Concept of a Data Thread Based Parking Space Occupancy Prediction in a Berlin Pilot Region
Tim Tiedemann, Thomas Vögele, Mario Michael Krell, Jan Hendrik Metzen, Frank Kirchner
In Papers from the 2015 AAAI Workshop, (WAIT-2015), 25.1.-26.1.2015, Austin, AAAI Press, Jan/2015.
In the presented research project, a software and Hardware infrastructure for parking space focussed inter-modal route planning in a public pilot region in Berlin is developed. One central topic is the development of a prediction system which gives an estimated occupancy for the parking spaces in the pilot region for a given date and time in the future. Occupancy data will be collected online by roadside parking sensors developed within the project. The occupancy prediction will be implemented using “Neural Gas” machine learning in combination with a proposed method which uses data threads to improve the prediction quality. In this paper, a short overview of the whole research Project is given. Furthermore, the concept of the software Framework and the learning methods are presented and first collected data
is shown. The prediction method using data threads is explained in more detail.