IDEAS home Printed from https://ideas.repec.org/p/mut/wpaper/30.html
   My bibliography  Save this paper

Quantifying the phantom jam externality: The case of an Autobahn section in Germany

Author

Listed:
  • Kathrin Goldmann

    (Institute of Transport Economics, Muenster)

  • Gernot Sieg

    (Institute of Transport Economics, Muenster)

Abstract

When traffic demand is high, traffic jams can occur in the absence of bottlenecks or of demand peaks, simply because of the interaction between vehicle drivers on the road, a phenomenon called phantom jam. The probability of phantom jams occurring increases with traffic flow. Road users only consider their own time costs and not those of other drivers, so that an unpriced phantom jam externality leads to inefficient road usage. We offer a method for quantifying the phantom jam externality, and apply the method to a specific highway section in Germany. Congestion charges calculated ignoring phantom jam externalities may be as high as 50 percent too low.

Suggested Citation

  • Kathrin Goldmann & Gernot Sieg, 2020. "Quantifying the phantom jam externality: The case of an Autobahn section in Germany," Working Papers 30, Institute of Transport Economics, University of Muenster.
    • Handle: RePEc:mut:wpaper:30
    as

    Download full text from publisher

    File URL: https://www.wiwi.uni-muenster.de/ivm/sites/ivm/files/documents/forschung/diskussionspapiere/workingpaper30.pdf
    File Function: First version, 2020
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Coifman, Benjamin, 2015. "Empirical flow-density and speed-spacing relationships: Evidence of vehicle length dependency," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 54-65.
    2. Fosgerau, Mogens & Small, Kenneth A., 2013. "Hypercongestion in downtown metropolis," Journal of Urban Economics, Elsevier, vol. 76(C), pages 122-134.
    3. Arnott, Richard, 2013. "A bathtub model of downtown traffic congestion," Journal of Urban Economics, Elsevier, vol. 76(C), pages 110-121.
    4. Arnott, Richard & Kokoza, Anatolii & Naji, Mehdi, 2016. "Equilibrium traffic dynamics in a bathtub model: A special case," Economics of Transportation, Elsevier, vol. 7, pages 38-52.
    5. Kenneth Small, 2015. "The Bottleneck Model: An Assessment and Interpretation," Working Papers 141506, University of California-Irvine, Department of Economics.
    6. Petter Arnesen & Odd A. Hjelkrem, 2018. "An Estimator for Traffic Breakdown Probability Based on Classification of Transitional Breakdown Events," Transportation Science, INFORMS, vol. 52(3), pages 593-602, June.
    7. Verhoef, Erik T., 2005. "Speed-flow relations and cost functions for congested traffic: Theory and empirical analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(7-9), pages 792-812.
    8. Stefanie Peer & Carl Koopmans & Erik T. Verhoef, 2010. "Predicting Travel Time Variability for Cost-Benefit Analysis," Tinbergen Institute Discussion Papers 10-071/3, Tinbergen Institute.
    9. Arnott, Richard & de Palma, Andre & Lindsey, Robin, 1993. "A Structural Model of Peak-Period Congestion: A Traffic Bottleneck with Elastic Demand," American Economic Review, American Economic Association, vol. 83(1), pages 161-179, March.
    10. Verhoef, Erik T., 1999. "Time, speeds, flows and densities in static models of road traffic congestion and congestion pricing," Regional Science and Urban Economics, Elsevier, vol. 29(3), pages 341-369, May.
    11. Kathrin Goldmann & Gernot Sieg, 2018. "Economic implications of phantom traffic jams: Evidence from traffic experiments," Working Papers 26, Institute of Transport Economics, University of Muenster.
    12. Small, Kenneth A., 2015. "The bottleneck model: An assessment and interpretation," Economics of Transportation, Elsevier, vol. 4(1), pages 110-117.
    13. Chung, Koohong & Rudjanakanoknad, Jittichai & Cassidy, Michael J., 2007. "Relation between traffic density and capacity drop at three freeway bottlenecks," Transportation Research Part B: Methodological, Elsevier, vol. 41(1), pages 82-95, January.
    14. Arnott, Richard & de Palma, Andre & Lindsey, Robin, 1990. "Economics of a bottleneck," Journal of Urban Economics, Elsevier, vol. 27(1), pages 111-130, January.
    15. Tu, Huizhao & Li, Hao & van Lint, Hans & van Zuylen, Henk, 2012. "Modeling travel time reliability of freeways using risk assessment techniques," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(10), pages 1528-1540.
    16. Fosgerau, Mogens, 2015. "Congestion in the bathtub," Economics of Transportation, Elsevier, vol. 4(4), pages 241-255.
    17. Ling-Ling Xiao & Hai-Jun Huang & Ronghui Liu, 2015. "Congestion Behavior and Tolls in a Bottleneck Model with Stochastic Capacity," Transportation Science, INFORMS, vol. 49(1), pages 46-65, February.
    18. Martin Schönhof & Dirk Helbing, 2007. "Empirical Features of Congested Traffic States and Their Implications for Traffic Modeling," Transportation Science, INFORMS, vol. 41(2), pages 135-166, May.
