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Urban Mobility Observatory (UMO)

 

Funding

Netherlands Organisation for Scientific Research (NWO)

Project

Urban Mobility Observatory (UMO)

Timeline

2018-2022

Supervisor

prof.dr.ing.M.R. van Steen

Daily supervisor

dr.ing.Konstantinos Gkiotsalitis

Computer scientist

 

Abstract

Urban transport systems are becoming increasingly complex. At the same time, transport technologies and services are developing rapidly, which changes travel behavior. The combination urgently demands new and integrated observation, analysis and modelling approaches. The Urban Mobility Observatory (UMO) will gather and store empirical multi-modal traffic, transport and mobility data, using a well-balanced set of data collection methods. UMO will make these comprehensive data available for researchers to allow better understanding, prediction and facilitation of multi-modal mobility in large urbanized regions. The partners of the project are: UT, TU Delft, Vrije Universiteit Amsterdam, Eindhoven University of Technology, University of Groningen, Utrecht University, Centrum voor Wiskunde & Informatica, Amsterdam Institute for Advance Metropolitan solutions.

In this project, University of Twente gives specific attention to portable sensors, namely Wi-Fi sensors. The sensor data consists of 1500 Wi-Fi access points from the university campus. The objective of this project is two-fold: (i) collect, process and store the Wi-Fi data to the Open Access requirements of UMO; (ii) integrate the Wi-Fi data with other mobility data sources, such as the time schedules of public transport lines.

Specific challenges that arise from this project are: (i) processing and analysis of Wi-Fi data; (ii) anonymization of personal data (following GDPR standards); (iii) fusion of data from multiple sensors to enable its use to mobility applications. The expected outcome of this project is:

·         Store Wi-Fi data in an open access environment after ensuring its anonymization and the compliance to GDPR regulations

·         Identify the reasons behind noisy Wi-Fi data and develop software for improving the data quality

 

More information: k.gkiotsalitis@utwente.nl