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First published online January 1, 2016

Demand-Responsive Pricing on the Cheap: Estimating Parking Occupancy with Meter Payment Data

Abstract

The SFpark pilot by the San Francisco Municipal Transportation Agency in California was the first large-scale test of demand-responsive parking pricing in a major city. Several evaluations of the pilot showed that the project yielded substantial benefits. However, measuring parking occupancy is critical to implementing demand-responsive pricing. San Francisco relied on wireless in-ground parking sensors to measure parking occupancy for the SFpark pilot, but those sensors met the end of their useful lives and were deactivated. Parking sensors are still a nascent and costly technology that presents a great deal of risk to cities. Yet many cities, including San Francisco, are adopting new parking meters that make meter payment data widely available. Using sensor and meter data from the SFpark pilot, the agency developed a sensor-independent rate adjustment model that estimated parking occupancy by using meter payment data. Although not everyone pays the meter when they park, the model can reliably estimate occupancy to support demand-responsive pricing. This capability allows San Francisco to continue its SFpark program and lays the foundation for other cities to implement demand-responsive pricing and promote the benefits of better parking policy more widely.

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References

1. Shoup D. The High Cost of Free Parking. Planners Press, Chicago, Ill., 2005.
2. SFpark Pilot Project Evaluation: The SFMTA’s Evaluation of the Benefits of the Sfpark Pilot Project. San Francisco Municipal Transportation Agency, Calif., 2014.
3. Zimmerman C., Klein R., Schroeder J., Turnbull K., Balke K., Burris M., Saunoi-Sandgren E., Martin E., Shaheen S., Rodier C., Schreffler E., and Joy B. San Francisco Urban Partnership Agreement: National Evaluation Report. Report FHWA-JPO-14-128. FHWA, U.S. Department of Transportation, 2014.
4. Millard-Ball A., Weinberger R., and Hampshire R. Is the Curb 80% Full or 20% Empty? Assessing the Impacts of San Francisco’s Parking Pricing Experiment. Transportation Research Part A, Vol. 63, 2014, pp. 76–92.
5. Performance-Based Parking Pilots. District Department of Transportation, Washington, D.C. http://ddot.dc.gov/service/performance-based-parking-pilots. Accessed Oct. 31, 2015.
6. Performance-Based Parking Pricing Study. Seattle Department of Transportation, Wash., 2011. http://www.seattle.gov/transportation/parking/docs/SDOT_PbPP_FinRpt.pdf. Accessed Oct. 31, 2015.
7. SeaPark Is SDOT’s Performance-Based Parking Pricing Program. Seattle Department of Transportation, Wash. http://www.seattle.gov/transportation/parking/signs_icons.htm. Accessed Nov. 12, 2015.
8. Downtown Berkeley Parking and Transportation Demand Management Report. Nelson\Nygaard Consulting Associates, 2011. http://www.ci.berkeley.ca.us/uploadedFiles/Public_Works/Level_3_-_Transportation/BERKELEY%20PTDM%20DRAFT%20FINAL%20-%20NEW.pdf. Accessed Oct. 31, 2015.
9. goBerkeley Pilot Program Results and Next Steps. Office of the City Manager, Berkeley, Calif., Dec. 16, 2014. http://www.cityofberkeley.info/Clerk/City_Council/2014/12_Dec/Documents/2014-12-16_Item_38_goBerkeley_Pilot_Program.aspx. Accessed Oct. 31, 2015.
10. SFpark Putting Theory into Practice: Pilot Project Summary and Lessons Learned. San Francisco Municipal Transportation Agency, Calif., 2014.
11. Parking Sensor Technology Performance Evaluation. San Francisco Municipal Transportation Agency, Calif., 2014.
12. Tamrazian A., Qian Z., and Rajagopal R. Where Is My Parking Spot? Online and Offline Prediction of Time-Varying Parking Occupancy. In Transportation Research Record: Journal of the Transportation Research Board, No. 2489, Transportation Research Board, Washington, D.C., 2015, pp. 77–85.
13. Shaaban K., and Tounsi H. Parking Space Detection System Using Video Images. In Transportation Research Record: Journal of the Transportation Research Board, No. 2537, Transportation Research Board, Washington, D.C., 2015, pp. 137–147.
14. SFpark Rate Adjustment Policy: On-Street Parking. Revision 1. San Francisco Municipal Transportation Agency, Calif., June 8, 2011. http://sfpark.org/wp-content/uploads/2011/06/SFpark_Pricing_OnStreetPolicy_110608.pdf. Accessed Nov. 11, 2015.
15. Pierce G., and Shoup D. SFpark: Pricing Parking by Demand. Access, Vol. 43, 2013, pp. 20–28.
16. Blakeley M., and Gray N. Saving Time and Money: Time-Lapse Cameras Measure Street Parking Demand. ITE Journal, Vol. 83, No. 9, 2013, pp. 36–39.

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Article first published online: January 1, 2016
Issue published: January 2016

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© 2016 National Academy of Sciences.
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Authors

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Alex Demisch
San Francisco Municipal Transportation Agency, 1 South Van Ness Avenue, San Francisco, CA 94103

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