Artificial Intelligence and Machine Learning for Predicting Urban Mobility and Territory Segmentation (UrbMob)

Project no.: PP-91P/19

Project description:

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.

Premium WordPress Themes Download
Download Premium WordPress Themes Free
Download WordPress Themes Free
Free Download WordPress Themes
free download udemy paid course

Project funding:

KTU R&D&I Fund

Period of project implementation: 2019-04-01 - 2019-12-31

Project coordinator: Kaunas University of Technology

Irina Matijošaitienė

2019 - 2019

Civil Engineering and Architecture Competence Centre, Faculty of Civil Engineering and Architecture

We are using cookies to provide statistics that help us give you the best experience of our site. You can find out more or switch them off if you prefer. However, by continuing to use the site without changing settings, you are agreeing to our use of cookies.
I agree