Calibrating Rail Transit Assignment Models with Genetic Algorithm and Automated Fare Collection Data

Developments in the application of automatic data collection (ADC) such as automated fare collection (AFC) systems have made the collection of detailed passenger trip data in an urban rail transit (URT) network possible. AFC systems using smart card technology have become the main method for collecting urban rail transit (URT) fares in many cities around the world. The transaction data obtained through these AFC systems contain a large amount of archived information including how passengers use the URT system. The information obtained from AFC systems can be used in calibrating assignment models for precise passenger flow calculation. This paper presents a methodology for calibrating URT assignment models using AFC data. The study provides an approach that calibrates models disaggregately based on AFC data that avoids some disadvantages of traditional manual data collection approaches and can be incorporated into an automatic calibration procedure for easily obtaining accurate results.

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  • English

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  • Accession Number: 01536376
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jul 25 2014 11:30AM