Pursue technology based, high risk policies incrementally to better manage likely organizational and technological challenges.

San Francisco Municipal Transportation Agency's experience in implementing advanced parking management (Interim Results).

August 2011
San Francisco,California,United States

Background (Show)

Lesson Learned

The SFpark pilot project of the San Francisco Municipal Transportation Agency (SFMTA) uses a demand-based approach to adjusting parking rates at metered parking spaces in the SFpark pilot areas and at SFpark garages. SFpark's combination of time-of-day demand-responsive pricing and off-peak discounts at garages is expected to reduce circling and double-parking, as well as influence when and how people choose to travel. Lessons learned from the SFpark implementation and operations are presented below.
  • Enforce parking policies effectively. Parking policies require effective enforcement. Without it, the benefit of any policy change is likely to be low.
  • Be cognizant of urgency associated with federally funded projects. Federal project deadlines created an urgency that is uncommon in public projects and gave SFMTA aggressive goals to work towards.
  • Beware that parking management technologies may require customization. The technology used in SFpark is not plug-and-play. Implementing SFpark required a lot of hand coding for different technologies to work together. As this field and market matures, this problem will likely diminish, but for now this will remain an issue for any city.
  • Expect organizational changes and challenges. Creating the SFpark data management system and then preparing to run a real-time information service required several significant changes within SFMTA as an organization. From a technical perspective, it has challenged the SFMTA to determine the best ways to use, support, and maintain the system with the rigor that is required for providing a high-availability data service.
  • Prepare for technological failures. Most technology used did not meet SFMTA's initial expectations. In particular, the accuracy and reliability of parking sensors is not perfect, which limits the possibilities of what can be done with that data. However, it is unlikely that a city with a high and/or unpredictable degree of non-payment can do demand-responsive pricing or offer real-time parking availability data without parking sensors. Parking sensor data is new, subtle, and complex. Over the next several years parking managers will be establishing new ways to understand and use that data.
  • Pursue technology based, high risk projects incrementally. Pursuing SFpark on a pilot basis was a sound approach. To have attempted this change all at once citywide would have had an unacceptably high risk of failure.
Cities around the world are interested in the common and urgent goals of reducing traffic congestion and transportation related greenhouse gas emissions. To the extent that SFpark successfully manages parking supply and demand, rates, and reduces congestion and emissions, the project is also relevant to other cities because it is easily replicable. SFpark is expected to improve traffic flow, reduce congestion and greenhouse gas emissions, increase safety for all road users, and enhance quality of life.

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SFpark: Putting Theory Into Practice - Post-launch implementation summary and Lessons learned

Published By: San Francisco Municipal Transportation Agency

Source Date: August 2011

URL: http://sfpark.org/wp-content/uploads/2011/09/sfpark_aug2011projsummary_web-2.pdf

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Lesson ID: 2012-00624