One of the Google Summer of Code 2015 projects offered by 52°North is “Social Driving Stats”. It is a sub project related to 52°North’s enviroCar project. I will be working on this project under the mentoring of Dr.Christoph Stasch and Matthes Rieke. enviroCar offers a citizen science platform for collecting, analyzing, sharing and discussing car sensor data. Sharing specific tracks or statistics is currently not possible with enviroCar. The Social Driving Stats project aims to enhance the enviroCar with sharing facilities and social media links by implementing a REST interface for creating and providing track visualizations and statistics. Furthermore, enviroCar should be linked to social networks, such as Google Plus, Facebook or Twitter. Therefore, this project will utilize the APIs of these platforms and integrate them within the user profile pages of enviroCar.
enviroCar Newsletter ….. Issue 1/May 6, 2015
enviroCar Analysis Service
Six students from the Bochum University of Applied Sciences worked on developing an analysis service for enviroCar last semester. As part of their studies, they developed a pilot system with which one can calculate routes that require the least amount of fuel on average. The basic idea was to create a model which estimates the power/energy consumption for electric cars along predefined routes, thus enabling drivers to determine if they could reach a certain destination with the power available. The students relied on user generated empirical values gathered via enviroCar, e.g. fuel consumption, emission and speed. They used street data from OpenStreetMap, which they edited with ArcGIS Tools.
enviroCar Processing Tools
Last semester, the Institute for Geoinformatics offered a course “Web-based Spatio-temporal Analysis of Floating Car Data”. It focused on developing tools to analyze enviroCar data. Sixteen students got together in small groups and successfully developed several analysis tools. One group created an R Shiny-based Web application for analyzing anomalies in the enviroCar data. Another group implemented an R package to assign the enviroCar data to street segments in order to calculate aggregates for the street segments, e.g. average speed. A third group evaluated an approach for estimating noise emissions using regression models and implemented a simple online tool for noise estimation. Add your own GeoJSON track!
These are only a few examples of the work done. But they nicely demonstrate further ways of analyzing enviroCar data to answer various questions from different application domains.