# Neighbor-Based Optimized Logistic Regression Machine Learning Model For Electric Vehicle Occupancy Detection

arXiv: https://arxiv.org/abs/2204.13702 · HTML: https://mlsanity.com/paper/2204.13702/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 28 Apr 2022.

## Builds on (most cited first, 4 of 4)

- [SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives](https://mlsanity.com/paper/1407.0202/) (2014)
- [Minimizing Finite Sums with the Stochastic Average Gradient](https://mlsanity.com/paper/1309.2388/) (2013)
- [Multistep Electric Vehicle Charging Station Occupancy Prediction using Hybrid LSTM Neural Networks](https://mlsanity.com/paper/2106.04986/) (2021)
- [LSTM-based Space Occupancy Prediction towards Efficient Building Energy Management](https://mlsanity.com/paper/2012.08114/) (2020)

## Abstract (arXiv)

This paper presents an optimized logistic regression machine learning model that predicts the occupancy of an Electric Vehicle (EV) charging station given the occupancy of neighboring stations. The model was optimized for the time of day. Trained on data from 57 EV charging stations around the University of California San Diego campus, the model achieved an 88.43% average accuracy and 92.23% maximum accuracy in predicting occupancy, outperforming a persistence model benchmark.