Skip to main content
SpringerLink
Log in

Multi-objective cordon price design to control long run adverse traffic effects in large urban areas

  • Published:
NETNOMICS: Economic Research and Electronic Networking Aims and scope Submit manuscript

Abstract

Pricing is seen as a viable alternative to manage the demand for transportation facilities. While supply increase might aggravate the problem, pricing is envisaged to relieve large cities from adverse traffic effects (congestion and pollution, among others). Nevertheless, pricing has its own drawbacks, often overlooked by the operators of the networks. It will cause changes in the travel behavior of the different groups and their demands (shoppers, retailers, and even basic businesses/ employees). This paper presents an extensive review of the subject, and an equilibrium model to estimate the long–run effects of a cordon pricing scheme. The problem of designing a price for a Central Business District (CBD) cordon is formulated in this study as a bi-level optimization problem. The lower level problem is a joint trip production-distribution-mode choice-assignment problem, with interactions among three groups of the users (agents). The upper level problem is a multi-objective decision-making problem, where CBD cordon price (as decision variable) forms the alternatives. It monitors four important objectives, namely maximization of consumers’ surplus, minimization of air pollution and congestion measures, as well as minimization of the internal migration of the retail employment. The latter is a main cause of the CBD degradation. The demands for shoppers and retailers are elastic, but that of the basic employees is assumed inelastic. Two test problems are examined and solved to show the behavior of the model. The results show that by employing under-pricing, or over-pricing, the cordon would lead to an unfavorable situation in that it would decrease the consumers’ surplus, and increase pollution and congestion levels. Moreover, at certain price levels the rate of migration would drastically increase, while it might be relatively insensitive to price in other ranges.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Aigner, D.J., & Hirschberg, J.G. (1985). Commercial / industrial customer response to time-of-use electricity prices: some experimental results. Road Journal of Economics, 16, 341–355.

    Google Scholar 

  2. Alonso, W. (1960). A theory of the urban land market. Papers and Proceedings of the Regional Science Association.

  3. Anas, A. (1982). Residential location markets and urban transportation: economic theory, econometrics, and policy analysis with discrete choice models. New York: Academic Press.

    Google Scholar 

  4. Anas, A. (1984). Discrete choice theory and the general equilibrium of employment, housing and travel networks in a Lowry-type model of the urban economy. Environment and Planning A, 16(11), 1489–1502.

    Article  Google Scholar 

  5. Anas, A., & Rhee, H.-J. (2006). Curbing excess sprawl with congestion tolls and urban boundaries. Regional Science and Urban Economics, 36(4), 510–541. doi:10.1016/j.regsciurbeco.2006.03.003.

    Article  Google Scholar 

  6. Anas, A., & Xu, R. (1999). Congestion, land use, and job dispersion: a general equilibrium model. Journal of Urban Economics, 45(3), 451–473. doi:10.1006/juec.1998.2104.

    Article  Google Scholar 

  7. Arentze, T., & Timmermans, H. (2007). Congestion pricing scenarios and change of job or residential location: results of a stated adaptation experiment. Journal of Transport Geography, 15(1), 56–61. doi:10.1016/j.jtrangeo.2006.02.013.

    Article  Google Scholar 

  8. Arnott, R. (1998). Congestion tolling and urban spatial structure. Journal of Regional Science, 38(3), 495–504.

    Article  Google Scholar 

  9. Arnott, R., de Palma, A., & Lindsey, R. (1993). A structural model of peak-period congestion: a traffic bottleneck with elastic demand. The American Economic Review, 83(1), 161–179.

    Google Scholar 

  10. Blackledge, D. (2008). Pricing in urban areas: the CURACAO project. TRB summer conference, Baltimore, June 2008. www.curacaoproject.eu.

  11. Boyce, D.E. (1980). A framework for constructing network equilibrium models of urban location. Transportation Science, 14(1), 77–96.

    Article  Google Scholar 

  12. Boyce, D.E., LeBlanc, L.J., & Chon, K.S. (1988). Network equilibrium models of urban location and travel choices: a retrospective survey. Journal of Regional Science, 28(2), 159–183.

