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Using Machine Learning to Predict Parking Difficulty

viernes, 3 de febrero de 2017

Posted by James Cook, Yechen Li, Software Engineers and Ravi Kumar, Research ScientistWhen Solomon said there was a time and a place for everything he had not encountered the problem of parking his automobile.-Bob Edwards, Broadcast Journaliststuck in trafficlooking for parkingGoogle MapsWazelaunched a new feature for Google Maps for Android

Parking availability is highly variable, based on factors like the time, day of week, weather, special events, holidays, and so on. Compounding the problem, there is almost no real time information about free parking spots.

Even in areas with internet-connected parking meters providing information on availability, this data doesn’t account for those who park illegally, park with a permit, or depart early from still-paid meters.

Roads form a mostly-planar graph, but parking structures may be more complex, with traffic flows across many levels, possibly with different layouts.

Both the supply and the demand for parking are in constant flux, so even the best system is at risk of being outdated as soon as it’s built.

Ground Truth Dataeasydifficult.How long did it it take to find parking?Model Featureswisdom of the crowdlive trafficpopular times and visit durations

Model Selection & Traininglogistic regressionLimited parkingEasyResults

Output of our parking difficulty model in the Financial District and Union Square areas of San Francisco. Red denotes a higher confidence that parking is difficult. Top row: a typical Monday at ~8am (left) and ~9pm (right). Bottom row: the same times but on a typical Saturday.