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RWTH Aachen
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Data Analysis of Micromobility in the City of Aachen

Thesis type
  • Bachelor
Status Open
Supervisor(s)
Advisor(s)

This thesis focuses on the data analysis of micromobility data in Aachen. The goal is to use data science methods to gain insights from the already gathered data. To this end, a suitable data model should be defined, which then enables the analysis of both station-based, free-floating and hybrid shared mobility modes. On this defined data basis, various analyses should be performed.

One of the main trends in mobility in Germany at the moment is the "Verkehrswende", which emphasizes a shift from traditional modes of transportation to a more sustainable mode of transportation. In the EU 27% of total CO2 emissions are related to the traffic sector.

The digitization in the recent years made new mobility modes available. One of these modes is shared-mobility, such as car-, bike-, scooter-, or ride-sharing. These mobility modes are very flexible as they are widely available in urban areas and can be used on short notice. Shared mobility attempts to solve the so called first-and-last mile problem for public transportation. The first-last mile problem captures the fact that it is difficult for most people to access the public transportation network from their origin and hard to reach their destination from their last stop. Shared mobility modes, which are available for the first and last mile attempt to alleviate this problem and thus allowing more people to access the public transport network in an efficient way and may help the shift towards more sustainable mobility.

This thesis focuses on the data analysis of micromobility data in Aachen. The goal is to use data science methods to gain insights from the already gathered data. To this end, a suitable data model should be defined, which then enables the analysis of both station-based, free-floating and hybrid shared mobility modes. On this defined data basis, various analyses should be performed. Among others, it should be researched for which routes micromobility is most often used, which factors may influence usage, and give estimations of the sustainability of micromobility. Analysis of this data is important in order to gain insights of how micromobility is used to integrate it reasonably into multimodal traffic simulations to further evaluate traffic concepts and policies.

Prerequisites

Knowledge of at least one programming language such as C++, Java, or Python will be fundamental.
Experience with databases and database modeling will be helpful, but not essential.
Experience with data analytics will be helpful, but not essential.
Experience with web APIs will be helpful, but not essential.
Good communication Skills.

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