Traffic research | Working at TML

At present, I am working for a research and consultancy company, called Transport & Mobility Leuven (TML), where I am employed as a researcher with expertise in transportation planning models, traffic flow theory, and the analysis of traffic flow data.

Sven Maerivoet holds a PhD degree in traffic engineering. Within TML he is a member of the Data Enrichment Group. His expertise mainly lies in transportation planning models, traffic flow theory, giving courses and lectures, numerical and statistical analysis of various big data, travel time loss functions, multi-agent systems, sustainability effects in cities, external costs of road congestion, the related parking problems and innovative technological solutions, intelligent transportation systems (ITS), and road user charging. He is also an active member of several working groups in the ITS Belgium organisation, member of the advisory council of the Flemish Institute for Mobility (VIM), and reviewer for various scientific journals.

Transport & Mobility Leuven

TML is a private research company founded in 2002 by the KU Leuven and the Dutch research institute TNO. TML is specialised in quantitative transport research and transport modelling. It is a multi-disciplinary team of high- level specialists in traffic engineering, transport modelling, transport economics, spatial economics, and environmental science. TML carries out research for governments and businesses to support their policy making. This research covers not only transport itself, but also a broad range of related issues: energy, environment, safety, spatial planning, and effects on society in general. On top, TML stands for a multidisciplinary team of researchers of the highest level.

Data Enrichment Group at Transport & Mobility Leuven

Data Enrichment Group at TML: discovering patterns for business opportunities!

Data enrichment is one of the important steps in the research activities for several projects performed by Transport & Mobility Leuven. As we are often confronted with administrations and private companies asking us to identify future issues and opportunities, the need for dedicated data analyses becomes more prominent. This is the case when looking at the micro level of mobility (dealing with driver behaviour), as well as the meso and macro levels (trip planning and long-term mobility planning, respectively). The objectives of these types of projects make it necessary to manipulate data in such a way that an understanding of the underlying structures within the data emerges.

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