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Living on the Highway: Addressing Germany's HGV Parking Crisis through Machine Learning Satellite Image Analysis

Author

Listed:
  • Julius Range
  • Benedikt Gloria
  • Albert Erasmus Grafe

Abstract

The rapid increasing demand for freight transport has precipitated a critical need for expanded infrastructure, particularly in Germany, where a significant crisis in Heavy Goods Vehicle (HGV) parking facilities is emerging. Our study aims to determine the optimum supply of HGV parking lots required to mitigate this problem. Utilizing state-of-the-art object detection techniques in satellite imagery, we conduct a comprehensive analysis to assess the current availability of HGV parking spaces. Our machine learning-based approach enables an accurate and large-scale evaluation, revealing a considerable undersupply of HGV parking lots across Germany. These findings underscore the severity of the infrastructure deficit in the context of increasing freight transport demands. In a next step, we conduct a location analysis to determine regions, which are impacted acutely. Our results therefore deliver valuable insights to specialized real-estate developers seeking to cater to the demand and profit from this deficit. Based on the results, we develop industry and policy recommendations aimed at addressing this shortfall.

Suggested Citation

  • Julius Range & Benedikt Gloria & Albert Erasmus Grafe, 2024. "Living on the Highway: Addressing Germany's HGV Parking Crisis through Machine Learning Satellite Image Analysis," ERES eres2024-164, European Real Estate Society (ERES).
  • Handle: RePEc:arz:wpaper:eres2024-164
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    File URL: https://eres.architexturez.net/doc/oai-eres-id-eres2024-164
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    More about this item

    Keywords

    Machine Learning; satellite image analysis; specialized real estate; Transportation;
    All these keywords.

    JEL classification:

    • R3 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location

    NEP fields

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