*AI can tell where you’re pretty likely to get squashed, but can’t figure out how to fix it. Probably, if AI invented a “solution,” it would be so counterintuitive that human beings wouldn’t even recognize it as an intervention.
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New York City has traffic counts for thousands of corridors reaching back to 2008, but not every street and corner is covered, and not every year. So Datakind spent nearly two years developing an “exposure model” capable of estimating car volumes, using exact traffic counts where they did exist and a machine learning model that predicts volumes where they didn’t. Essentially, artificial intelligence software (which Microsoft provided) “reads” real counts on thousands of corridors, “learns” the contours of high- and low-volume streets, and then spits back predictions for similar locations.
The data scientists then amassed dozens of datasets that reflect the shape of traffic in New York City—crash rates, street widths, locations of pedestrian plazas, signal timings, bus lanes, bike lanes, transit schedules, the volume of retailers, among many others—to tease out whether any of these particular interventions had a statistically significant effect on crash rates. This would be the underpinning of the vaunted “if, then” predictive safety model….
AI and urban pedestrian killing zones
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