Anticipatory Parking

This IoT use case was carried out in conjunction with the City of Saint John Parking Commission and the HotSpot Parking Company. Data was utilized from both organizations to build a parking prediction model for predicting whether a parking spot is occupied or vacant within the next hour, or the next day. The scenario is that drivers and autonomous cars will know vacant parking spots before arriving at the destination. The research was focused on providing a working prototype of a low-cost solution to the City of Saint John. We have developed a prediction model based on the Random Forest approach. The streaming data from 2016 and 2017 were used to train the model, which was then tested against new HotSpot data from January and February 2018, and predictions were made for April 2018. The accuracy of prediction was 90%. Parking prediction systems can drastically decrease parking times between 20 and 40% in Saint John. This means improving traffic flow by up to 30% and a decreasing in carbon emissions due to a shorter trip time.

University of New Brunswick


The People in Motion Lab is located at the Department of Geodesy and Geomatics.


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University of New Brunswick

Department of Geodesy & Geomatics Engineering

Head Hall, E-50
P.O. Box 4400
Fredericton, N.B. E3B 5A3