    Article  Google Scholar 

  13. Braeutigam, R.R. (1989). Optimal policies for natural monopolies. In R.L. Schmalensee, & R.D. Willig (Eds.) Handbook of industrial organization (pp. 1289–1346). Amsterdam: North Holland.

    Google Scholar 

  14. Button, K.J., & Hensher, D.A. (2001). Handbook of transport systems and traffic control. Amsterdam.

  15. Chang, J.S., & Mackett, R.L. (2006). A bi-level model of the relationship between transport and residential location. Transportation research-Part B, 40, 123–146.

    Article  Google Scholar 

  16. Couclelis, H. (1985). Cellular Worlds: a framework for modeling micro-macro dynamics. Environment and Planning A, 17, 585–596.

    Article  Google Scholar 

  17. Couclelis, H. (1997). From Cellular Automata to urban models: new principles for model development and implementation. Environment and planning, B, 24, 165–174.

    Article  Google Scholar 

  18. Cracknell, J.A. (2000). Experience in urban traffic management and demand management in developing countries. Final Report, World Bank, Washington.

  19. De Borger, B., & Van Dender, K. (2003). Transport tax reform, commuting, and endogenous values of time. Journal of Urban Economics, 53(3), 510–530. doi:10.1016/S0094-1190(03)00024-X.

    Article  Google Scholar 

  20. De Lara, M, de Palme, A., Kilani, M., & Piperno, S. (2013). Congestion pricing and long term urban form: application to Paris region. Regional Science and Urban Economics, 43(2), 282–295.

    Article  Google Scholar 

  21. de Palma, A., Kilani, M., & Lindsey, R. (2005). Congestion pricing on a road network: a study using the dynamic equilibrium simulator METROPOLIS. Transportation Research Part A, 39, 588–611.

    Google Scholar 

  22. de Palma, A., & Lindsey, R. (2011). Traffic congestion pricing methodology and technologies. Overview Paper, Transportation Research-Part C, 19(6), 1377–1399. doi:10.1016/trc.2011.02.010.

    Article  Google Scholar 

  23. ECMT (2007). Managing urban traffic congestion (Summary Document). Transportation Research Center, OECD, ISBN 978-92 821-0128-5.

  24. Eliasson, J. (2008). Lessons from the Stockholm congestion charging trial - The views of a transport economist. Centre for transport studies, royal institute of technology, WSP analysis and strategy. http://viandorica2008.vegagerdin.is. Accessed March 2013.

  25. Eliasson, J., Hultkrantz, L., Nerhagen, L., & Rosqvist, L.S. (2009). The Stockholm congestion – charging trial 2006: overview of effects. Transportation Research Part A, 43(3), 240–250.

    Google Scholar 

  26. Engelen, G., White, R., Uljee, I., & Drazan, P. (1995). Using cellular automata for integrated modeling of socio-environmental systems. Environmental Monitoring and Assessment, 34, 203–214.

    Article  Google Scholar 

  27. EPA (2008). Emission facts: average annual emissions and fuel consumption for gasoline-fueled passenger cars and light trucks. Office of Transportation and Air Quality, EPA420-F-08-024.

  28. Expert Group Summary (Accessed Dec. 2014). http://www.stockholmsforsoket.se. (Expert Group includes: Algers, S., Freij, K. B., Eliasson,J., Henricksson, C., Hultkrantz, L., Ljungberg, C., Nerhagen, L., & Rosqvist, L. S.)

  29. FHWA (2010). Vehicle registrations, fuel consumption, and vehicle miles of travel as indices. Office of highway policy information, highway statistics series, (USDOT).

  30. Florian, M., & Spiess, H. (1982). The convergence of diagonalization allgorithms for asymmetric network equilibrium problem. Transportation Research-Part B, 16(6), 477–483.

    Article  Google Scholar 

  31. Frey, B.S. (2003). Why are efficient transport policy instruments so seldom used? In J. Schade & B. Schlag (Eds.), Acceptability of transport pricing strategies (pp. 63–75). Oxford: Elsevier.

