Impacts of the urban parking system on cruising traffic and policy development: the case of Zurich downtown area, Switzerland



Cruising-for-parking is a common phenomenon in many urban areas worldwide. Properly understanding and mitigating cruising can reduce travel times, alleviate traffic congestion, and improve the local environment. Most of the existing studies estimating cruising traffic are based on empirical data and/or detailed simulation models. Both approaches have large data requirements, and the detailed simulation models tend to have high computational costs. In this paper, we present a case study of an area within the city of Zurich, Switzerland, using a recently proposed macroscopic model to analyze the current conditions of cruising-for-parking. The results are validated with empirical data. The macroscopic model, inspired by a bottleneck model, reproduces the dynamics of both, the parking and the traffic system, as well as their interactions. As such, it calculates the delays encountered by drivers while waiting for parking, and the impact of such delays on the overall traffic stream, which involves not only the searching traffic but also the through traffic. It is shown that the macroscopic parking model could, additionally, incorporate the data generated by agent-based models, cooperatively producing valid and trustworthy results of cruising estimations, while requiring comparatively few data inputs and relatively low computational costs. The study shows that in a small area of Zurich (0.28 km2) with a demand of 2687 trips in a typical working day, cruising-for-parking generates 83 h of additional travel time and 1038 km of additional travel distance. Surprisingly, the worst conditions are observed at noon, corresponding to a maximum number of 30 searchers with an average search time of 13 min. Additionally, four types of parking policies are discussed, and their potential impacts on traffic performance are either quantitatively or qualitatively illustrated. The four policies include: the adjustment of the parking supply, the adjustment of parking time controls, the adoption of dynamic parking charges, and the provision of parking forecasts.


Cruising-for-parking Parking management and policy Traffic operation Macroscopic parking model Parking search time Number of parking searchers Probability of finding parking 



This work was partially supported by ETH Research Grant ETH-40 14-1.

Author contributions statement

JC and MM designed the research; RW provided the input data from MATSim; JC performed the research and wrote the paper in collaboration with MM; all authors reviewed the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.


