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Recovering scheduling preferences in dynamic departure time models.

Zhenyu Yang1, Pietro Giardina1, Nikolas Gerolimnis1

  • 1Urban Transport Systems Laboratory (LUTS), École Polytechnique Fedéralé de Lausanne (EPFL), Lausanne, 1015 Switzerland.

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Summary
This summary is machine-generated.

Commuters

Keywords:
BottleneckScheduling preferencesTraffic flowTravel demand management

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Area of Science:

  • Transportation Science
  • Econometrics
  • Urban Planning

Background:

  • Understanding commuter behavior is crucial for urban planning and traffic management.
  • Observed arrival times are influenced by traffic congestion and individual preferences.
  • Existing models often simplify the complex interplay between scheduling preferences and traffic conditions.

Purpose of the Study:

  • To infer commuters' scheduling preferences using their observed arrival times.
  • To develop a structural model that balances congestion costs with early/late arrival penalties.
  • To estimate the population distribution of scheduling preferences and desired arrival times.

Main Methods:

  • Structural modeling of commuter decision-making.
  • Incorporating within-day traffic congestion patterns.
  • Applying maximum likelihood estimation (MLE) to synthetic data.

Main Results:

  • Successfully recovered parameters of the joint distribution of scheduling preferences and desired arrival times.
  • Demonstrated the effectiveness of the proposed maximum likelihood estimation method.
  • Quantified the trade-offs commuters make between travel time and schedule adherence.

Conclusions:

  • The developed model accurately infers commuter scheduling preferences from arrival data.
  • This approach provides valuable insights for transportation planning and traffic flow optimization.
  • The method is effective in estimating underlying preference distributions in a population.