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Automated speed enforcement systems to reduce traffic-related injuries: closing the policy implementation gap
  1. Stephanie R Morain1,
  2. Andrea C Gielen2,
  3. Kavi Bhalla3
  1. 1Berman Institute of Bioethics, Johns Hopkins University, Baltimore, Maryland, USA
  2. 2Johns Hopkins Center for Injury Research and Policy, Johns Hopkins School of Public Health, Baltimore, Maryland, USA
  3. 3Johns Hopkins International Injury Research Unit, Johns Hopkins School of Public Health, Baltimore, Baltimore, USA
  1. Correspondence to Dr Stephanie R Morain, Berman Institute of Bioethics, Johns Hopkins University, 1809 Ashland Ave, Baltimore, MD 21205, USA; smorain1{at}

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In the USA, traffic-related injuries are responsible for over 33 000 deaths each year1 and are the leading cause of death for individuals aged 1–24 years.2 Traffic-related injuries also take a substantial economic toll, accounting for over $99 billion annually in medical costs and associated productivity losses.3 Globally, >1.3 million people are killed each year by traffic-related injuries, and an additional 78.2 million sustain nonfatal injuries warranting medical care.4

Controlling speed is an important means for preventing traffic-related injuries. Speed is a ‘two-pronged aggravator’ of injuries, correlated both to crash likelihood and to severity.5 Speed is estimated to be a factor in over 30% of fatal crashes in the USA, resulting in over 10 000 annual fatalities.1 Enforcement of speed limits has been repeatedly demonstrated to reduce the frequency and severity of motor vehicle crashes (MVCs).6 ,7 Speed enforcement can also have positive environmental impacts, reducing fuel consumption and resultant emissions.7

How best to enforce speed limits remains an open question. In the USA, the most common method of speed enforcement relies upon police officers using radar technologies installed in vehicles to detect speeding drivers and issue citations at the time of the offense. However, several features of traditional enforcement limit its effectiveness. As Delaney et al8 note, traditional enforcement is ‘resource intensive and inconsistent in its application.’ This inconsistency reduces effectiveness, while introducing concerns for biased enforcement, including the potential for racial profiling in traffic stops.9 Drivers may also evade traditional enforcement by reducing speed only in areas known for high enforcement or using in-car radar detection systems.7

Automated speed enforcement (ASE) is a promising strategy to address many of the limitations of current approaches to speed enforcement. Evidence from several countries suggests that ASE is an effective and cost-effective …

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