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Will New Driving Technologies Change the Value of Public Transportation Investments?

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Listed:
  • Imke Reimers
  • Benjamin Reed Shiller
  • Benjamin R. Shiller

Abstract

We analyze how self-driving vehicles (SDVs) influence commuter behavior and returns to long-lived public transit investments. Using a commuting mode model estimated on detailed home and work location data from Greater Boston, we simulate the widespread entry of SDVs, which offer passive travel similar to transit but use existing road networks. We find that SDVs increase vehicle miles by 40% while decreasing public transit use by about 10%. Transit improvements continue to moderately boost revenues and lower miles driven, but their effects on mileage are small compared to SDVs. These findings highlight planning challenges posed by the emergence of SDVs.

Suggested Citation

  • Imke Reimers & Benjamin Reed Shiller & Benjamin R. Shiller, 2025. "Will New Driving Technologies Change the Value of Public Transportation Investments?," CESifo Working Paper Series 11956, CESifo.
  • Handle: RePEc:ces:ceswps:_11956
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    References listed on IDEAS

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    1. Nicholas Buchholz & Laura Doval & Jakub Kastl & Filip Matejka & Tobias Salz, 2025. "Personalized Pricing and the Value of Time: Evidence From Auctioned Cab Rides," Econometrica, Econometric Society, vol. 93(3), pages 929-958, May.
    2. Harish Guda & Upender Subramanian, 2019. "Your Uber Is Arriving: Managing On-Demand Workers Through Surge Pricing, Forecast Communication, and Worker Incentives," Management Science, INFORMS, vol. 67(5), pages 1995-2014, May.
    3. Hsu, Wen-Tai & Zhang, Hongliang, 2014. "The fundamental law of highway congestion revisited: Evidence from national expressways in Japan," Journal of Urban Economics, Elsevier, vol. 81(C), pages 65-76.
    4. Ajay Agrawal & Joshua S. Gans & Avi Goldfarb, 2019. "Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction," Journal of Economic Perspectives, American Economic Association, vol. 33(2), pages 31-50, Spring.
    5. Qu, Xiaobo & Wang, Shuaian & Zhang, Jin, 2015. "On the fundamental diagram for freeway traffic: A novel calibration approach for single-regime models," Transportation Research Part B: Methodological, Elsevier, vol. 73(C), pages 91-102.
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