• Graduate Programs
  • Research
  • Browse our Courses
  • Events
    • Events Calendar
    • Events Archive
    • Summer School
      • Applied Public Policy Evaluation
      • Deep Learning
      • Development Economics
      • Economics of Blockchain and Digital Currencies
      • Economics of Climate Change
      • The Economics of Crime
      • Foundations of Machine Learning with Applications in Python
      • From Preference to Choice: The Economic Theory of Decision-Making
      • Inequalities in Health and Healthcare
      • Marketing Research with Purpose
      • Markets with Frictions
      • Modern Toolbox for Spatial and Functional Data
      • Sustainable Finance
      • Tuition Fees and Payment
      • Business Data Science Summer School Program
    • Tinbergen Institute Lectures
    • 2026 Tinbergen Institute Opening Conference
    • Annual Tinbergen Institute Conference
  • News
  • Summer School
    • Applied Public Policy Evaluation
    • Deep Learning
    • Development Economics
    • Economics of Blockchain and Digital Currencies
    • Economics of Climate Change
    • The Economics of Crime
    • Foundations of Machine Learning with Applications in Python
    • From Preference to Choice: The Economic Theory of Decision-Making
    • Inequalities in Health and Healthcare
    • Marketing Research with Purpose
    • Markets with Frictions
    • Modern Toolbox for Spatial and Functional Data
    • Sustainable Finance
    • Tuition Fees and Payment
  • Alumni

Verhoef, ErikT. and Silva, HugoE. (2017). Dynamic equilibrium at a congestible facility under market power Transportation Research. Part B, Methodological, 105:174--192.


  • Journal
    Transportation Research. Part B, Methodological

This paper studies equilibrium and optimum at a congested facility when firms have market power; e.g., when a few airlines jointly use a congested airport. Unlike most of the previous literature, we characterize the equilibrium in terms of timing of arrivals in a continuous-time congestion model when firms simultaneously schedule services. Using the Henderson-Chu dynamic model of flow congestion in a multiple-firm setting, we find that a stable and unique Nash equilibrium in pure strategies always exists. Importantly, it also exists in cases where it fails to exist under bottleneck congestion (notably when the value of schedule late exceeds the value of travel delays). We find that symmetric firms schedule arrivals inefficiently, and strongly concentrated around the desired arrival time so that the peak is shorter and delays are higher than socially optimal. We show that when firms are asymmetric in terms of output, all firms schedule vehicles in the peak center, around the desired arrival time, with arrival windows increasing with firm size such that a smaller firm's window is always fully contained in a larger firm's window and only the largest firm operates in the early and late shoulders. Furthermore, for any pair of asymmetric firms, the larger firm has a higher instantaneous arrival rate at any moment where both firms schedule arrivals. Our results also show that even though self-internalization can be substantial, there is scope for decentralizing the first-best outcome through time-varying tolls.