Asset-Lite Parking: Big Data Analytics in Development of Sustainable Smart Parking Solutions in Washington, D.C.

Abstract

Real-time parking occupancy detection is often the missing piece when municipalities migrate up the smart parking spectrum. Municipalities use occupancy detection to ( a) provide real-time information to customers about parking availability; ( b) make demand-based price adjustments for the efficient use of curb space in a congested area; ( c) establish the right meter policies, such as time limits; and ( d) inform parking enforcement. The state of the practice for occupancy detection has been the use of assets (e.g., sensors and cameras) for every parking space. However, given current pricing models and usage patterns, this approach is neither economically sustainable nor necessary. The ParkDC Penn Quarter–Chinatown pilot launched in Washington, D.C., seeks to develop reliable occupancy data on the basis of information from all parts of the parking ecosystem (e.g., networked meters, enforcement, and pay-by-cell transactions). Combined with a sampling of occupancy data collected through limited sensor deployment, mobile cameras, and fixed cameras, Washington, D.C., aims to develop a sustainable solution based on an optimal mix of assets and coverage. The cost, customer satisfaction, revenue, and operational implications of asset-lite solutions are discussed. This paper will enable jurisdictions to develop cost-efficient models for occupancy detection so that those jurisdictions can strategically position themselves to implement real-time availability information and performance-based pricing.