Targeting Fatal Traffic Collision Risk from Prior Non-Fatal Collisions in Toronto
Authors
Bavcevic, Zeljko
Harinam, Vincent
Publication Date
2020-12Journal Title
Cambridge Journal of Evidence-Based Policing
ISSN
2520-1344
Publisher
Springer Science and Business Media LLC
Volume
4
Issue
3-4
Pages
187-201
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Bavcevic, Z., & Harinam, V. (2020). Targeting Fatal Traffic Collision Risk from Prior Non-Fatal Collisions in Toronto. Cambridge Journal of Evidence-Based Policing, 4 (3-4), 187-201. https://doi.org/10.1007/s41887-020-00054-z
Description
Funder: University of Cambridge
Abstract
<jats:title>Abstract</jats:title><jats:sec>
<jats:title>Research question</jats:title>
<jats:p>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 non-fatal collisions in the preceding 4 years?</jats:p>
</jats:sec><jats:sec>
<jats:title>Data</jats:title>
<jats:p>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’.</jats:p>
</jats:sec><jats:sec>
<jats:title>Methods</jats:title>
<jats:p>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.</jats:p>
</jats:sec><jats:sec>
<jats:title>Findings</jats:title>
<jats:p>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%.</jats:p>
</jats:sec><jats:sec>
<jats:title>Conclusion</jats:title>
<jats:p>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.</jats:p>
</jats:sec>
Keywords
Article, Fatal traffic collisions, Policing, Targeting, Non-fatal collisions, False positives, False negatives, True negatives, True positives, Specificity, Sensitivity, Traffic safety
Identifiers
s41887-020-00054-z, 54
External DOI: https://doi.org/10.1007/s41887-020-00054-z
This record's URL: https://www.repository.cam.ac.uk/handle/1810/315598
Rights
Attribution 4.0 International (CC BY 4.0)
Licence URL: https://creativecommons.org/licenses/by/4.0/
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