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Once unimaginable analytics that are now practical


The combination of technology and analytics software is solving problems in ways that only a few years ago were unimaginable.



Gary Cokins
Gary Cokins
Technology’s contribution is with the Internet and high performance computing (HPC). Analytics’ contribution to this marriage with technology is – to keep this simple – the math. It is the equations and algorithms. Here is a practical example of the union involving automobile traffic congestion in cities.

San Francisco’s automobile street parking solution
Every major city, like New York or Paris, is experiencing high automobile traffic density. Crude solutions to limit the number of cars entering city boundaries, such as in London, include pricey city entry tolls and restrictive car tag registration fees. There are not much analytics involved there. But that type of controlled city access does not solve the aggravating problem of insufficient – often non-existent – available street parking that worsens traffic congestion.

It is a maddening quest and often competitive for drivers to locate a curbside parking space on a city street. This results in wasteful lost time for the driver and costly auto fuel by the car, and it is harmful to the environment with polluted air from excess emissions. A New York Times article, “A Meter So Expensive, It Creates Parking Spots,” states that as much as a third of car traffic in some city locations is cars continuously circling as drivers hunt for an open space to park in. The article describes a successful experiment by the San Francisco Municipal Transportation Agency that applies analytics in a way few could have ever imagined – until a few years ago.

Economics 101 – pricing and the law of supply and demand
San Francisco’s objective is to substantially reduce a driver’s hunt time and constant circling traffic by aiming to always have at least one empty parking spot available on every block that has parking meters.

How does analytics apply to this problem? They involve quantifying the price elasticity of the number of consumer / buyers (i.e., car drivers) and their sensitivity to price levels. The analytics raises the price of parking on the crowded blocks and lowers them on the emptier blocks. The analytics accomplish this by dynamically adjusting each parking meter’s hourly rate to achieve the objective of “at least one empty parking space per block.” Sensors and Internet-enabled parking meters enable the solution, but it is the back-office analytical software that delivers the solution.

With analytics, if you can imagine then you can do it!
This curbside parking space solution is in an early stage and continuing to improve. An independent assessment confirms this approach is having the desired effect and reported that 3/4th of the blocks either hit their targets or moved closer to the goal. To reduce the circling the city has also cut prices at many of its parking lots and garages to lure cars off the street.

Like me, you may have social equity and fairness concerns that this type of street parking solution adversely affects lower income drivers making desirable parking areas less accessible and thus favoring the more affluent drivers. San Francisco officials acknowledge that potential inequities become thorny issues. Analytics is rarely without complications and trade-offs between interest groups. The officials noted that all of the parking meter revenues are used for mass transit bus and rail and any reduction in traffic density will speed the buses that many people rely on.

What intrigued me most about this is that the idea for the solution was proposed by theories of Donald Shoup, an urban planning professor at UCLA. His 2005 book, “The High Cost of Free Parking,” has made him a cult figure to city planners. A Facebook group, The Shoupistas, has more than a thousand members. Are they a cult? Not really. They simply share the breakthrough thinking that technology and analytics enable.

With more data the dynamic parking meter pricing can be self-learning and eventually be optimized for each hour of the day and factor in adjustments for week-ends, holidays, weather, Fridays, and other special conditions.

Analytics that are practical and feasible
With analytics, finding an available parking space no longer needs to be a result of luck, good timing, or karma. It can be scientific.

With analytics, approaches to problems and opportunities that once one could not have imagined can now be applied.

Gary Cokins, CPIM
(gary.cokins@sas.com; phone: 919-531-2012)
blogs.sas.com/content/cokins

Gary Cokins (Cornell University BS IE/OR, 1971; Northwestern University Kellogg MBA 1974) is an internationally recognized expert, speaker, and author in advanced cost management and enterprise performance and risk management systems. He is a Principal in business consulting involved with analytics-based enterprise performance management solutions with SAS, a global leader in business intelligence and analytics software. He began his career in industry with a Fortune 100 company in CFO and operations roles. He then worked 15 years in consulting with Deloitte, KPMG, and EDS. His two most recent books are Performance Management: Finding the Missing Pieces to Close the Intelligence Gap (ISBN 0-471-57690-5) and Performance Management: Integrating Strategy Execution, Methodologies, Risk, and Analytics (ISBN 978-0-470-44998-1). Mr. Cokins can be contacted at gary.
cokins@sas.com
123 words.

Mardi 27 Mars 2012
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