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    29. October 2019

    Offer for various projects and bachelor and master theses

    DEMAND ANALYSIS AND PREDICTION IN THE CONTEXT OF SHARED MOBILITY SERVICES USING DATA SCIENCE METHODS AND MACHINE LEARNING

    Various projects and bachelor and master theses

    Shared mobility services such as nextbike, DriveNow, TIER or Uber are experiencing a rapid growth worldwide. In many cities, shared mobility services with bicycles, cars and e-scooters are central components of alternative mobility strategies to tackle traffic-related problems.

    However, as a result of the rapidly growing fleets, operators of modern services are facing existential challenges. At the same time, the services must be designed to be economical, customer-friendly, environmentally friendly and in cooperation with public authorities. For planning and efficient operation, precise estimations of future mobility demand are required.

    Existing approaches to forecasting demand in the context of shared mobility services, however, mostly focus on station-based systems and cannot address the complex spatial and temporal dependencies resulting from the flexible provision of vehicles without fixed stations.

    Methods from the fields of data science and machine learning offer the opportunity to leverage the large amounts of data generated by the ever faster growing fleets and provide a basis for efficient operation and successful optimization of the offerings.

    Main topics:

    • Development of web crawlers for data retrieval
    • Data cleansing and data analysis
    • Machine learning and other data science methods

    Contactperson:
    Lukas Böhm (lukas.boehm(at)uni-siegen.de)

    The full offer is also available here.