# Investigating the Spatiotemporal Charging Demand and Travel Behavior of Electric Vehicles Using GPS Data: A Machine Learning Approach

arXiv: https://arxiv.org/abs/2203.00135 · HTML: https://mlsanity.com/paper/2203.00135/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 28 Feb 2022.

## Builds on (most cited first, 2 of 2)

- [Resilience Analysis and Cascading FailureModeling of Power Systems under Extreme Temperatures](https://mlsanity.com/paper/2009.14155/) (2020)
- [Analyzing the Travel and Charging Behavior of Electric Vehicles -- A Data-driven Approach](https://mlsanity.com/paper/2106.06475/) (2021)

## Built on by (most cited first, 1 of 1)

- [Modeling of New Energy Vehicles' Impact on Urban Ecology Focusing on Behavior](https://mlsanity.com/paper/2406.06602/) (2024)

## Abstract (arXiv)

The increasing market penetration of electric vehicles (EVs) may change the travel behavior of drivers and pose a significant electricity demand on the power system. Since the electricity demand depends on the travel behavior of EVs, which are inherently uncertain, the forecasting of daily charging demand (CD) will be a challenging task. In this paper, we use the recorded GPS data of EVs and conventional gasoline-powered vehicles from the same city to investigate the potential shift in the travel behavior of drivers from conventional vehicles to EVs and forecast the spatiotemporal patterns of daily CD. Our analysis reveals that the travel behavior of EVs and conventional vehicles are similar. Also, the forecasting results indicate that the developed models can generate accurate spatiotemporal patterns of the daily CD.