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Targeting Fatal Traffic Collision Risk from Prior Non-Fatal Collisions in Toronto

Published version
Peer-reviewed

Change log

Authors

Bavcevic, Zeljko 
Harinam, Vincent 

Abstract

jats:titleAbstract</jats:title>jats:sec jats:titleResearch question</jats:title> jats:pHow 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:titleData</jats:title> jats:pAll 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:titleMethods</jats:title> jats:pThe 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:titleFindings</jats:title> jats:pThe 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:titleConclusion</jats:title> jats:pHighly 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>

Description

Funder: University of Cambridge

Keywords

3509 Transportation, Logistics and Supply Chains, 35 Commerce, Management, Tourism and Services, 3 Good Health and Well Being

Journal Title

Cambridge Journal of Evidence-Based Policing

Conference Name

Journal ISSN

2520-1344
2520-1336

Volume Title

4

Publisher

Springer Science and Business Media LLC