Parking Industry

Predictive Analytics in Parking2 min read

May 25, 2019 2 min

Predictive Analytics in Parking2 min read

Reading Time: 2 minutesPredictive Analytics in Parking

Predictive analytics is the use of machine learning and statistics to anticipate future outcomes based on historical data sets. The technology has made waves in several industries, promising new tools that offer advanced analytics capabilities. In the parking business, predictive analytics can be employed by facility owners to anticipate occupancy during different periods of the day, identify consumer patterns, and accordingly price spaces to increase revenue.

How predictive analytics can transform parking

At present, smart parking systems generate a ton of customer data. The real value, however, lies in putting this data to use. For instance, a facility could receive a customer request to reserve a parking spot. Using predictive analytics in parking, the management could anticipate occupancy based on historical patterns and reserve a spot accordingly.

Pricing is another area where predictive analytics can help businesses boost revenue. By studying past occupancy trends and identifying peak hours and days when demand is the highest, a business can set dynamic pricing and charge more during peak times.

Also readUsing Video Analytics for Smarter Parking

Using predictive analytics in parking

The city of Aspen noticed a wide variation in seasonal parking occupancy – demand was high in the summers and winters and low at other times a year. Using predictive analytics, the city analyzed patterns of vehicle use and created a strategy for implementing dynamic pricing. The result? There was a clear shift in the utilization of parking spots and the city increased its revenue.

A similar smart parking system was rolled out in Pittsburgh. Discoveries from their assessment uncovered that one out of each two respondents saying the application had decreased the overall time it takes to discover a parking spot from times ranging from less than a minute to over six minutes, with most of the people noting a 4-6 minute decrease in search time. It was estimated that this would save upwards of $1.2 million at full-scale implementation.

Given Indian cities are much more densely populated and the cars easily outnumber the number in Pittsburgh, the savings would be far greater in terms of fuel and reductions in wastage of working hours. Clearly, the future of parking, much like any other industry, lies in data and extracting rich insights from it.