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Final Thesis: Requirements for an Open Mobility Data Processing Language

Abstract: Exchanging open data plays an increasingly important role in the domain of mobility. A wide range of participants provide and consume data, involving subjects such as traffic management or public transportation. To make use of the data, programming proficiency is necessary in order to realize data engineering tasks. However, a dedicated Domain-Specific Language may decrease complexity and lower the barrier for subject-matter experts to engage in the process. This design science contribution presents a process to gather and analyze metadata from National Access Points. A catalog of requirements is developed by executing this process and compiling the resulting insights for an exemplary National Access Point by German government institutions. It contains requirements for six distinct concepts relating to topics of interest in the open mobility data domain, and intends to support the development of an open mobility data processing language. According to the analysis results, CSV, ATOM, and WMS_SRVC constitute the most important media formats to support, while relational data structures were deemed significant overall. Additionally, the Well-known text format, geospatial system information, and mobility schema models were recognized as value types that necessitate support. Moreover, data sources may be accessed mostly via the HTTPS protocol and do not require authentication. However, live data appears sparse, as the majority of data is updated irregularly or not at all. The provided catalog of requirements serves as an initial point of reference for the development of Domain-Specific Languages supporting the handling of open mobility data, corresponding to the properties of real-world data offers.

Keywords: Open Data, Domain-specific language, Data engineering, Requirements

PDF: Master Thesis

Reference: Maximilian Lattka. Requirements for an Open Mobility Data Processing Language. Master Thesis. Friedrich-Alexander-Universität Erlangen-Nürnberg: 2023.