Brussels MOBI-AID : Brussels MOBIlity Advanced Indicators Dashboard



Project Overview


Brussels MOBI-AID (Brussels MOBIlity-Advanced Indicators Dashboard) aims at designing and building this performance monitoring system, by means of a dashboard of advanced mobility indicators that will allow


  • to better understand mobility dynamics in Brussels Region,
  • to support local authorities in designing suitable and sustainable policies,
  • to assist Brussels, capital of Europe, to be recognized as a model of a Smart Region.

The transition from the current model to a Smart Region model raises a number of non-trivial challenges, which range from reliable storage of, and easy access to, massive amounts of mobility data, to extracting sustainable mobility indicators in an automated and validated manner and to actioning the extracted indicators in a dashboard. We expect that Brussels MOBI-AID by making mobility smarter and more sustainable in Brussels Region will provide many valorisation pathways as it will provide an open data platform with smart data on mobility.
This project aims to provide a smart knowledge base for Brussels Region's mobility sector. A cloud of structured and interlinked information elements produced by identifying relevant databases and other resources will be established. Besides, the design of an advanced Data Analytics layer and the development of an indicator framework to raise awareness will be investigated, which is necessary to get an idea of the effect of mobility and logistics operations. The private and public sectors, citizens and by extension, policymakers are the primary stakeholders who should be made aware. In order to enable the state-of-the-art technologies in Brussels city, a comparative smart cities study will be performed. This will enable to detect the knowledge gaps, and hence recommendation and field tests will be implemented to make Brussels first in row.

Funding

FEDER, European Union

Partners

Gianluca Bontempi

Machine Learning Group, Computer Science Department, ULB

Cathy Macharis

Faculty of Economic, Social and Political Sciences and Solvay Business School, MOBI Research Group, VUB

Wouter Verbeke

Faculty of Economic, Social and Political Sciences and Solvay Business School, MOBI Research Group, VUB


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Legend

Different classes of Heavy Good Vehicles

  • 1 class: weight > 32 t,
  • 2 class: 12 t < weight < 32 t,
  • 3 class: 3.5 t < weight < 12 t,

Brussels MOBI-AID

The presented study makes use of data collected by on board unit (OBU) device set up in trucks driving within and around Brussels Metropolitan Area. The OBU are required in Belgium for heavy goods vehicles of over 3.5 tons . The OBUs need to be set up in all the aforementioned cathegory of trucks and constantly switched on when they drive on public streets. The OBU data coming from different trucking companies are collected and stored by the public service Bruxelles Mobilite'.
The company was willing to share the trucks data with the Machine Learning Research Group of Universite' Libre De Bruxelles, since the resulting freight performance measures and potential big data infrastructure improvements could be appealig for their public service. Each truck device sends a message approximately every 30 seconds starting from 2 a.m. to 1.59 of the following day.
In the platform the OBU datahave been aggregated every hour from 13/10/2017 to 19/10/2017.



The OBU informations consist of

  • an anonymous identifier (ID) resetting every day at 2 a.m.,
  • timestamp, GPS position (latitude, longitude),
  • speed (engine),
  • direction (compass) and vehicole data (EUROvalue, MAM and CountryCode).

Copyright © 2017 GiovanniBuroni
Copyright © 2017 GiovanniBuroni