Automated Enforcement and Traffic Safety

• 成果类型: Article; Early Access
• 署名作者: Cheng, Zhi (Aaron); Dong, Zhanyu; Pang, Min-Seok
• 署名单位: University of London; London School Economics & Political Science; Sun Yat Sen University; Sun Yat Sen University; University of Wisconsin System; University of Wisconsin Madison
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2023.00575
• 发表日期: 2025
• 关键词: automated enforcement; traffic safety; deterrence; event study

摘要

Traffic safety poses a persistent challenge for society and public policy. Conventional law enforcement by human police is often cost-ineffective because of 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 with 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 that increases driver awareness of these cameras and encourages 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.