    Google Scholar 

  32. Ødeck, J., & Brathen, S. (2002). Toll financing in Norway: the success, the failures and perspectives for the future. Transport Policy, 9, 253–260.

    Article  Google Scholar 

  33. Garin, R. (1966). A matrix formulation of the Lowry model for intra-metropolitan activity allocation. Journal of the American Institute of Planners, 32, 361–364.

    Article  Google Scholar 

  34. Gaunt, M., Rye, T., & Allen, S. (2007). Public acceptability of road user charging: the case of Edinburgh and the 2005 Referendum. Transportation Reviews, 27(1), 85–102.

    Article  Google Scholar 

  35. A. Gullberg, & K. Isaksson (Eds.) (2009). Congestion taxes in city traffic: lessons learnt from the stockholm trial. Lund: Nordic Academic Press. ISBN 978-9185509232.

    Google Scholar 

  36. Goodwin, P. (2004). Congestion charging in Central London: lessons learned. Planning Theory & Practice, 5(4), 501–505. doi:10.1080/1464935042000293242.

    Article  Google Scholar 

  37. Hårsman, B., & Quigley, J.M. (2011). Political and public acceptability of congestion pricing: ideology and self interest in Sweden. ACCESS #38, UCTC.

  38. Hecker, J.E.Z. (2003). Reducing congestion: congestion pricing has promise for improving use of transportation infrastructure. US General Accounting Office, GAO-03-735T.

  39. Hwang, C.L., & Yoon, K. (1981). Multiple attribute decision making: methods and applications, a state-of-the-art survey. Berlin: Springer.

    Book  Google Scholar 

  40. Iacano, M., Levinson, D., & El-Geneidy, A. (2008). Modes of transportation and land use change: a guide to the territory. Journal of Planning Literature, 22, 323–340.

    Article  Google Scholar 

  41. Ieromonachou, P., Potter, S., & Warren, J.P. (2006). Norway’s urban toll rings: evolving towards congestion charging Transport Policy, 13(5), 367–378.

    Article  Google Scholar 

  42. Jackson, D.Z. (2012). Finding the cure for traffic. The Boston Globe, http://www.bostonglobe.com/opinionAccessedMarch2013.

  43. Johnston, R., Lund, J.R., & Craig, P.P. (1995). Capacity-allocation methods for reducing urban traffic congestion. Journal of Transportation Engineering, ASCE, 121(1), 27–39.

    Article  Google Scholar 

  44. Johnston, R.A., Shabazian, D.R., & Gao, S. (2003). UPlan: a versatile urban growth model for transportation planning. Transportation Research Record, 1831, 202–209.

    Article  Google Scholar 

  45. Jones, P., & Hervik, A. (1992). Restraining car traffic in European cities: An emerging role for road pricing. Transportation Research Part A, 26(2), 133–145.

    Google Scholar 

  46. Kain, J. (1972). How to improve transportation at practically no cost. Public Policy, 20, 335–352.

    Google Scholar 

  47. Karlge, F. (2011). Congestion charge: one ingredient to double modal split in greater Gothenburg. International Association of Public Transport (UITP), Brussels.

  48. Kii, M., & Doi, K. (2005). Multi-agent land-use and transport model for the policy evaluation of a compact city. Environment and Planning B, 32(4), 485–504.

    Article  Google Scholar 

  49. Knight, F. (1924). Some fallacies in the interpretation of social cost. Quarterly Journal of Economics, 38(4), 582–606. doi:10.2307/1884592.

    Article  Google Scholar 

  50. Kristoffersson, I., & Engelson, L. (2009). A dynamic transportation model for the Stockholm area: implementation issues regarding departure time choice and OD-pair reduction. Network Spatial Economics, 9, 551–573. doi:10.1007/s11067-009-9104-0.

    Article  Google Scholar 

  51. Larson, O.I. (2001). Implementing congestion pricing. Working Paper, Molde University College.

  52. Larson, R.C., & Sasanuma, K. (2010). Urban vehicle congestion pricing: a review. Journal of Industrial and Systems Engineering, 3(4), 227–242.