  1. Anderson, S.P., de Palma, A.: The economics of pricing parking. J. Urban Econ. 55(1), 1–20 (2004)CrossRefGoogle Scholar
  2. Arnott, R., Inci, E.: An integrated model of downtown parking and traffic congestion. J. Urban Econ. 60(3), 418–442 (2006)CrossRefGoogle Scholar
  3. Arnott, R., Inci, E.: The stability of downtown parking and traffic congestion. J. Urban Econ. 68(3), 260–276 (2010)CrossRefGoogle Scholar
  4. Arnott, R., Rowse, J.: Modeling parking. J. Urban Econ. 124, 97–124 (1999)CrossRefGoogle Scholar
  5. Arnott, R., Rowse, J.: Downtown parking in auto city. Reg. Sci. Urban Econ. 39(1), 1–14 (2009)CrossRefGoogle Scholar
  6. Arnott, R., de Palma, A., Lindsey, R.: A temporal and spatial equilibrium analysis of commuter parking. J Public Econ 45(3), 301–335 (1991)CrossRefGoogle Scholar
  7. Arnott, R., de Palma, A., Lindsey, R.: A structural model of peak-period congestion: a traffic bottleneck with elastic demand. Am. Econ. Rev. 83(1), 161–179 (1993)Google Scholar
  8. Axhausen, K.W., Polak, J.W.: Choice of parking: stated preference approach. Transportation 18(1), 59–81 (1991)CrossRefGoogle Scholar
  9. Belloche, S.: On-street parking search time modelling and validation with survey-based data. Transp. Res. Procedia 6, 313–324 (2015)CrossRefGoogle Scholar
  10. Benenson, I., Martens, K., Birfir, S.: PARKAGENT: an agent-based model of parking in the city. Comput. Environ. Urban Syst. 32(6), 431–439 (2008)CrossRefGoogle Scholar
  11. Bodenbender, A.: A CGE model of parking in Zürich: Implementation and policy test (MSc thesis). IVT, ETH Zürich, Zürich (2013)Google Scholar
  12. Boyles, S.D., Tang, S., Unnikrishnan, A.: Parking search equilibrium on a network. Transp. Res. Part B Methodol. 81, 390–409 (2015)CrossRefGoogle Scholar
  13. Cao, J.: Effects of parking on urban traffic performance. Doctoral Dissertation, Swiss Federal Institute of Technology (ETH Zürich), Zurich, No. 23527 (2016)Google Scholar
  14. Cao, J., Menendez, M.: Methodology to evaluate cost and accuracy of parking patrol surveys. Transp. Res. Rec. J. Transp. Res. Board 2359, 1–9 (2013)CrossRefGoogle Scholar
  15. Cao, J., Menendez, M.: Generalized effects of on-street parking maneuvers on the performance of nearby signalized intersections. Transp. Res. Rec. J. Transp. Res. Board 2483, 30–38 (2015a)CrossRefGoogle Scholar
  16. Cao, J., Menendez, M.: System dynamics of urban traffic based on its parking-related-states. Transp. Res. Part B Methodol. 81, 718–736 (2015b)CrossRefGoogle Scholar
  17. Cao, J., Menendez, M., Nikias, V.: The effects of on-street parking on the service rate of nearby intersections. J. Adv. Transp. 50(4), 406–420 (2016)CrossRefGoogle Scholar
  18. Cassidy, M.J., Bertini, R.L.: Some traffic features at freeway bottlenecks. Transp. Res. B Methodol. 33B, 25–42 (1999)CrossRefGoogle Scholar
  19. Gallo, M., D’Acierno, L., Montella, B.: A multilayer model to simulate cruising for parking in urban areas. Transp. Policy 18(5), 735–744 (2011)CrossRefGoogle Scholar
  20. Ge, Q., Menendez, M.: An efficient sensitivity analysis approach for computationally expensive microscopic traffic simulation models. Int. J. Transp. 2(2), 49–64 (2014)CrossRefGoogle Scholar
  21. Ge, Q., Menendez, M.: Extending Morris method for qualitative global sensitivity analysis of models with dependent inputs. Reliab. Eng. Syst. Saf. 162(2017), 28–39 (2017)CrossRefGoogle Scholar
  22. Ge, Q., Ciuffo, B., Menendez, M.: Combining screening and metamodel-based methods: an efficient sequential approach for the sensitivity analysis of model outputs. Reliab. Eng. Syst. Saf. 134(2015), 334–344 (2015)CrossRefGoogle Scholar
  23. Glazer, A., Niskanen, E.: Parking fees and congestion. Reg. Sci. Urban Econ. 22(1), 123–132 (1992)CrossRefGoogle Scholar
  24. Haddad, J., Geroliminis, N.: On the stability of traffic perimeter control in two-region urban cities. Transp. Res. Part B Methodol. 46(9), 1159–1176 (2012)CrossRefGoogle Scholar
  25. Haddad, J., Ramezani, M., Geroliminis, N.: Cooperative traffic control of a mixed network with two urban regions and a freeway. Transp. Res. Part B Methodol. 54, 17–36 (2013)CrossRefGoogle Scholar
  26. Horni, A., Montini, L., Waraich, R.A., Axhausen, K.W.: An agent-based cellular automaton cruising-for-parking simulation. Transp. Lett. 5(4), 167–175 (2013)CrossRefGoogle Scholar
