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access icon free Optimal storage and loading zones within surface parking facilities for privately owned automated vehicles

There is much speculation about the prospective impacts of automated vehicles (AVs) on parking supply and behaviour, however the literature contains little quantitative evidence. In this study, we develop a mixed-integer non-linear optimisation (MINLP) model of revenue maximisation to design parking facility layouts for privately-owned automated cars that include separate vehicle-storage and drop-off/pick-up zones (DOPU, or alternatively termed “PUDO zones”). The control variable is the allocation of space between these two competing uses. The model balances between revenue derived from parking (including revenue during activity time) and costs associated with the range of AVs’ parking and loading/unloading activities. Via numerical analysis of an archetypal shopping centre's parking facility, the authors demonstrate that the model responds intuitively to the stimulus of systematically varying the input parameters. This study is intended to provide an incremental advance to support researchers and practitioners tasked with quantifying the impacts of AVs on the parking system.

References

    1. 1)
      • 2. Average Fuel Efficiency of U.S. Passenger Cars and Light Trucks. Available at https://www.bts.gov/content/average-fuel-efficiency-us-passenger-cars-and-light-trucks, last accessed 6/15/2019.
    2. 2)
      • 9. Belzner, H., Pedron, P.: ‘Method for processing measurement data of a vehicle in order to determine the start of a search for a parking space’. U.S. Patent Application 2016/0210860, 2014.
    3. 3)
      • 17. Nissan Motor Company Ltd. (n.d.) Concept Car Pivo 2. Available at https://www.nissan-global.com/EN/MOTORSHOW/2011/TOKYO/PIVO3/, last accessed 6/15/19.
    4. 4)
      • 19. Nourinejad, M., Bahrami, S., Roorda, M.J.: ‘Designing parking facilities for autonomous vehicles’, Transp. Res. B, Methodol., 2018, 109, pp. 110127.
    5. 5)
      • 16. Vincent, R.: ‘When car ownership fades, this parking garage will be ready for its next life’. Los Angeles Times. Available at http://www.latimes.com/business/la-fi-car-future-real-estate-20170405-story.html, accessed on 1/1/18.
    6. 6)
      • 5. Wadud, Z., MacKenzie, D., Leiby, P.: ‘Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles’, Transp. Res. A, Policy Pract., 2016, 86, pp. 118.
    7. 7)
      • 6. Dayan, M.: ‘System and method for determining and reserving available parking’. U.S. Patent Application 2011/0022427, 2011.
    8. 8)
      • 35. Larson, R.C., Sasanuma, K.: ‘Congestion pricing: A parking queue model’. MIT Engineering Systems Division Working Paper #ESD-WP-2007-23, 2007.
    9. 9)
      • 33. Millard-Ball, A., Weinberger, R.R., Hampshire, R.C.: ‘Is the curb 80% full or 20% empty? Assessing the impacts of San Francisco's parking pricing experiment’, Transp. Res. A, Policy Pract., 2014, 63, pp. 7692.
    10. 10)
      • 22. Kong, Y., Le Vine, S., Liu, X.: ‘Capacity impacts and optimal geometry of automated cars’ surface parking facilities’, J. Adv. Transp., 2018, 2018, pp. 113.
    11. 11)
      • 23. Nourinejad, M.: ‘Economics of parking: short, medium, and long-term planning’. PhD thesis, University of Toronto, 2017.
    12. 12)
      • 15. Ji, Y., Tang, D., Blythe, P., et al: ‘Short-term forecasting of available parking space using wavelet neural network model’, IET Intel. Transp. Syst., 2013, 9, (2), pp. 202209.
    13. 13)
      • 38. Padberg, M., Rinaldi, G.: ‘A branch-and-cut algorithm for the resolution of large-scale symmetric traveling salesman problems’, SIAM Rev., 1991, 33, (1), pp. 60100.
    14. 14)
      • 39. Bussieck, M.R., Vigerske, S.: ‘MINLP solver software. Wiley encyclopedia of operations research and management science’, 2010.
    15. 15)
      • 26. Arnott, R., Inci, E., Rowse, J.: ‘Downtown curbside parking capacity’, J. Urban Econ., 2015, 86, pp. 8397.
    16. 16)
      • 3. Gasoline and Diesel Fuel Update. Available at https://www.eia.gov/petroleum/gasdiesel/, last accessed 6/15/2019.
