Q. Toronto has had automated speed enforcement for a few years now – what does the Toronto data tell us about its impact on driver speed? 

A. The data from first-of-its-kind research released this summer by Toronto Metropolitan University and Sick Kids Hospital show that automated speed enforcement has had a meaningful impact on reducing speeding in Toronto school zones. Overall, there was a 45-per-cent reduction in the proportion of drivers exceeding the speed limit during enforcement periods. For those driving well over the limit, the impact was even stronger, with an 83- to-87-per-cent reduction in extreme speeding – drivers going more than 15 or 20 km/h over the posted limit. We also saw the 85th-percentile speed, the speed at which at or below 85-per-cent of drivers travel, drop by just over 10 km/h. That said, when cameras were removed from locations, speeds tended to return to previous levels, which tells us that sustained or rotating deployment along with more permanent road design changes are important. While these results are encouraging, our next step is to research how these speed reductions are related to actual collision and injury outcomes.

Q.  How can municipalities ensure ASE programs are implemented equitably and transparently, especially in diverse urban cities? 

A. For ASE programs to be successful in diverse urban areas such as Toronto, equity and transparency are critical. Municipalities should be clear about how the program works, including reporting how revenue from fines is used. In the City of Guelph and in Niagara Region, for example, they publicly have stated on their websites that revenue is directed back into road safety initiatives, which helps counter the perception that this is a cash grab. Open data is another important piece – regularly updated dashboards that share information on speeds, volumes, and enforcement results build trust and allow for independent review. Equity can also be advanced through meaningful community engagement before cameras are installed. Consulting with residents, schools and local workers ensures that site selection is fair and reflects local needs. In addition, it is important to analyze results by neighbourhood to confirm that the automated speed enforcement is distributed equitably across the city. Finally, ASE should always be framed as one part of a larger strategy that includes redesigning roads, implementing traffic calming and educating drivers.

Q. What are some common misconceptions about ASE and how can public health data help shift the conversation from punishment to prevention? 

A. There are common misconceptions about ASE that can undermine public support. One is that it is simply a cash grab. The way to address this is by clearly showing how funds are reinvested in safety projects and by framing costs and benefits in terms of injury prevention. Another misconception is that ASE does not improve safety. Here, the evidence from Toronto is compelling: we have already seen significant reductions in speeds and research consistently shows that slower speeds reduce both the number and severity of crashes. There is also skepticism about the accuracy or fairness of cameras. Municipalities can counter this by publishing quality assurance processes, technical evaluations, and the criteria for site selection. Most importantly, ASE needs to be framed not as punishment but as prevention. It is a public health tool designed to reduce the risk of collisions and injuries and it works best when combined with infrastructure improvements and education efforts.

In summary, Toronto’s experience shows that automated speed enforcement can significantly reduce speeding. When implemented transparently and equitably, and when framed as part of a broader public health approach to road safety, ASE has the great potential to save lives. 

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