The agony associated with finding an on-street parking spot is familiar to most people trying to park in congested cities. To reduce this agony, cities like San Francisco have turned to Performance Parking, where the cost to park on a particular block face is tied to market demand. However, to measure this demand typically requires placing a sensor in every on-street parking space — an option that isn’t financially sustainable or practical at a large scale.
The ParkDC effort builds off projects in other cities to develop a unique “asset lite” approach to capture and predict occupancy. Instead of placing a sensor in every parking space, the occupancy is determined using spatial and temporal sampling, including partial sensor coverage, payment data, fixed and portable cameras, computer vision algorithms, and other data inputs. A “blended” data approach then combines the data to inform several real-time parking apps and to provide occupancy data for future price changes.
ParkDC Eases the Agony of On-Street Parking
Kittelson is working with the District Department of Transportation (DDOT) and Conduent to implement performance parking in the Penn Quarter and Chinatown neighborhoods of Washington, DC. The ParkDC initiative is being used by DDOT as a laboratory to test new technologies that make it easier to find a parking space, and for jurisdictions like DDOT to manage their parking supply. Kittelson is currently evaluating the effectiveness of the pilot project, and strategies that are cost-effective and enhance the user experience will be implemented in other locations in the District.