    Google Scholar 

  53. LeBlanc, L.J. (1975). An algorithm for the discrete network design problem. Transportation Science, 9, 183–199.

    Article  Google Scholar 

  54. Lerman, S.R. (1976). Location, housing, automobile ownership and mode to work: a joint choice model. Transportation Research Record, 610, 6–11.

    Google Scholar 

  55. Lian, J.I. (2008). The Oslo and Bergen toll rings and road-building investment- effect on traffic development and congestion. Journal of Transport Geography, 16(3), 174–181. doi:10.1016/j.jtrangeo.2007.08.004.

    Article  Google Scholar 

  56. Lowry, I.S. (1964). A model of Metropolis. Santa Monica: Rand Corporation.

    Google Scholar 

  57. Marcotte, P., & Wynter, L. (2004). A new look at the multiclass network equilibrium problem. Transportation Science, 38(3), 282–292.

    Article  Google Scholar 

  58. Maruyama, T., & Sumalee, A. (2007). Efficiency and equity comparisons of cordon- and area-based road pricing schemes using a trip-chain equilibrium mode. Transportation Research Part A, 41, 655–671.

    Google Scholar 

  59. May, A.D. (1992). Road Pricing: an international perspective. Transportation, 19(4).

  60. McFadden, D.L. (1978). Modelling the choice of residential location. In A. Karlqvist (Ed.), Spatial interaction theory and planning models (pp. 75–96). Amsterdam: North-Holland.

    Google Scholar 

  61. Meland, S., Tretvik, T., & Welde, M. (2010). The effects of removing the Trondheim toll cordon. Transport Policy, 17, 457–485.

    Article  Google Scholar 

  62. Numrich, J., Ruja, S., & Voss, S. (2012). Global navigation satellite system based tolling: state-of-the-art. Netnomics, 13, 93–123.

    Article  Google Scholar 

  63. Olszewski, P., & Xie, L. (2005). Modeling the effects of road pricing on traffic in Singapore. Transportation Research Part A, 39(7-9), 755–772.

    Google Scholar 

  64. Otter, H.S., van der Veen, A., & de Vriend, H.J. (2001). ABLOoM: location behavior, spatial patterns and agent-based modeling. Journal of Artificial Societies and Social Simulation, 4(4). http://jasss.soc.surrey.ac.uk/4/4/2.html.

  65. Parry, I.W.H., & Bento, A. (2001). Revenue recycling and the welfare effects of road pricing. The Scandinavian Journal of Economics, 103(4), 645–671. doi:10.1111/1467-9442.00264.

    Article  Google Scholar 

  66. Pigou, A.C. (1920). The economics of welfare. London: Macmillan.

    Google Scholar 

  67. Poorzahedy, H., & Rezaei, A. (2013). Peer evaluation of multi-attribute analysis techniques: case of a light rail transit network choice. Scientia Iranica, 20(3), 371–386.

    Google Scholar 

  68. Poorzahedy, H., & Rouhani, O.M. (2007). Hybrid meta-heuristic algorithms for solving network design problem. European Journal of Operational Research, 182, 578–596.

    Article  Google Scholar 

  69. Poorzahedy, H., & Safari, F. (2011). An ant system application to the bus network design problem: an algorithm and a case study. Public Transport, 3(2), 165–187. doi:10.1007/s12469-011-0046-9.

    Article  Google Scholar 

  70. Quddus, M.A., Bell, M.G.H., Schmöcker, J.-D., & Fonzone, A. (2007). The impact of the congestion charge on the retail business in London: an econometric analysis. Transport Policy, 14(5), 433–444.

    Article  Google Scholar 

  71. Richardson, T., Isaksson, K., & Gullberg, A. (2008). Changing frames of mobility: the Stockholm congestion tax, Trafikdage på Aalborg Universitet, Denmark. ISSN 1603-9696.

  72. Safirova, E., Houde, S., Lipman, D.A., Harrington, W., & Baglino, A. (2006). Congestion pricing: long-term economic and land-use effects. Resources for the Future. RFF DP 06-37.

