Electric Vehicle Infrastructure: Week 6

This week was incredibly productive for Team EV. While we wait for our IRB to be approved we are happy to put survey design aside for a few days while we are hard at work wrapping up our sentiment analysis.

The final model, a convolutional neural network, is complete, thanks to Kevin’s hard work. We are now just adding new features to see if it further increases the model’s accuracy. Once our best model is complete, we will use it to get the most accurate results possible.

Meanwhile, Arielle and Emerson have worked on analyzing the sentiment results using the classifications from the (slightly less accurate) support vector machine. Once the CNN is completely done, their code will be rerun with the more accurately classified data. Emerson created an interactive map in Leaflet and D3 that allows users to visualize and inspect any charger location in North America. The map will help identify possible trends in the data that can later be investigated for statistical significance. Arielle has been working on creating models in R to figure out what factors are associated with a location having more positive or negative sentiment. While results are still preliminary, it seems as though the day of the week is a predictor for whether or not a location will have more negative reviews!

Lastly, we are preparing a working paper for the Bloomberg Data for Good Exchange! Our abstract is due this Sunday and the paper next week so we are writing away to submit our best work.

By next week, we hope to have our IRB approved so that we can put up our surveys as soon as possible!

A mapping of EV station reviews in the LA metro. Larger circles signify more reviews and greener reviews signify a more positive sentiment in reviews for the station