New York's cyclists and bus-riders are certain they're being slowed and endangered by an epidemic of illegal lane-obstructions from delivery vehicles, taxis and Ubers, but policymakers have refused to do anything about it, saying that the evidence is all anaecdotal.
So a machine learning researcher/cycling advocate named Alex Bell created an image classifier that is trained to recognize when a bike- and/or bus-lane is illegally obstructed (sourcecode on Github), producing the first hard data on the phenomenon. He's analyzed the footage from a single traffic camera in his Harlem neighborhood, one St. Nicholas Avenue between 145th and 146th Streets, and found that the bus-stop there is blocked 57% of the time, while the bike-lanes are obstructed 40% of the time.
“Everyone keeps talking about the bus lanes being blocked, and buses being so slow I could just say, ‘Oh they are so blocked, it’s so bad,’ — but that’s not very helpful,” Mr. Bell said in an interview. “I thought to myself, ‘How could you show to everyone that bus stops and bike lanes are routinely blocked?’ ”
...The enforcement that does exist through the use of cameras is slim: Just 12 of the city’s 317 bus routes have cameras mounted on objects like streetlights that are similar to red-light cameras. Any plan to add more such cameras must be approved by the State Legislature.
Our Camera [Alex Bell/Github]
Bus Lane Blocked, He Trained His Computer to Catch Scofflaws [Sarah Maslin Nir/New York Times]
Johns Hopkins Computer Science prof Professor Peter Fröhlich grades his students on a curve: the highest score on the final gets an A and everyone else is graded accordingly.
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IoT Inspector is a new tool from Princeton's computer science department; it snoops on the traffic from home IoT devices and performs analysis to determine who they phone home to, whether they use encryption, and what kinds of data they may be leaking.
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