  73. Schade, J., & Schlag, B. (2003a). Acceptability of urban transport pricing strategies. Transport Research Part F, 6(1), 45–61.

    Article  Google Scholar 

  74. Schade, J., & Schlag, B. (2003b). Acceptability of pricing reform, IMPRINT-EUROPE Seminar, May 13 and 14.

  75. Schlag, B., & Schade, J. (2000). Public acceptability of traffic demand management in Europe. Traffic Engineering and Control, 41(8), 314–318.

    Google Scholar 

  76. Sheffi, Y. (1985). Urban transportation network: equilibrium analysis with mathematical programming methods. New York: Prentice-Hall.

    Google Scholar 

  77. Small, K.A., & Gomez-Ibañez, J.A. (1998). Road pricing for congestion management: the transition from theory to policy. In K.J. Button, & E.T. Verhoef (Eds.), Road pricing, traffic congestion and the environment: issues of efficiency and social feasibility (pp. 213–246). Cheltenham: Edward Elgar.

    Google Scholar 

  78. Steininger, K.W., Friedl, B., & Gebetsroither, B. (2007). Sustainability impacts of car road pricing: a computable general equilibrium analysis for Austria. Ecological Economics, 63(1), 59–69.

    Article  Google Scholar 

  79. Stewart, K. (2007). Tolling traffic links under stochastic assignment: modeling the relationship between the number and price level of tolled links and optimal traffic flows. Transportation Research Part A, 41, 644–654.

    Google Scholar 

  80. Tehran Traffic Organization (1996). A study on the evolution of Tehran traffic Cordon plan, The Municipality of Tehran, Tehran.

  81. TCTTS: Tehran Comprehensive Transportation and Traffic Studies Co. (1996). Estimated fuel consumption and air pollutant emission models in Tehran Transportation and Traffic model. Final Report 130-3, Tehran. (In Farsi.)

  82. The Federal Office of Environmental Protection, Berne (1986). Emmissions polluantes du trafic routier prive’ de 1950 A’ 2000. Les Cahiers de l’environnement Publie’ Par, l’Office Fédéral de la Protection de I’Environnement, Berne.

  83. Tillema, T., Wee, B., & Ettema, D. (2010). The influence of (toll-related) travel costs in residential location decisions of households: a stated choice approach. Transportation Research Part A, 44, 785–796.

    Google Scholar 

  84. Tsekeris, T., & Vogiatzoglou, K. (2011). Spatial agent-based modeling of household and firm location with endogenous transport costs. Netnomics, 12, 77–98.

    Article  Google Scholar 

  85. Tsekeris, T., Vogiatzoglou, K., & Bekiros, S. (2011). Multi-regional agent-based modeling of household and firm location choices with endogenous transport costs. European Regional Science Association, ERSA Conference Papers, number ERSA2010, 479.

  86. Tsekeris, T., & Voß, S. (2009). Design and evaluation of road pricing: state-of-the-art and methodological advances. Netnomics, 10, 5–52. doi:10.1007/s11066-008-9024-z.

    Article  Google Scholar 

  87. Vahnberg, R., & Blomqvist, L. (2012). Gothenburg: political will behind congestion charging and measures to encourage the use of public transport. Public Transport International UITP, 61(1), 16–18.

    Google Scholar 

  88. Verhoef, E. (2005). Second-best congestion pricing schemes in the monocentric city. Journal of Urban Economics, 58, 367–388.

    Article  Google Scholar 

  89. Verhoef, E.T., Emmerink, R.H.M., Nijkamp, P., & Rietveld, P. (1996). Information provision, flat and fine congestion tolling and the efficiency of road usage. Regional Science and Urban Economics, 26(5), 465–559. doi:10.1016/0166-0462(96)02130-8.

    Article  Google Scholar 

  90. Vickrey, W. (1963). Pricing in urban and suburban transport. American Economic Review, 52(20), 452–465.

    Google Scholar 

  91. Vickrey, W. (1969). Congestion theory and transport investment. American Economic Review (Papers and Proceedings), 59, 251–61.

