Do you want to contribute to sustainable transportation and be part of a consistent, long-term research program? Gain experience in practical research and development in the field of IoT, Machine Learning and Computer Science. We will hook you up with experienced analysts, access to 3+ years of detailed bus and truck data, state of the art hardware and expert coaching.
ViriCiti Labs for you?
Our application process is simple, but we are picky who we work with. We expect you to be as enthusiastic about applied science and data as we are. We want to invest in you; let us know why we should! Go to the short application form by clicking the button below. We ask you to introduce yourself, what study you are following and what you are interested in.
ViriCiti Labs is the academy for zero-emission future of public transit. Our projects are flexible in design so they can be worked on individually, in teams, at the office or from home. We have limited spaces available in our program for motivated BSc and MSc students with a background in Mathematics/Statistics, Data Science, Computer Science or Engineering. Check out our projects below:
Energy consumption estimation using historical data
To estimate a vehicle’s energy consumption one can use the average consumption of the last 10 days. How well does this method work? Are the results more accurate if the vehicle drives the same route each day? Can you find features that improve the estimation?
Visualization of altitude data
We augment the road information from OpenStreetMap with altitude data from satellite imaging. The altitude data is gained from a 90m x 90m grid. Due to its low resolution, errors can occur when retrieving data from locations where measurements intersect with e.g. buildings. Visualizing the result of altitude augmentation is, therefore, a necessary step to ensure accurate results. Can you examine the altitude data and improve the process?
Mapping road altitudes using gyroscope data
OpenStreetMap does not contain altitude information, but for energy consumption calculations this data is necessary. Satellite images are used to add altitudes to the map. As a complementary source, the gyroscope readings from the vehicle itself is used. Can you develop a method that combines altitudes from the satellite imagery with the sensor data and improve the accuracy?
Urban traffic prediction
City buses drive the same route multiple times a day; the whole year through. Traffic conditions may change during the day, as well as with each season. The bus consumes more energy in conditions with high traffic due to frequent braking (deceleration) and acceleration. Currently, we use the factor ‘time’ in our energy consumption models to predict peaks in traffic. Can you make a better prediction of traffic peaks and improve the accuracy of the prediction model?
Effective wind force in urban areas
Part of the energy required to propel the vehicle is lost in overcoming air resistance. The resistance depends on the vehicle velocity in relation to the wind. Wind direction and force are available through weather services but do not take into account the varying wind conditions in urban areas due to buildings. Can you transform the global wind data to local wind direction and wind force that can be used in the energy consumption predictions?
Mapping missing roads
We use OpenStreetMap to map vehicles’ GPS positions to nearby roads. However, the road infrastructure isn’t static and roads are added, removed, and diverted rendering the map out of date. Can you use the GPS data from the vehicles to determine new roads, and perhaps even the type of road?
How to apply
It’s very simple. Fill in our short questionnaire by clicking on the button below. We are interested in you and what you’re doing at the moment. Are you studying? Taking a break? Fulltime available or are you interested in a side project? Tell us what project topics you are most interested in. We’ll take it from there!