    17. 17)
      • 12. Sun, D.J., Ni, X.-Y., Zhang, L.-H.: ‘A discriminated release strategy for parking variable message sign display problem using agent-based simulation’, IEEE Trans. Intell. Transp. Syst., 2016, 17, (1), pp. 3847.
    18. 18)
      • 31. Chodash, I.L.: ‘Relative efficiencies of various parking angles’, ITE J., 1986, 56, (3), pp. 3437.
    19. 19)
      • 11. Elie, L.D., Rhode, D.S.: ‘System and method for autonomous valet parking using plenoptic cameras’. U.S. Patent US9557741B1, 2017.
    20. 20)
      • 36. Floudas, C.A.: ‘Nonlinear and mixed-integer optimization: fundamentals and applications’ (Oxford University Press, Oxford, UK, 1995).
    21. 21)
      • 32. Larson, R.C., Odoni, A.R.: ‘Urban operations research’, 1981.
    22. 22)
      • 10. Urbach, S.R.: ‘Systems and methods for determining parking difficulty of segments of a geographic area’. US Patent 8,847,791, 2014.
    23. 23)
      • 25. Childress, S., Nichols, B., Charlton, B., et al: ‘Using an activity-based model to explore possible impacts of automated vehicles’, Transp. Res. Rec. J. Transp. Res. Board, 2015, 2493, pp. 99106.
    24. 24)
      • 8. Kuhlman, F.F., Sarma, D.H.R., Harback, A.P.: ‘Vehicle parking spot locator system and method using connected vehicles’. U.S. Patent Application 2012/0056758, 2012.
    25. 25)
      • 28. Belenky, P.: ‘Revised departmental guidance on valuation of travel time in economic analysis’. US Department of Transportation, Washington, DC, 2011.
    26. 26)
      • 21. Estepa, R., Estepa, A., Wideberg, J., et al: ‘More effective use of urban space by autonomous double parking’, J. Adv. Transp., 2017, 2017, pp. 110.
    27. 27)
      • 29. Urban Land Institute [ULI] The Dimensions of Parking. National Parking Association. 5th ed, 2010.
    28. 28)
      • 30. Ricker, E.R.: ‘Traffic design of parking garages’, 1957.
    29. 29)
      • 37. LINDO Software for Integer Programming. Available at http://www.lindo.com/index.php/, access on 6/15/19.
    30. 30)
      • 7. Levine, U., Shinar, A., Shabtai, E.: ‘System and method for parking time estimations’. US Patent 7,936,284, 2011.
    31. 31)
      • 27. Millard-Ball, A.: ‘The autonomous vehicle parking problem’, Transp. Policy, 2019, 75, pp. 99108.
    32. 32)
      • 34. Ma, J., Clausing, E., Liu, Y.: ‘Smart on-street parking system to predict parking occupancy and provide a routing strategy using cloud-based analytics(No. 2017-01-0087). SAE Technical Paper, 2017.
    33. 33)
      • 13. Ni, X.-Y., Sun, D.J.: ‘Agent-based modelling and simulation to assess the impact of parking reservation system’, J. Adv. Transp., 2017, 2017, pp. 110.
    34. 34)
      • 24. Kim, K., Rousseau, G., Freedman, J., et al: ‘The travel impact of autonomous vehicles in metro atlanta through activity-based modeling’. 15th Annual Transportation Applications Conf., Washington, DC, USA, 2015. Retrieved 2/1/19. Available at https://www.trbappcon.org/oldsite/2015conf/presentations/198_AgingImpact_Kyeil%20Kim_Final.pptx.
    35. 35)
      • 20. Timpner, J., Friedrichs, S., Van Balen, J., et al: ‘K-Stacks: high-density valet parking for automated vehicles’, IEEE Intell. Veh. Symp. Proc., 2015, 2015–Augus, (Iv), pp. 895900.
    36. 36)
      • 18. Kroger, M.: ‘Auto-studien: wenn visionen wirklichkeit warden (automotive studies: when vision becomes reality)’, 2012. Available at http://www.spiegel.de/fotostrecke/alte-konzeptautos-frueher-science-fictionheute-realitaet-fotostrecke-86469-2.html, last accessed on 6/15/19.
    37. 37)
      • 14. Barone, R.E., Giuffrè, T., Siniscalchi, S.M., et al: ‘Architecture for parking management in smart cities’, IET Intel. Transp. Syst., 2013, 8, (5), pp. 445452.
    38. 38)
      • 1. Aspelin, K., Carey, N.: ‘Establishing pedestrian walking speeds’. Portland State University, 2005, pp. 525.
    39. 39)
      • 4. Fagnant, D.J., Kockelman, K.: ‘Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations’, Transp. Res. A, Policy Pract., 2015, 77, pp. 167181.
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