |
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| RecordInfo | BibRecord: BibEntity: Subjects: – SubjectFull: Physics - Physics and Society Type: general Titles: – TitleFull: Vehicle Energy Dataset (VED), A Large-scale Dataset for Vehicle Energy Consumption Research Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Oh, G. S. – PersonEntity: Name: NameFull: Leblanc, David J. – PersonEntity: Name: NameFull: Peng, Huei IsPartOfRelationships: – BibEntity: Dates: – D: 19 M: 04 Type: published Y: 2019 |
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