Destination labels for battery electric vehicles in eVMT dataset

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., 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., , # 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....