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

Julius Range, Benedikt Gloria and Albert Erasmus Grafe

ERES from European Real Estate Society (ERES)

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.

Keywords: Machine Learning; satellite image analysis; specialized real estate; Transportation (search for similar items in EconPapers)
JEL-codes: R3 (search for similar items in EconPapers)
Date: 2024-01-01
New Economics Papers: this item is included in nep-big, nep-cmp, nep-tre and nep-ure
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Persistent link: https://EconPapers.repec.org/RePEc:arz:wpaper:eres2024-164

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