Abstract Manager

During my career I have undertaken many parking studies on behalf of municipalities and private clients, using the tried-and-true method of manual vehicle counts, usually undertaken by summer students or co-ops, along with supervising technicians, the odd client representative as well as myself. It always struck me that there was a lack of transparency and granularity in the data set that was collected, and there was especially a tendency for errors to creep into the process by the end of a long day walking the streets and parking lots. I have also been aware of many near-misses and other safety issues as technicians navigate parking lots in low-light and inclement weather conditions.

When the City of Courtenay approached McElhanney to update their Downtown Parking Study, we saw an opportunity to do things a little differently and, with the enthusiastic support of the City, we scoped out a new methodology for collecting meaningful parking data using the latest technology that would provide ongoing utility and value to the City beyond the timeframe of the study itself.

In this session, we will review the traditional method of parking data collection and outline the strengths and weaknesses of this approach. We will then give an overview of the new methodology that we have deployed with the City of Courtenay and demonstrate how leveraging emerging technologies can provide safer, more reliable, higher granularity parking data.

The approach to blend drone photography, digital imaging software, GIS data handling, open-source machine learning, and transportation planning and engineering principles is a new and innovative way to provide higher-value consulting services to municipal and private sector clients, ultimately leading to better planning, approvals, community engagement, and investment decisions for urban mobility projects.