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Forecasting effects of congestion charges

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    This paper performs an ex-post evaluation of the transport model forecast of the effects of the Gothenburg congestion charges, implemented in 2013. We find that the predicted traffic reductions across the cordon and travel time gains were close to those observed in the peak. However, the reduction in traffic across the cordon was under-predicted in off-peak. The design of the charging system implies that the path disutility cannot be computed as a sum of link attributes. The route choice model is therefore implemented as a hierarchical algorithm, including a continuous value of travel time (VTT) distribution. The VTT distribution was estimated from stated choice (SC) data, but had to be adjusted to be consistent with observed outcome. One reason for the discrepancy may be that VTT inferred from SC data does not reveal travellers’ long-term preferences. Another reason may be that apart from distance, travel time and charge there are other factors that determine drivers’ route choice.

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    File URL: http://www.transportportal.se/swopec/CTS2016-9.pdf
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    Paper provided by CTS - Centre for Transport Studies Stockholm (KTH and VTI) in its series Working papers in Transport Economics with number 2016:9.

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    Length: 18 pages
    Date of creation: 29 Mar 2016
    Handle: RePEc:hhs:ctswps:2016_009
    Contact details of provider: Postal:
    Centrum för Transportstudier (CTS), Teknikringen 10, 100 44 Stockholm, Sweden

    Web page: http://www.cts.kth.se/

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    1. Fosgerau, Mogens & Bierlaire, Michel, 2007. "A practical test for the choice of mixing distribution in discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 41(7), pages 784-794, August.
    2. David Hartgen, 2013. "Hubris or humility? Accuracy issues for the next 50 years of travel demand modeling," Transportation, Springer, vol. 40(6), pages 1133-1157, November.
    3. Jonathan Leape, 2006. "The London Congestion Charge," Journal of Economic Perspectives, American Economic Association, vol. 20(4), pages 157-176, Fall.
    4. Kottenhoff, Karl & Brundell Freij, Karin, 2009. "The role of public transport for feasibility and acceptability of congestion charging - The case of Stockholm," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(3), pages 297-305, March.
    5. De Borger, Bruno & Fosgerau, Mogens, 2008. "The trade-off between money and travel time: A test of the theory of reference-dependent preferences," Journal of Urban Economics, Elsevier, vol. 64(1), pages 101-115, July.
    6. Börjesson, Maria & Eliasson, Jonas, 2014. "Experiences from the Swedish Value of Time study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 59(C), pages 144-158.
    7. Robert Bain, 2009. "Error and optimism bias in toll road traffic forecasts," Transportation, Springer, vol. 36(5), pages 469-482, September.
    8. Welde, Morten, 2011. "Demand and operating cost forecasting accuracy for toll road projects," Transport Policy, Elsevier, vol. 18(5), pages 765-771, September.
    9. Flyvbjerg, Bent, 2005. "Measuring inaccuracy in travel demand forecasting: methodological considerations regarding ramp up and sampling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(6), pages 522-530, July.
    10. Börjesson, Maria & Kristoffersson, Ida, 2015. "The Gothenburg congestion charge. Effects, design and politics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 75(C), pages 134-146.
    11. Leurent, Fabien, 1993. "Cost versus time equilibrium over a network," European Journal of Operational Research, Elsevier, vol. 71(2), pages 205-221, December.
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