    19. Vickrey, William S, 1969. "Congestion Theory and Transport Investment," American Economic Review, American Economic Association, vol. 59(2), pages 251-260, May.
    20. Chen, Danjue & Ahn, Soyoung & Laval, Jorge & Zheng, Zuduo, 2014. "On the periodicity of traffic oscillations and capacity drop: The role of driver characteristics," Transportation Research Part B: Methodological, Elsevier, vol. 59(C), pages 117-136.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Zhi-Chun & Huang, Hai-Jun & Yang, Hai, 2020. "Fifty years of the bottleneck model: A bibliometric review and future research directions," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 311-342.
    2. Kathrin Goldmann & Gernot Sieg, 2018. "Economic implications of phantom traffic jams: Evidence from traffic experiments," Working Papers 26, Institute of Transport Economics, University of Muenster.
    3. Hall, Jonathan D., 2018. "Pareto improvements from Lexus Lanes: The effects of pricing a portion of the lanes on congested highways," Journal of Public Economics, Elsevier, vol. 158(C), pages 113-125.
    4. Amirgholy, Mahyar & Gao, H. Oliver, 2017. "Modeling the dynamics of congestion in large urban networks using the macroscopic fundamental diagram: User equilibrium, system optimum, and pricing strategies," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 215-237.
    5. Anderson, Michael L. & Davis, Lucas W., 2020. "An empirical test of hypercongestion in highway bottlenecks," Journal of Public Economics, Elsevier, vol. 187(C).
    6. Frascaria, Dario & Olver, Neil & Verhoef, Erik, 2020. "Emergent hypercongestion in Vickrey bottleneck networks," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 523-538.
    7. Hall, Jonathan D. & Savage, Ian, 2019. "Tolling roads to improve reliability," Journal of Urban Economics, Elsevier, vol. 113(C).
    8. Richard Arnott & Anatolii Kokoza & Mehdi Naji, 2016. "A Model of Rush-Hour Traffic Dynamics in an Isotropic Downtown Area," Working Papers 201612, University of California at Riverside, Department of Economics.
    9. Richard Arnott & Anatolii Kokoza & Mehdi Naji, 2015. "A Model of Rush-Hour Traffic in an Isotropic Downtown Area," Working Papers 201511, University of California at Riverside, Department of Economics.
    10. Bao, Yue & Verhoef, Erik T. & Koster, Paul, 2021. "Leaving the tub: The nature and dynamics of hypercongestion in a bathtub model with a restricted downstream exit," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    11. Arnott, Richard & Kokoza, Anatolii & Naji, Mehdi, 2016. "Equilibrium traffic dynamics in a bathtub model: A special case," Economics of Transportation, Elsevier, vol. 7, pages 38-52.
    12. Chen, Jin-Yong & Jiang, Rui & Li, Xin-Gang & Hu, Mao-Bin & Jia, Bin & Gao, Zi-You, 2019. "Morning commute problem with queue-length-dependent bottleneck capacity," Transportation Research Part B: Methodological, Elsevier, vol. 121(C), pages 184-215.
    13. Lamotte, Raphaël & Geroliminis, Nikolas, 2018. "The morning commute in urban areas with heterogeneous trip lengths," Transportation Research Part B: Methodological, Elsevier, vol. 117(PB), pages 794-810.
    14. Li, Chuanyao & Huang, Haijun, 2019. "Analysis of bathtub congestion with continuous scheduling preference," Research in Transportation Economics, Elsevier, vol. 75(C), pages 45-54.
    15. Yu, Xiaojuan & van den Berg, Vincent A.C. & Li, Zhi-Chun, 2023. "Congestion pricing and information provision under uncertainty: Responsive versus habitual pricing," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
    16. Li, Zhi-Chun & Lam, William H.K. & Wong, S.C., 2017. "Step tolling in an activity-based bottleneck model," Transportation Research Part B: Methodological, Elsevier, vol. 101(C), pages 306-334.
    17. Guo, Ren-Yong & Yang, Hai & Huang, Hai-Jun & Li, Xinwei, 2018. "Day-to-day departure time choice under bounded rationality in the bottleneck model," Transportation Research Part B: Methodological, Elsevier, vol. 117(PB), pages 832-849.
    18. Xiao, Yu & Coulombel, Nicolas & Palma, André de, 2017. "The valuation of travel time reliability: does congestion matter?," Transportation Research Part B: Methodological, Elsevier, vol. 97(C), pages 113-141.
    19. Khan, Zaid & Amin, Saurabh, 2018. "Bottleneck model with heterogeneous information," Transportation Research Part B: Methodological, Elsevier, vol. 112(C), pages 157-190.
    20. Button, Kenneth, 2004. "1. The Rationale For Road Pricing: Standard Theory And Latest Advances," Research in Transportation Economics, Elsevier, vol. 9(1), pages 3-25, January.

    More about this item

    Keywords

    hypercongestion; congestion costs; stochastic capacity; phantom jams; external costs;
    All these keywords.

    JEL classification:

    • L91 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Transportation: General
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:mut:wpaper:30. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Birgit Rueschenschmidt (email available below). General contact details of provider: https://edirc.repec.org/data/ilmuede.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.