Parking Occupancy Prediction and Pattern Analysis

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Parking Occupancy Prediction and Pattern Analysis

According to the Department of Parking and Traffic, San Francisco has more cars per square mile than any other city in the US [1]. The search for an empty parking spot can become an agonizing experience for the city’s urban drivers. A recent article claims that drivers cruising for a parking spot in SF generate 30% of all downtown congestion [2]. These wasted miles not only increase traffic congestion, but also lead to more pollution and driver anxiety. In order to alleviate this problem, the city armed 7000 metered parking spaces and 12,250 garages spots (total of 593 parking lots) with sensors and introduced a mobile application called SFpark [3], which provides real time information about availability of a parking lot to drivers. However, safety experts worry that drivers looking for parking may focus too much on their phone and not enough on the road. Furthermore, the current solution does not allow drivers to plan ahead of a trip. We wish to tackle the parking problem by (i) predicting the occupancy rate, defined as number of occupied parking spots over total number of spots, of parking lots in a zone given a future time and geolocation, (ii) working on aggregated parking lots to explore if there is estimation error reduction pattern in occupancy prediction, (iii) classifying daily parking occupancy patterns to investigate different travel behavior at different region.

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