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.
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.
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.
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.
During Google Summer of Code 2020, I will be working on an enviroCar Android app, which is one of the 52° North GSoC projects. The project is based on sustainable mobility. The main goal of the enviroCar app is to collect track data and analyze data to reduce the costs of running a car and to advance effective traffic planning.
The question is, is this data dependent on the car type? Of course yes! The data is fully dependent on the car type because of different engine displacement and power, which in term lead to different CO2 emissions. Currently, enviroCar app is flexible for creating artificial types of cars with user-defined attribute values, like adding engine displacement and power value to any undefined or nonexistent value.