Methods for travel pattern analysis using large-scale passive data

Comprehensive knowledge of travel patterns is crucial to enable planning for a more efficient traffic system that accommodates human mobility demand. Currently, this knowledge is mainly based on traffic models based on relatively small samples of observations collected from travel surveys and traffic counts. The data is expensive to collect and provides only partial observations of travel patterns. With the rise of new technology, new large-scale passive data sources can be used to analyse travel patterns. This thesis aims to expand the knowledge about how to use cellular network data collected by cellular network operators and smart-card data from public transit systems to analyse travel patterns. The focus is particularly on the data processing methods needed to extract travel patterns. The thesis’s contributions include new methods for extracting trips, estimating travel demand, route inference and travel mode choice from cellular network data and a method to extract travel behaviour changes from smart-card data. Different approaches are proposed to evaluate the methods: the validation using experimental data, validation using other available data sources, and comparison of results obtained using different methods.

Language

  • English

Media Info

Subject/Index Terms

Filing Info

  • Accession Number: 01844850
  • Record Type: Publication
  • Source Agency: Swedish National Road and Transport Research Institute (VTI)
  • ISBN: 9789179296650
  • Files: ITRD, VTI
  • Created Date: May 6 2022 5:07PM