    Google Scholar 

  92. Vold, A. (2006). Phased implementation of transport pricing for greater Oslo. Transport Policy, 13(2), 140–148. doi:10.1016/j.tranpol.2005.11.009.

    Article  Google Scholar 

  93. Waddell, P.A. (2002). UrbanSim, modeling urban development for land use, transportation and environmental planning. Journal of the American Planning Association, 68, 297–314.

    Article  Google Scholar 

  94. Waddell, P.A., Borning, A., Noth, M., Freier, N., Becke, M., & Ulfarsson, G. (2003). Microsimulation of urban development and location choices: design and implementation of UrbanSim. Networks and Spatial Economics, 3(1), 43–67.

    Article  Google Scholar 

  95. Walker, J. (2011). The acceptability of pricing. The Royal Automotive Club Foundation, Ltd., London.

  96. Weisbrod, G., Vary, D., & Treyz, G. (2003). Measuring economic costs of urban traffic congestion to business. Transportation Research Record, 1839, 98–106. doi:10.3141/1839-0.

    Article  Google Scholar 

  97. Wessel, P., & Persson, L. (2011). With a full perspective on co-modal travel services, route by route. In Proceedings of the 18th ITS world congress. 16 to 20 October. Orlando.

  98. Whitehead, T. (2002). Road user charging and business performance: identifying the processes of economic change. Transport Policy, 9(3), 221–240.

    Article  Google Scholar 

  99. Whitehead, T., Preston, J., & Holvad, T. (2005). The whole-life impacts of transport-charging interventions on business performance: a time-marching framework. Environment and Planning A, 37(5), 877–894.

    Article  Google Scholar 

  100. Wilson, A.G. (1967). A statistical theory of sptial distribution models. Transportation Research, 1, 253–269.

    Article  Google Scholar 

  101. Wilson, A.G. (1970). Entropy in urban and regional modeling. London: Pion Ltd.

    Google Scholar 

  102. Yang, H., Xu, W., He, B., & Meng, Q. (2010). Road pricing for congestion control with unknown demand and cost functions. Transportation Research Part C, 18(2), 157–175.

    Article  Google Scholar 

  103. Yang, H., & Zhang, X. (2002). Multiclass network toll design problem with social and spatial equity constraints. Journal of Transportation Engineering, 128(5), 420–428.

    Article  Google Scholar 

  104. Yildirim, M.B., & Hearn, D.W. (2005). A first-best toll pricing framework for variable demand traffic assignment problems. Transportation Research Part B, 39(8), 659–678. doi:10.1016/j.trb.2004.08.001.

    Article  Google Scholar 

  105. Zhang, X., & Yang, H. (2004). The optimal cordon-based network congestion pricing problem. Transportation Research Part B, 38(6), 517–537. doi:10.1016/j.trb.2003.08.001.

    Article  Google Scholar 

  106. Zhang, X., Yang, H., & Huang, H.-J. (2008). Multiclass multicriteria mixed equilibrium on networks and uniform link tolls for system optimum. European Journal of Operational Research, 189, 146–158.

    Article  Google Scholar 

  107. Zhou, X., Mahmassani, H., & Zhang, K. (2008). Dynamic micro-assignment modeling approach for integrated multimodal urban corridor management. Transportation Research Part C, 16, 167–186.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Sharif University of Technology, Tehran, Iran

    Mahyar Amirgholy & Hojjat Rezaeestakhruie

  2. Department of Civil Engineering, Institute for Transportation Studies and Research, Sharif University of Technology, Tehran, Iran

    Hossain Poorzahedy

Authors
  1. Mahyar Amirgholy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hojjat Rezaeestakhruie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hossain Poorzahedy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hossain Poorzahedy.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Amirgholy, M., Rezaeestakhruie, H. & Poorzahedy, H. Multi-objective cordon price design to control long run adverse traffic effects in large urban areas. Netnomics 16, 1–52 (2015). https://doi.org/10.1007/s11066-015-9092-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11066-015-9092-9

Keywords

Search

Navigation