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Park-An: A Cloud-Based Service for Parking Pressure Analysis Based on Open and Municipal Data

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HCI in Mobility, Transport, and Automotive Systems (HCII 2025)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15817))

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Abstract

Cities and municipalities face significant challenges in implementing an efficient municipal parking management system due to a lack of human and IT infrastructural resources to conduct data evaluations and build effective systems, as well as the lack of standards and APIs for efficient data sharing. Scattered and unstructured data in various formats and qualities hinder its integration with other open data sources. To address these challenges, we developed Park-an, a cloud-based service. It enables public administrators to upload, analyze, and visualize parking data in combination with other public datasets, such as OpenStreetMap (OSM). The prototype facilitates the identification of parking pressure through indicators and offers interactive visualization tools to enable better planning and decision-making. This paper outlines the software architecture of Park-an, which is based on microservices and is cloud-native, ensuring robustness and scalability. The primary feature of Park-an is the calculation of parking pressure using various indicators, such as the ratio of building area to parking area and geometric distances of points of interest (POIs) to parking spaces. The paper presents a demonstration of Park-an’s functionalities for urban parking planning and optimization based on three German cities.

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Notes

  1. 1.

    For a detailed view of the following results, it is recommended that the reader view this document digitally and zoom in on the respective figures.

  2. 2.

    The size of parking spaces is regulated in the Empfehlungen für Anlagen des ruhenden Verkehrs (EAR) and in the Garagenverordnung (GaVO) of the federal states. For example, the GaVO of Baden-Württemberg specifies a minimum parking space size for cars of \({2.3}\,\textrm{m} \times {5}\,\textrm{m}\) [21].

  3. 3.

    For example, due to their early pre-medieval history and relatively limited land area, European cities have city walls with dense housing and winding streets in the center, as well as mostly multi-family houses outside the city center. American cities, on the other hand, often have single-family homes outside the center and usually no city walls, as well as significantly fewer monarchist and religious buildings and a greater distribution of workspaces throughout the metropolitan area [1].

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Authors and Affiliations

  1. Fraunhofer Institute for Industrial Engineering IAO, Research and Innovation Center for Cognitive Service Systems (KODIS), Bildungscampus 9, 74076, Heilbronn, Germany

    Sebastian Straßburg, Vincent Philipp Göbels, Diana Fischer-Preßler, Jens Neuhüttler & Janika Kutz

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  1. Sebastian Straßburg
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  2. Vincent Philipp Göbels
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  3. Diana Fischer-Preßler
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Correspondence to Sebastian Straßburg .

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  1. Technische Universität Ilmenau, Ilmenau, Germany

    Heidi Krömker

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Straßburg, S., Göbels, V.P., Fischer-Preßler, D., Neuhüttler, J., Kutz, J. (2025). Park-An: A Cloud-Based Service for Parking Pressure Analysis Based on Open and Municipal Data. In: Krömker, H. (eds) HCI in Mobility, Transport, and Automotive Systems. HCII 2025. Lecture Notes in Computer Science, vol 15817. Springer, Cham. https://doi.org/10.1007/978-3-031-92689-1_17

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