The Google Summer of Code 2020 is just about over. During the summer, I investigated improving different aspects of the enviroCar Android app. It was a very amazing learning experience. In this final blog post, I’d like to wrap up and summarize my achievements during the second half of the Google Summer of Code 2020.
Trajectory Analytics Toolbox: Midterm Post
In my project “Trajectory Analytics Toolbox”, I will be creating a general-purpose trajectory analytics toolbox in R. See my first blog post for more background information.
Since the beginning of Google Summer of Code 2020, I have created an R package on Github called traviz. Please see a live demo of some of these functions on a subset of enviroCar data using Shiny.
Improvement of Car Selection Process in enviroCar App – Mid term Blog Post
It’s been some fun time working on the enviroCar Android App. In this midterm blog post, I will give an overview of the achievements I’ve reached since the start of the coding phase to the work done until the start of the midterm evaluation.
Introduction
The overarching goal of the project is to improve the car selection process in the enviroCar Android app by integrating previously defined datasets of vehicles. This is app side implementation. For more information about this project, feel free to read the introductory blog post. The following blog post highlights the core tasks and achievements during the first four weeks of this project.
Fuel consumption models in enviroCar
The enviroCar platform offers a broad range of data about traffic that can be used by different stakeholder groups. Fuel consumption is of special interest, e.g. when trying to understand the environmental impact of traffic or when trying to minimize fuel consumption by individual drivers. In enviroCar, there are currently two models to calculate fuel consumption: 1) using motor data from the on-board diagnostics (OBD) and 2) using GPS data from mobile phones running the enviroCar app.
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