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Estimating flexibility preferences to resolve temporal scheduling conflicts in activity-based modelling.

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This study introduces a new activity-based demand model that integrates temporal scheduling with discrete choice. It resolves activity time conflicts to maximize daily utility, improving urban planning models.

Keywords:
Activity-based modelDiscrete choiceMathematical optimisationMaximum likelihood estimationMixed-integer linear program

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Area of Science:

  • Transportation Planning
  • Urban Modeling
  • Behavioral Economics

Background:

  • Activity-based demand models are crucial for urban planning.
  • Existing models struggle to jointly optimize temporal scheduling and activity choices.
  • Resolving temporal conflicts in daily schedules is a key challenge for individuals.

Purpose of the Study:

  • To present a novel activity-based demand model integrating continuous temporal scheduling with discrete choice.
  • To address temporal scheduling conflicts by maximizing daily utility.
  • To improve the accuracy and efficiency of urban travel demand modeling.

Main Methods:

  • Combined optimization framework for temporal scheduling (timings, durations) with discrete choice models (participation, tours, destinations).
  • Introduced flexibility parameters to capture behavioral preferences and penalize timing deviations.
  • Developed estimation and simulation routines for efficient conflict resolution and parameter estimation.

Main Results:

  • The model jointly treats time conflicts and activity sequencing.
  • Flexibility parameters are estimated using a utility maximization approach.
  • The framework efficiently simulates city-scale case studies, reproducing empirical observations.

Conclusions:

  • The novel model effectively integrates temporal scheduling and activity choices.
  • It accurately reproduces empirical observations in a real-world case study (Lausanne).
  • This approach offers significant advantages over existing activity-based modeling techniques.