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Quantifying the potential savings in travel time resulting from parking guidance systems — a simulation case study

Author

Listed:
  • B J Waterson

    (University of Southampton)

  • N B Hounsell

    (University of Southampton)

  • K Chatterjee

    (University of Southampton)

Abstract

Parking Guidance and Information (PGI) signs are thought to enable a more efficient use of the available parking stock. Despite the installation of PGI systems in many cities and their operation for a number of years, there is a lack of reliable evidence of the size of the benefits that these systems can achieve. This paper describes the development of driver parking choice models (both during the journey and pre-trip) and the implementation of these models in the existing network traffic simulation model RGCONTRAM. Besides quantifying the effects of the PGI system on both the drivers seeking suitable parking spaces and the parking stock itself, this also enables quantification of the impact of parking choice on the wider network. Factors influencing PGI effectiveness are described and conclusions are drawn that illustrate the potential of PGI to induce the demand to spread more efficiently across the parking stock.

Suggested Citation

  • B J Waterson & N B Hounsell & K Chatterjee, 2001. "Quantifying the potential savings in travel time resulting from parking guidance systems — a simulation case study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(10), pages 1067-1077, October.
    • Handle: RePEc:pal:jorsoc:v:52:y:2001:i:10:d:10.1057_palgrave.jors.2601207
    DOI: 10.1057/palgrave.jors.2601207
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    Citations

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    Cited by:

    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. Rodier, Caroline J & Shaheen, Susan A, 2010. "Transit-based smart parking: An evaluation of the San Francisco Bay area field test," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6c6613ks, Institute of Transportation Studies, UC Berkeley.
    3. Rodier, Caroline J. & Shaheen, Susan, 2006. "Transit-Based Smart Parking in the U.S.: Behavioral Analysis of San Francisco Bay Area Field Test," Institute of Transportation Studies, Working Paper Series qt60j0r6m5, Institute of Transportation Studies, UC Davis.
    4. Xiao, Jun & Lou, Yingyan & Frisby, Joshua, 2018. "How likely am I to find parking? – A practical model-based framework for predicting parking availability," Transportation Research Part B: Methodological, Elsevier, vol. 112(C), pages 19-39.
    5. Chai, Huajun, 2019. "Dynamic Traffic Routing and Adaptive Signal Control in a Connected Vehicles Environment," Institute of Transportation Studies, Working Paper Series qt9ng3z8vn, Institute of Transportation Studies, UC Davis.
    6. Rodier, Caroline J. PhD & Shaheen, Susan A. PhD & Kemmerer, Charlene, 2008. "Smart Parking Management Field Test: A Bay Area Rapid Transit (BART) District Parking Demonstration; Final Report," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt3w33t61b, Institute of Transportation Studies, UC Berkeley.
    7. Pel, Adam J. & Chaniotakis, Emmanouil, 2017. "Stochastic user equilibrium traffic assignment with equilibrated parking search routes," Transportation Research Part B: Methodological, Elsevier, vol. 101(C), pages 123-139.
    8. Teodorovic, Dusan & Lucic, Panta, 2006. "Intelligent parking systems," European Journal of Operational Research, Elsevier, vol. 175(3), pages 1666-1681, December.
    9. Cao, Jin & Menendez, Monica, 2018. "Quantification of potential cruising time savings through intelligent parking services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 151-165.

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