A collaboration between Pacific Northwest National Laboratory (PNNL) and the University of Washington’s Urban Freight Lab has developed a prototype webapp that combines smart sensors and machine learning to predict parking space availability. The prototype is ready for initial testing to help commercial delivery drivers find open spaces without expending fuel and losing time.

The webapp combines curbside maps with data from nearly 300 sensors placed within 74 parking spaces in commercial and passenger loading zones from 1^st to 3^rd avenues and between Battery and Stewart streets in the Belltown neighborhood in downtown Seattle.

Since late December, PNNL researchers gathered the sensors’ real-time data about parking behaviors and combined it with historical data to train the model. They analyzed the combined data and fed it to a traffic model to provide real-time visibility of parking spaces occupancy and a glance into future occupancy.

Delivery drivers can access the webapp through web browsers on their phones and tablets. They can see available and unavailable parking spots depicted in green and red, respectively.

—Amelia Bleeker, PNNL software engineer who developed the webapp

The data update every 10 to 15 seconds and provide a range of vehicle sizes that a space can accommodate. Spaces that are open but may not accommodate a particular vehicle size are highlighted as yellow.

Vinay Amatya, a PNNL computer scientist who, along with data scientist Milan Jain, performed predictive modeling for the webapp, added that information about available parking in commercial and passenger loading zones can be obtained in both real time and prediction mode, allowing the webapp to predict which spaces will be available up to 30 minutes in the future.

The team is testing the webapp to determine its accuracy and, when fully rolled out, will provide metrics on efficiencies and what improvements can be made.

We can use the data to compare driver behaviors of those using the tool against those not using the webapp. For example, we can assess efficiencies such as length of time for stops, time spent searching for parking, number of deliveries performed per stop, and whether the webapp is actually helping improve or change actions drivers are taking when making a delivery.

—Giacomo Dalla Chiara, a postdoctoral scholar from the Urban Freight Lab

Dalla Chiara said freight carriers have expressed enthusiasm for the webapp, but the research team also hopes to extend it to additional carriers and drivers.

Beyond Belltown, the team is also assessing the webapp in a location in Bellevue, Washington, just east of Seattle. The project is supported by the US Department of Energy’s Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office.

Resources

• Dalla Chiara, Giacomo and Cheah, Lynette and Azevedo, Carlos Lima and Ben-Akiva, Moshe E. (2020) “A Policy-Sensitive Model of Parking Choice for Commercial Vehicles in Urban Areas.” Transportation Science doi: 10.1287/trsc.2019.0970