  27. Leurent, F., Boujnah, H.: A user equilibrium, traffic assignment model of network route and parking lot choice, with search circuits and cruising flows. Transp. Res. Part C Emerg. Technol. 47, 28–46 (2014)CrossRefGoogle Scholar
  28. Levy, N., Martens, K., Benenson, I.: Exploring cruising using agent-based and analytical models of parking. Transp. A Transp. Sci. 9(9), 773–797 (2013)Google Scholar
  29. Litman, T.: Parking taxes: evaluating options and impacts. In: Transportation, vol. 19 (2010)Google Scholar
  30. Loder, A., Ambühl, L., Menendez, M., Axhausen, K.W.: Empirics of multi-modal traffic networks: using the 3D macroscopic fundamental diagram. Transp. Res. Part C Emerg. Technol. 82, 88–101 (2017)CrossRefGoogle Scholar
  31. Montini, L., Horni, A., Rieser-Schussler, N.: Searching for parking in GPS data. Eidgenssische Technische Hochschule Zurich, IVT, Institute for Transport Planning and Systems (2012)Google Scholar
  32. Ortigosa, J., Menendez, M., Tapia, H.: Study on the number and location of measurement points for an MFD perimeter control scheme: a case study of Zurich. EURO J. Transp. Logist. 3(3–4), 245–266 (2014)CrossRefGoogle Scholar
  33. Parkleitsystem Stadt Zürich (2016). Accessed May 2016
  34. Pierce, G., Shoup, D.: SFpark: parking by demand. Access 43(Fall), 20–28 (2013)Google Scholar
  35. Qian, Z.S., Xiao, F.E., Zhang, H.M.: Managing morning commute traffic with parking. Transp. Res. Part B Methodol. 46(7), 894–916 (2012)CrossRefGoogle Scholar
  36. Shoup, D.C.: The High Cost of Free Parking. Planners Press, American Planning Association, Chicago (2005)Google Scholar
  37. Shoup, D.C.: Cruising for parking. Transp. Policy 13(6), 479–486 (2006)CrossRefGoogle Scholar
  38. Stadt-Zurich (2016). Accessed May 2016
  39. Timeanddata (2016). Accessed May 2016
  40. Van Ommeren, J.N., Wentink, D., Rietveld, P.: Empirical evidence on cruising for parking. Transp. Res. Part A Policy Pract. 46(1), 123–130 (2012)CrossRefGoogle Scholar
  41. Vickrey, W.S.: Congestion theory and transport investment. Am. Econ. Rev. (Pap. Proc.) 59(2), 251–261 (1969)Google Scholar
  42. Waraich, R.A., Axhausen, K.W.: Agent-based parking choice model. Transp. Res. Rec. J. Transp. Res. Board 2319(1), 39–46 (2012)CrossRefGoogle Scholar
  43. Waraich, R.A., Dobler, C., Axhausen, K.W.: (2012) Modelling parking search behaviour with an agent-based approach. In: 13th International Conference on Travel Behaviour Research, pp. 1–12Google Scholar
  44. Waraich, R.A., Dobler, C., Weis, C., Axhausen, K.W.: (2013). Optimizing parking prices using agent-based approach. In: 92nd Annual Meeting of the Transportation Research BoardGoogle Scholar
  45. Washbrook, K., Haider, W., Jaccard, M.: Estimating commuter mode choice: a discrete choice analysis of the impact of road pricing and parking charges. Transportation 33(6), 621–639 (2006)CrossRefGoogle Scholar
  46. Weinberger, R., Kaehny, J., Rufo, M.: U.S. Parking Policies: An Overview of Management Strategies Institute for Transportation and Development Policy. Institution for Transportation and Development Policy, New York (2012)Google Scholar
  47. Weis, C., Vrtic, M., Widmer, P., Axhausen, K.W.: (2012). Influence of parking on location and mode choice: a stated choice survey. In: Vorgetragen bei 91st Annual Meeting of the Transportation Research Board, Washington, DCGoogle Scholar
  48. Yang, H., Liu, W., Wang, X., Zhang, X.: On the morning commute problem with bottleneck congestion and parking space constraints. Transp. Res. Part B 58(4), 106–118 (2013)CrossRefGoogle Scholar
  49. Yang, K., Zheng, N., Menendez, M.: Multi-scale perimeter control approach in a connected-vehicle environment. Transp. Res. Part C Emerg. Technol. (2017). Google Scholar
  50. Zhang, X.N., Yang, H., Huang, H.J.: Improving travel efficiency by parking permits distribution and trading. Transp. Res. Part B 45(7), 1018–1034 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Institute for Transport Planning and Systems (IVT)ETH ZurichZurichSwitzerland
  2. 2.Institute for Transport Planning and Systems (IVT)ETH ZurichZurichSwitzerland
  3. 3.Lawrence Berkeley National LaboratoryBerkeleyUSA

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