Generating Complete All-Day Activity Plans with Genetic Algorithms

This paper is part of a larger attempt to build an integrated multi-agent simulation model for transportation planning. Activity-based demand generation fits well into the paradigm of multi-agent simulation, where each traveler is kept as an individual throughout the whole modeling process. Activity-based demand generation constructs complete all-day activity plans for each member of a population and derives transportation demand from the fact that consecutive activities at different locations need to be connected by travel. This paper uses genetic algorithms (GAs) to construct all-day activity plans. This GA keeps, for each member of the population, several instances of possible all-day activity plans in memory. Those plans are modified by mutation and crossover, while "bad" instances are eventually discarded. Any GA needs a fitness function to evaluate the performance of each instance. For all-day activity plans, it makes sense to use a utility function to obtain such a fitness. In consequence, a significant part of the paper is spent discussing such a utility function. In addition, the paper shows the performance of the GA for a few selected problems, including very busy and less busy days. Findings show that the algorithm generates plausible solutions for both crowded and relaxed activity sets even when the computation time is restricted.

  • Availability:
  • Authors:
    • Charypar, David
    • Nagel, Kai
  • Publication Date: 2005-7


  • English

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  • Accession Number: 01000470
  • Record Type: Publication
  • Files: TRIS, ATRI
  • Created Date: May 30 2005 3:58PM