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A Futures Market for Demand Responsive Travel Pricing

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  • Fournier, Nicholas PhD
  • Patire, Anthony PhD
  • Skabardonis, Alexander PhD

Abstract

Dynamic toll pricing based on demand can increase transportation revenue while also incentivizing travelers to avoid peak traffic periods. However, given the unpredictable nature of traffic, travelers lack the information necessary to accurately predict congestion, so dynamic pricing has minimal effect on demand. Dynamic toll pricing also poses equity concerns for those who lack other travel options. This research explores a potential remedy to these concerns by using a simple “futures market” pricing mechanism in which travelers can lock in a toll price for expected trips by prepaying for future tolls, with the future price increasing as more travelers book an overlapping time slot. This approach encourages travelers to avoid driving during the peak periods when pricing increases toward capacity or to purchase trips in advance when the price remains low or discounted, thus using infrastructure capacity more efficiently. Travelers that do not prepurchase their trip are subject to the real-time market price, which is determined by dynamic congestion pricing. This futures-market mechanism can augment existing toll collection technologies and provide travelers with sufficient pricing information and purchasing options to preplan their travel and avoid excessive prices.

Suggested Citation

  • Fournier, Nicholas PhD & Patire, Anthony PhD & Skabardonis, Alexander PhD, 2023. "A Futures Market for Demand Responsive Travel Pricing," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6g34r2mx, Institute of Transportation Studies, UC Berkeley.
    • Handle: RePEc:cdl:itsrrp:qt6g34r2mx
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    References listed on IDEAS

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    1. Henderson, J. V., 1974. "Road congestion : A reconsideration of pricing theory," Journal of Urban Economics, Elsevier, vol. 1(3), pages 346-365, July.
    2. Prateek Bansal & Rohan Shah & Stephen D. Boyles, 2018. "Robust network pricing and system optimization under combined long-term stochasticity and elasticity of travel demand," Transportation, Springer, vol. 45(5), pages 1389-1418, September.
    3. Donna, Javier D., 2018. "Measuring Long-Run Price Elasticities in Urban Travel Demand," MPRA Paper 90059, University Library of Munich, Germany.
    4. Javier D. Donna, 2021. "Measuring long‐run gasoline price elasticities in urban travel demand," RAND Journal of Economics, RAND Corporation, vol. 52(4), pages 945-994, December.
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