Cost–benefit analysis of a distracted pedestrian intervention

Abstract

ObjectiveCellphone ubiquity has increased distracted pedestrian behaviour and contributed to growing pedestrian injury rates. A major barrier to large-scale implementation of prevention programmes is unavailable information on potential monetary benefits. We evaluated net economic societal benefits of StreetBit, a programme that reduces distracted pedestrian behaviour by sending warnings from intersection-installed Bluetooth beacons to distracted pedestrians’ smartphones.MethodsThree data sources were used as follows: (1) fatal, severe, non-severe pedestrian injury rates from Alabama’s electronic crash reporting system; (2) expected costs per fatal, severe, non-severe pedestrian injury—including medical cost, value of statistical life, work-loss cost, quality-of-life cost—from CDC and (3) prevalence of distracted walking from extant literature. We computed and compared estimated monetary costs of distracted walking in Alabama and monetary benefits from implementing StreetBit to reduce pedestrian injuries at intersections.ResultsOver 2019–2021, Alabama recorded an annual average of 31 fatal, 83 severe and 115 non-severe pedestrian injuries in intersections. Expected costs/injury were US$11 million, US$339 535 and US$93 877, respectively. The estimated distracted walking prevalence is 25%–40%, and StreetBit demonstrates 19.1% (95% CI 1.6% to 36.0%) reduction. These figures demonstrate potential annual cost savings from using interventions like StreetBit statewide ranging from US$18.1 to US$29 million. Potential costs range from US$3 208 600 (beacons at every-fourth urban intersection) to US$6 359 200 (every other intersection).ConclusionsEven under the most parsimonious scenario (25% distracted pedestrians; densest beacon placement), StreetBit yields US$11.8 million estimated net annual benefit to society. Existing data sources can be leveraged to predict net monetary benefits of distracted pedestrian interventions like StreetBit and facilitate large-scale intervention adoption.