Mandatory speed limits and crash frequency on motorways — A causal machine learning approach

Maike Metz-Peeters

Transportation Research Part A: Policy and Practice, 2025, vol. 200, issue C

Abstract: This study analyzes the effects of binding segment-level speed limits on injury crash frequencies on German motorways. Various geo-spatial data sources are merged to a rich novel data set, providing detailed information on 500-meter segments of large parts of the network. A causal forest is applied to estimate effects under fairly weak assumptions about the underlying data generating process and to offer insights into effect heterogeneity. Furthermore, the study explores potential biases through a phenomenon called spatial overfitting and examines potential solutions. Substantial negative effects of three levels of speed limits on crash frequencies are found, particularly for crashes involving severe or fatal injuries, while effects on crashes involving light injuries are comparably small. The heterogeneity analysis suggests larger crash rate reductions on roads with less traffic, as well as on roads with entrance and exit ramps, while heterogeneity regarding shares of heavy vehicle traffic is inconclusive.

Keywords: Crash frequency; Speed limits; Causal machine learning; Spatial machine learning (search for similar items in EconPapers)
JEL-codes: C21 H10 R41 R48 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.tra.2025.104616

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