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Decision Analytics for Parking Availability in Downtown Pittsburgh

Author

Listed:
  • Tayo Fabusuyi

    (Numeritics, Pittsburgh, Pennsylvania 15206)

  • Robert C. Hampshire

    (H. John Heinz III College, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

  • Victoria A. Hill

    (Numeritics, Pittsburgh, Pennsylvania 15206)

  • Katsunobu Sasanuma

    (H. John Heinz III College, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

Abstract

ParkPGH is a novel parking application that provides real-time and predictive information on the availability of garage parking spaces within Pittsburgh’s Cultural District. The core of the application is a module that collects real-time parking information from the garages by tapping into their gate counts. The real-time component is complemented by a module that uses historical data and an events calendar to predict parking availability. In 2011, visitors to downtown Pittsburgh used ParkPGH more than 300,000 times to determine when and where to park. The application has also been beneficial to garage operators because the information it provides on parking demand affords them greater flexibility in addressing contingencies and managing lease holders.The deployment of ParkPGH, which includes a robust evaluation component, is one piece of a broader transportation ecosystem within the Greater Pittsburgh region. The lessons we learned from the initiative, the application’s relatively low cost, its ease of retrofitting, and its open-source platform can enable other cities to lower the costs of implementing and managing similar smart-parking solutions and significantly shorten their learning curves.

Suggested Citation

  • Tayo Fabusuyi & Robert C. Hampshire & Victoria A. Hill & Katsunobu Sasanuma, 2014. "Decision Analytics for Parking Availability in Downtown Pittsburgh," Interfaces, INFORMS, vol. 44(3), pages 286-299, June.
    • Handle: RePEc:inm:orinte:v:44:y:2014:i:3:p:286-299
    DOI: 10.1287/inte.2014.0743
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    References listed on IDEAS

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    1. Shaheen, Susan A & Kemmerer, Charlene, 2008. "Smart Parking Linked to Transit: Lessons Learned from Field Test in San Francisco Bay Area of California," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt3wh3r99g, Institute of Transportation Studies, UC Berkeley.
    2. Shaheen, Susan & Kemmerer, Charlene, 2008. "Smart Parking Linked to Transit: Lessons Learned from the Field Test in San Francisco Bay Area of California," Institute of Transportation Studies, Working Paper Series qt2bd6m65k, Institute of Transportation Studies, UC Davis.
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    Citations

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    Cited by:

    1. Fabusuyi, Tayo & Hampshire, Robert C., 2018. "Rethinking performance based parking pricing: A case study of SFpark," Transportation Research Part A: Policy and Practice, Elsevier, vol. 115(C), pages 90-101.
    2. Tayo Fabusuyi & Michael P Johnson, 2022. "Enhancing the quality and social impacts of urban planning through community-engaged operations research," Environment and Planning B, , vol. 49(4), pages 1167-1183, May.
    3. Qi Feng & J. George Shanthikumar & Mengying Xue, 2022. "Consumer Choice Models and Estimation: A Review and Extension," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 847-867, February.
    4. Li, Baibing, 2022. "Stochastic modeling and adaptive forecasting for parking space availability with drivers’ time-varying arrival/departure behavior," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 313-332.

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    1. Rodier, Caroline & Shaheen, Susan A. & Blake, Tagan, 2010. "Smart Parking Pilot on the Coaster Commuter Rail Line in San Diego, California," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt06s723rw, Institute of Transportation Studies, UC Berkeley.

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