On suspicious tracks: Machine-learning based approaches to detect cartels in railway-infrastructure procurement

Hannes Wallimann and Silvio Sticher

Transport Policy, 2023, vol. 143, issue C, 121-131

Abstract: In railway infrastructure, construction and maintenance is typically procured using competitive procedures such as auctions. However, these procedures only fulfill their purpose – using (taxpayers’) money efficiently – if bidders do not collude. Employing a unique dataset of the Swiss Federal Railways, we present two methods in order to detect potential collusion: First, we apply machine learning to screen tender databases for suspicious patterns. Second, we establish a novel category-managers’ tool, which allows for sequential and decentralized screening. To the best of our knowledge, our study represents the first attempt to adapt and implement machine-learning-based price screens within the context of a railway-infrastructure market.

Keywords: Railway infrastructure; Cartel detection; Machine learning; Procurement auctions (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:trapol:v:143:y:2023:i:c:p:121-131

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DOI: 10.1016/j.tranpol.2023.09.010

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