I first really learned about “Big Data” at the PIE 2016 show in Las Vegas. It was talked about in many seminars I participated in. Then, in the exhibitor area of the show, I was lucky enough to connect with a company named Smarking, which uses data analytics to help parking managers get the most out of their facilities.
They said they could turn my parking data into useful and presentable information. Wow, have they done a great job for Aspen, CO.
Big Data has given me, the city’s Parking Director, the ability to measure and graphically display, with data, the exact results of the programs and initiatives I have worked on in Aspen. It has become significantly easier to communicate and prove our results to the public, City Council and the media on what our goals are and why we made the changes that have been instituted.
Big Data has helped evaluate data and optimize price points and visually communicate goals and results to all interested stakeholders in the community. The data I review every day are hourly peak occupancy, peak occupancy, revenue by type and duration.
I have been able to track real-time results and compare them with the stated goals I shared with City Council.
Aspen has extreme seasonal demand for parking, with thousands of tourists driving in the summer season. In 2015, parking occupancies varied from about 50% in the off-peak season and more than 100% in the busy summer months.
With the help of Big Data, I was able to convince city council to try a three-month test program in 2016 to manage parking demand.
The policies put in place to help alleviate parking and traffic congestion included:
Increase parking rates 50% only during the peak summer season in the downtown core.
Keep parking prices low in the parking garage and residential areas.
Start a “free” Downtowner door-to-door, on-demand electric shuttle service.
Implement a “Drive-Less” campaign.
Promote the use of “We-Cycle,” Aspen’s shared-bike program.
The goals were to:
Reduce vehicle traffic coming into Aspen.
Cut down on vehicle congestion in town.
Have no more than 90% parking occupancy in the Downtown core.
Increase transit, carpool, pedestrian and bicycle trips.
Encourage use of the parking garage, Brush Creek Intercept lot and the residential zones.
Big Data has given me the ability to measure and graphically display the exact results of the programs and initiatives I have worked on in Aspen.
The three-month test was carefully measured and results showed that the program went above expectations. Parking occupancy decreased during peak periods, making it easier for visitors to find a place to park. Parking turnover increased, revenue increased and more vehicles were parking in the garage and in the residential area, rather than the congested downtown core.
In addition, the number that really matters is that Aspen had one of its best retail and restaurant summers ever, with sales tax revenue up over 20% for the summer months.
With the help of Big Data, we were able to prove to City Council and the citizens that the plan worked exactly as laid out. We achieved all the goals as rolled out.
Because I was able to show council the data in real-time, it was easy to get it to adopt the new parking rates for the future. We will now have seven months of higher parking rates and five months of lower ones.
The next step for Aspen is to use Big Data to go to real dynamic pricing and change the parking prices by day of the week and month of the year. With Smarking’s help, I believe we can get the planned approved in the next six months.
Mitch Osur, City of Aspen Parking Director. Contact him at mitchosur@cityofaspen.com.
BIG DATA: According to Wikipedia:
“Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate to deal with them. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating and information privacy. The term “big data” often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. “There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem.”
