Targeting Fatal Traffic Collision Risk from Prior Non-Fatal Collisions in Toronto
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Targeting Fatal Traffic Collision Risk from Prior Non-Fatal Collisions in Toronto Zeljko Bavcevic 1 & Vincent Harinam 2 Accepted: 13 October 2020/ # The Author(s) 2020
Abstract Research question How accurately can all locations of 44 fatal collisions in 1 year be forecasted across 1403 micro-areas in Toronto, based upon locations of all 1482 nonfatal collisions in the preceding 4 years? Data All 1482 non-fatal traffic collisions from 2008 through 2011 and all 44 fatal traffic collisions in 2012 in the City of Toronto, Ontario, were geocoded from public records to 1403 micro-areas called ‘hexagonal tessellations’. Methods The total number of non-fatal traffic collisions in Period 1 (2008 through 2011) was summed within each micro-area. The areas were then classified into seven categories of frequency of non-fatal collisions: 0, 1, 2, 3, 4, 5, and 6 or more. We then divided the number of micro-areas in each category in Period 1 into the total number of fatal traffic collisions in each category in Period 2 (2012). The sensitivity and specificity of forecasting fatal collision risk based on prior non-fatal collisions were then calculated for five different targeting strategies. Findings The micro-locations of 70.5% of fatal collisions in Period 2 had experienced at least 1 non-fatal collision in Period 1. In micro-areas that had zero non-fatal collisions during Period 1, only 1.7% had a fatal collision in Period 2. Across all areas, the probability of a fatal collision in the area during Period 2 increased with the number of non-fatal collisions in Period 1, with 6 or more non-fatal collisions in Period 1 yielding a risk of fatal collision in Period 2 that was 8.7 times higher than in areas with no non-fatal collisions. This pattern is evidence that targeting 25% of micro-areas effectively could cut total traffic fatalities in a given year by up to 50%. Conclusion Highly elevated risks of traffic fatalities can be forecasted based on prior non-fatal collisions, targeting a smaller portion of the city for more concentrated investment in saving lives. Keywords Fatal traffic collisions . Policing . Targeting . Non-fatal collisions . False positives .
False negatives . True negatives . True positives . Specificity . Sensitivity . Traffic safety
* Vincent Harinam [email protected] Extended author information available on the last page of the article
Cambridge Journal of Evidence-Based Policing
Introduction In 2016, the City of Toronto reported 76 fatal traffic collisions that resulted in 78 individual fatalities. This peak year prompted increased efforts by law enforcement to target and reduce the number of fatalities resulting from traffic collisions, with special attention being paid to the numbers of pedestrian and cyclist deaths from these incidents. In the City of Toronto, public concern over traffic collisions is evidenced by initiatives such as the Vision Zero Plan (City of Toronto 2017), a municipal datadriven strategy for reducing traffic-related fatalities. At the core of such initiatives is the all-important
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