DOI: 10.5061/dryad.6wwpzgn60
title: Destination labels for battery electric vehicles in eVMT dataset

description: This dataset provides detailed information on the destination labels for
 the trip trajectory and charging of 65 Battery Electric Vehicles in
 California in the eVMT dataset. This dataset includes the Tesla Model S
 and Chevrolet Bolt only. Additionally, the repository contains a Python
 script that trains a deep-learning model to predict the driving behavior
 of the drivers in this dataset. The aim of this model is to forecast the
 charging needs of the drivers, so that they can align their charging needs
 with renewable energy resources availability. This way, the impact of
 fossil fuel resources in charging the vehicle can be decreased, and carbon
 emission per mile driven can be reduced.
description: This dataset was collected by the Electric Vehicle Research
 Center at the University of California, Davis (2015-2020). A data logger
 was connected to each vehicle to gather information about the trip
 trajectory and charging details of the vehicles. The destination labels
 were generated using the DB-Scan clustering technique to cluster the
 destinations based on their number at each location within a certain
 radius into Home, Work, and Other locations.
description: # eVMT dataset
 [https://doi.org/10.5061/dryad.6wwpzgn60](https://doi.org/10.5061/dryad.6wwpzgn60) ## Description of the data and file structure The research conducted in this study involved configuring models using a subset of data from the eVMT project's dataset. This dataset was created as part of a California-wide study that spanned five years (2015-2020). The primary objective of the study was to gain insights into the driving and charging behaviors of battery-electric vehicles. Data was collected from approximately 400 households and 800 vehicles, out of which 132 were BEVs. The BEV dataset includes around 182,000 trips and 39,000 charges from 132 EVs, and it provides second-by-second on-road data. The data logger recorded important driving and charging characteristics like speed and GPS coordinates at a second-by-second interval. For this study, a subset of data from the ***24 Chevrolet Bolts and 42 Tesla Model S vehicles was selected*** for training and testing the LSTM model. However, due to the data management agreement and privacy of the vehicle's owner, only the destination label is shareable with the public. ### eVMT Dataset Highlights: * **Study Period:** 2015-2020. * **Focus:** Insights into BEV driving and charging behaviors. * **Data Source:** About 400 households with 800 vehicles, including 132 BEVs. * **Data Points:** Approximately 182,000 trips and 39,000 charging sessions from 132 BEVs. * **Details:** Second-by-second on-road data, including speed and GPS coordinates. ### Available Data: * Due to privacy agreements, only the destination label of trips and charging sessions is presented in this repository. * **Subset Used:** Data from **24 Chevrolet Bolts and 42 Tesla Model S vehicles**. ### This Repository Contents: 1. **Destination Labels:** CSV files containing trip and charging session destination labels for 65 BEVs. 2. **Python Scripts:** Training and testing models for each vehicle in the dataset. ### About the CSV Dataset: * **Contents:** Destination labels for vehicle trips including trip IDs, vehicle models, clustering results, and actual labels. * **Clustering:** Uses DB Scan Analysis to group GPS coordinates into 'Home', 'Work', or 'Other Location'. ### Python Scripts Details: * **Files Naming:** Each script is named after the vehicle number, e.g., `LSTM-1087_final-lstm_final.py`. * **Matching:** Each vehicle number in the CSV file corresponds to a specific Python script.