Classifying vehicle-as-a-weapon attacks: an inductive taxonomy and design implications

Vehicle-as-a-Weapon (VAAW) attacks, distinct from explosive-based vehicle attacks (VBIEDs), pose an evolving threat to public, urban spaces. Existing security guidance overlooks spatial, behavioural, and operational nuances, limiting effective design and response. This paper proposes a multidimensional taxonomy of VAAW attacks, developed using Factor Analysis for Mixed Data and k-means clustering of 135 global cases. Six distinct clusters are identified across three axes: Temporal Attack Complexity, Tactical Sophistication, and Operational Lethality. These clusters are mapped to situational crime-prevention, offering actionable insights for security planners, urban designers, and policymakers. The taxonomy reframes vehicle-based threats not as attacks to be managed through rigid perimeters of protection, but as situated, probabilistic scenarios that require differentiated mitigation; advancing the integration of protective security within broader urban resilience strategies.

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Publication Details

Journal
Crime Science
Published
2026-02-09
DOI
https://doi.org/10.1186/s40163-025-00265-7
Primary Topic
Crime Patterns and Interventions
Type
article
Field-Weighted Citation Impact
0.00
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article

Classifying vehicle-as-a-weapon attacks: an inductive taxonomy and design implications

Codee Roy Ludbey, Shreemen Prabhakaran, Samyak Bharthur, Daniel Versaci
Crime Science
Crime Patterns and Interventions
article

Classifying vehicle-as-a-weapon attacks: an inductive taxonomy and design implications

Codee Roy Ludbey, Shreemen Prabhakaran, Samyak Bharthur, Daniel Versaci
article en

Abstract

Vehicle-as-a-Weapon (VAAW) attacks, distinct from explosive-based vehicle attacks (VBIEDs), pose an evolving threat to public, urban spaces. Existing security guidance overlooks spatial, behavioural, and operational nuances, limiting effective design and response. This paper proposes a multidimensional taxonomy of VAAW attacks, developed using Factor Analysis for Mixed Data and k-means clustering of 135 global cases. Six distinct clusters are identified across three axes: Temporal Attack Complexity, Tactical Sophistication, and Operational Lethality. These clusters are mapped to situational crime-prevention, offering actionable insights for security planners, urban designers, and policymakers. The taxonomy reframes vehicle-based threats not as attacks to be managed through rigid perimeters of protection, but as situated, probabilistic scenarios that require differentiated mitigation; advancing the integration of protective security within broader urban resilience strategies.

Crime ScienceVol. 15(1)
Japan Foundation Sydney (AU)
Sustainable cities and communities
Openalex Percentile: Top 66%
Crime Patterns and Interventions
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