Collecting accurate data on parking trends and needs in a cost-effective and timely way is a perennial problem for municipalities and developers. In the past, a baseline was established through manual data collection of parking behaviour. However, MMM Group (MMM) pioneered a new solution for the District of North Vancouver (DNV)’s Deep Cove parking study. Using License Plate Recognition (LPR), an on-the-market and affordable technology, MMM was able to provide the DNV with more accurate, timely, cost-effective, and relevant data for decision making.
The Technical Challenge
Nestled on forested mountain slopes on the east side of North Vancouver, B.C. is the idyllic waterfront residential community of Deep Cove, which is also a popular destination for visitors wishing to enjoy the outdoors, kayaking on the still waters of Indian Arm, taking in the shops and galleries of Deep Cove Village, picnicking and swimming at Panorama Park, or climbing the Baden Powell Trail to take in the stunning vistas from Quarry Rock. The popularity of this destination has resulted in a downside for the community. Residents, visitors, and businesses must compete for the limited number of public parking spaces (about 600). MMM was retained by the DNV to study parking in Deep Cove, and to develop an implementation plan that addresses and balances the needs of residents, visitors, and businesses.
Before identifying the solutions that would improve parking in Deep Cove, the existing conditions needed to be understood. This included the collection and analysis of parking metrics such as average and peak parking occupancy, duration and turnover, and trip origin. Traditionally, this data is collected manually through licence plate surveys that record the last four letters / digits from license plates on a block-by-block basis over an extended period of time, such as six consecutive hours. Manual data collection and data entry are expensive, manpower-intensive, time-consuming given the large number of data points, and subject to a relatively large number of field and transcription error sources. The partial nature of the license plate data also precludes using the manually collected data to establish trip origin.
To overcome these issues, MMM’s solution was to use a vehicle equipped with License Plate Recognition (LPR) technology to gather the desired data. LPR technology automates license plate reading and identification for easier to collect data on a block-by-block and lot-by-lot basis. The LPR cameras are also designed to be mounted in a vehicle, which made it quick and easy for the two-person team to conduct the six-hour surveys of the 74 block faces and three parking lots over a two-day period.
This is an unusual and new use of LPR technology to solve a common challenge for municipalities, cities, developers, and planners for a variety of development types, including airports, universities, sports and event facilities, and shopping centres.
For the Deep Cove parking study, the benefits of LPR technology included:
- Rapid mobilization, as only two people needed to be trained
- Collection of nearly 4,000 data sets at a low cost
- Completion of data collection, entry, and processing in days, instead of weeks
- Detailed information about average and peak parking occupancy, duration, turnover, and trip origin for Deep Cove and each of its four subareas
As a result of the LPR approach put forward by MMM, the DNV was able to make the decisions about the solution based on a more complete set of data that was more efficient and cost effective to collect and process.