Automated enforcement and traffic safety

Cheng, Aaron ORCID: 0000-0002-2070-3761, Dong, Zhanyu and Pang, Min-Seok (2025) Automated enforcement and traffic safety. Management Science. ISSN 0025-1909 (In Press)

Text (Cheng Dong Pang 2025_Automated Enforcement and Traffic Safety) - Accepted Version Pending embargo until 1 January 2100. Available under License Creative Commons Attribution. Download (5MB)

Abstract

Traffic safety poses a persistent challenge for society and public policy. Conventional law enforcement by human police is often cost-ineffective due to information asymmetry and negative externalities of unsafe driving behaviors. Automated enforcement, in the form of traffic cameras on the road, has gained prominence in recent decades, yet its effectiveness and underlying mechanisms remain debated. This study examines the impact of traffic cameras on road safety using longitudinal data from a metropolitan city in China. We distinguish between advanced cameras, which use machine learning to detect various traffic violations and constantly record video, and conventional cameras, which rely on triggered image capture for a limited number of violations. Using an event study design with staggered camera installations at road intersections, we observe a significant and sustained reduction in accidents near advanced cameras, compared to locations with no cameras or only conventional cameras. Further analysis identifies three key mechanisms driving the effects of advanced cameras: (i) automated detection effect—superior technical capabilities to automate violation detection; (ii) real-time recording effect— continuous monitoring and recording capability to augment accident cause identification; and (iii) driver learning effect—technology-enabled deterrence to increase driver awareness of these cameras and encourage behavioral adjustments to mitigate accident risks. This study contributes to information systems, transportation economics, and criminology, offering policy insights into the effective design and deployment of automated enforcement to improve traffic safety.

Item Type:	Article
Additional Information:	© 2025 The Author(s)
Divisions:	Management
Subjects:	H Social Sciences > HE Transportation and Communications T Technology K Law Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date Deposited:	17 Feb 2025 11:03
Last Modified:	17 Feb 2025 12:36
URI:	http://eprints.lse.ac.uk/id/eprint/127319

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