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A smart market for passenger road transport (SMPRT) congestion: an application of computational mechanism design

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  • Sheri Markose

    ()

  • Amadeo Alentorn
  • Deddy Koesrindartoto
  • Peter Allen
  • Phil Blythe
  • Sergio Grosso

Abstract

To control and price negative externalities in passenger road transport, we develop an innovative and integrated computational agent based economics (ACE) model to simulate a market oriented "cap" and trade system. (i) First, there is a computational assessment of a digitized road network model of the real world congestion hot spot to determine the "cap" of the system in terms of vehicle volumes at which traffic efficiency deteriorates and the environmental externalities take off exponentially. (ii) Road users submit bids with the market clearing price at the fixed "cap" supply of travel slots in a given time slice (peak hour) being determined by an electronic sealed bid uniform price Dutch auction. (iii) Cross-sectional demand data on car users who traverse the cordon area is used to model and calibrate the heterogeneous bid submission behaviour in order to construct the inverse demand function and demand elasticities. (iv) The willingness to pay approach with heterogeneous value of time is contrasted with the generalized cost approach to pricing congestion with homogeneous value of travel time.

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Bibliographic Info

Paper provided by University of Essex, Department of Economics in its series Economics Discussion Papers with number 630.

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Date of creation: 28 Apr 2007
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Handle: RePEc:esx:essedp:630

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Postal: Discussion Papers Administrator, Department of Economics, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, U.K.
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  1. Sheri M. Markose, 2004. "Computability and Evolutionary Complexity: Markets As Complex Adaptive Systems (CAS)," Economics Discussion Papers 574, University of Essex, Department of Economics.
  2. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-81, September.
  3. Andreoni James & Miller John H., 1995. "Auctions with Artificial Adaptive Agents," Games and Economic Behavior, Elsevier, vol. 10(1), pages 39-64, July.
  4. Chris Nash & Tom Sansom, 2001. "Pricing European Transport Systems: Recent Developments and Evidence from Case Studies," Journal of Transport Economics and Policy, London School of Economics and University of Bath, vol. 35(3), pages 363-380, September.
  5. Kenneth A. Small & Clifford Winston & Jia Yan, 2005. "Uncovering the Distribution of Motorists' Preferences for Travel Time and Reliability," Econometrica, Econometric Society, vol. 73(4), pages 1367-1382, 07.
  6. Richard Schmalensee & Paul L. Joskow & A. Denny Ellerman & Juan Pablo Montero & Elizabeth M. Bailey, 1998. "An Interim Evaluation of Sulfur Dioxide Emissions Trading," Journal of Economic Perspectives, American Economic Association, vol. 12(3), pages 53-68, Summer.
  7. Alvin E. Roth, 2002. "The Economist as Engineer: Game Theory, Experimentation, and Computation as Tools for Design Economics," Econometrica, Econometric Society, vol. 70(4), pages 1341-1378, July.
  8. Brownstone, David & Ghosh, Arindam & Golob, Thomas F & Kazimi, Camilla & Van Amelsfort, Dirk, 2002. "Drivers' Willingness-to-Pay to Reduce Travel Time: Evidence from the San Diego I-15 Congestion Pricing Project," University of California Transportation Center, Working Papers qt3mx1d1k4, University of California Transportation Center.
  9. Bower, John & Bunn, Derek, 2001. "Experimental analysis of the efficiency of uniform-price versus discriminatory auctions in the England and Wales electricity market," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 561-592, March.
  10. Friedman,Daniel & Sunder,Shyam, 1994. "Experimental Methods," Cambridge Books, Cambridge University Press, number 9780521456821, October.
  11. Brownstone, David & Ghosh, Arindam & Kazimi, Camilla & Van Amelsfort, Dirk, 2002. "Drivers' Willingness-to-Pay to Reduce Travel Time: Evidence from the San Diego I-15 Congestion Pricing Project," University of California Transportation Center, Working Papers qt8q7331mz, University of California Transportation Center.
  12. Koesrindartoto, Deddy P. & Tesfatsion, Leigh, 2004. "Testing the Reliability of FERC's Wholesale Power Market Platform: An Agent-Based Computational Economics Approach," Staff General Research Papers 12326, Iowa State University, Department of Economics.
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Cited by:
  1. Markose, Sheri & Giansante, Simone & Shaghaghi, Ali Rais, 2012. "‘Too interconnected to fail’ financial network of US CDS market: Topological fragility and systemic risk," Journal of Economic Behavior & Organization, Elsevier, vol. 83(3), pages 627-646.
  2. Theodore Tsekeris & Klimis Vogiatzoglou & Stelios Bekiros, 2011. "Multi-Regional Agent-Based Modeling of Household and Firm Location Choices with Endogenous Transport Costs," ERSA conference papers ersa10p479, European Regional Science Association.
  3. Theodore Tsekeris & Klimis Vogiatzoglou, 2011. "Spatial agent-based modeling of household and firm location with endogenous transport costs," Netnomics, Springer, vol. 12(2), pages 77-98, July.

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