Traffic Prediction scenario
Traffic-aware path finding demo
- People involved:
- Florian Steinke, Ralph Grothmann,Yi Huang, Volker Tresp (Siemens)
- Daniele Dell'Aglio, Irene Celino, Emanuele Della Valle (CEFRIEL)
- Description:
- State-of-the-art statistical learning / probabilistic reasoning can be well-integrated into LarKC
- LarKC can deal with large-scale, time-varying data
- Challenges: Data size, Time-dependence, Heterogeneous information sources. Open world, Data quality / Uncertainty
- Showcase results:
- Batch-time LarKC workflow: to recursively calculate traffic predictions for the next x hours
- Run-time LarKC workflow: to calculate the quickest path for specific user’s routing requests
- Deliverable/tasks: WP3 + WP6
Video: http://larkc.cefriel.it/traffic-larkc/traffic-larkc-video.html
Poster: pdf version
Presentation: ppt slideset