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Forecasting effects of congestion charges

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Abstract

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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  • West , Jens & Börjesson , Maria & Engelson , Leonid, 2016. "Forecasting effects of congestion charges," Working papers in Transport Economics 2016:9, CTS - Centre for Transport Studies Stockholm (KTH and VTI).
    • Handle: RePEc:hhs:ctswps:2016_009
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    File URL: http://www.transportportal.se/swopec/CTS2016-9.pdf
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    References listed on IDEAS

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    1. Jonathan Leape, 2006. "The London Congestion Charge," Journal of Economic Perspectives, American Economic Association, vol. 20(4), pages 157-176, Fall.
    2. 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.
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    6. 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.
    7. Robert Bain, 2009. "Error and optimism bias in toll road traffic forecasts," Transportation, Springer, vol. 36(5), pages 469-482, September.
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    9. 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.
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    More about this item

    Keywords

    Congestion charges; Transport model; Validation; Value of time; Volume delay function; Decision support;

    JEL classification:

    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • R42 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government and Private Investment Analysis; Road Maintenance; Transportation Planning
    • R48 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government Pricing and Policy

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