AI predicts parking availability by using weather, traffic speed, and meter data

We’ve all been there: You drive miles to a venue only to discover that, to your dismay, every parking space is fully occupied. Apps like Google Maps, which can predict busyness based on historical data, can help to a degree, but what if you’re in need of a more adaptable solution? Enter research by scientists at Carnegie Mellon University, who describe in a newly published paper on the preprint server an AI system for predicting parking occupancy in real time.

Rather than collect data from parking sensors, which the study’s coauthors contend are susceptible to failure and error, they draw on parking meter transactions to first estimate parking availability before using additional data for prediction. An estimated 95 percent of on-street paid parking is managed by meters, making their model more generalizable than sensor-dependent systems…

In tests, the model outperformed others’ baseline methods when predicting parking occupancies 30 minutes in advance, the researchers say. They credit the weather and traffic speed data for the AI system’s superior performance — particularly the weather data, which boosted prediction accuracy in recreational areas.