San Francisco’s smart parking project SFpark made supporting economic development the primary goal. Store owners often complained that it was difficult for their customers to find a place to park. If customers can’t park, they can’t buy anything, and shopkeepers are impacted.
To solve this problem, SFpark aimed their smart parking project at managing demand for parking spots so that people, when they choose to drive, rarely had to circle a block to find parking. Another challenge was the scope of the project. San Francisco has 441,530 parking spots. Managing these spaces and the amount of data collected requires much more than a simple database: it needed a big data business intelligence strategy.
SFpark had a unique way of looking at parking spots and considered them assets. In doing so, they could determine what the return on those assets would be. In a business, return on assets is a critical KPI and a vital measure of a company’s wisdom and management strength.
The measures of return on assets may not always be financial, as in the case of SFpark. SFpark looked beyond a simple financial return on assets and considered other goals and strategies for their smart parking initiative.
To accomplish smart parking initiative goals, a smart parking solution needs to deliver specific business intelligence capabilities. These capabilities can be summed up in the following past, present and future analysis capabilities:
Present Real-time Analysis:
The ability to develop a solid parking transformation strategy starts by understanding the actual current use of the parking spot inventory. This is an example of SFpark’s parking garage occupancy analysis report.
Detailed data capture of specific parking spots and parking areas
This example highlights SFpark’s ability to drill down on specific parking spot/area/district occupancy.
Enforcement: Citation per metered space
Here is the detailed analysis of citations per metered parking spot.
Present Real-Time Analysis
Ability to guide a driver to parking spot availability
Drivers are mobile, and so a mobile guidance solution is mandatory to guide drivers to available parking. Here is an example of the parking information application that SFpark has implemented based on real-time data analysis.
Ability to calculate Parking occupancy Rates
This example shows SFpark’s ability to predict the impact of parking rates on occupancy.
Occupancy Rate recommendations
This report enables SFpark to generate occupancy rate recommendations.
I have only scratched the surface of how big data business intelligence technology can help you accomplish your smart parking goals. In our next blog, we will continue to examine additional case studies on how big data business intelligence analytics can help you improve your parking revenue.
About the Author:
Bill is the Digital Strategist for FoxNet Solutions. Formerly the Cloud Chief Technologist for Hewlett-Packard Enterprise Canada, Bill has provided Hybrid IT and IoT Strategic Planning advisory and planning services to over fifty Private and Public sector clients to help them migrate to a Hybrid IT Cloud Operating model. These transformation plans have helped both government and industry reduce the cost of IT, re-engineer their IT governance models, and reduce the overall complexity of IT. Bill is also a member of the Open Alliance for Cloud Adoption Team and has co-authored several documents on Cloud Maturity and Hybrid IT implementation.