This project aims to develop an artificial intelligence (AI) mechanism, able to predict urban mobility and the origin-destination type of the trip, as a result of an urban zone segmentation. Though, so far there is no methodology existing on how to preprocess urban mobility data, to retrieve useful information from this data and to make predictions. The current problems in this field are the following: noise in the data, erroneous values and misleading location information, the lack of standardized data models, the lack of program software and applications for utilizing mobility big data, the lack of labeling, data privacy etc. In the frames of our project the methodology for urban data preprocessing and predictions of mobility has been developed by using the artificial intelligence and machine learning methods and real-world data. As a result, we will develop an application software prototype (based on AI), that is able to predict mobility and the type of the trip’s origin-destination in a real time.
Project funding:
KTU R&D&I Fund
Period of project implementation: 2019-04-01 - 2019-12-31
