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Using Floating Car Data in Route Choice Modelling - Field Study
Author(s) -
Hekmat Dabbas,
Walid Fourati,
Bernhard Friedrich
Publication year - 2021
Publication title -
transportation research procedia
Language(s) - English
Resource type - Journals
eISSN - 2352-1465
pISSN - 2352-1457
DOI - 10.1016/j.trpro.2021.01.084
Subject(s) - computer science , trajectory , process (computing) , logit , data set , field (mathematics) , choice set , set (abstract data type) , data mining , point (geometry) , mixed logit , logistic regression , operations research , machine learning , artificial intelligence , statistics , engineering , mathematics , physics , geometry , astronomy , pure mathematics , programming language , operating system
Route choice assessment is an essential step in classic traffic modeling techniques, which have long been based on theoretical, empirically calibrated models. As floating car data (FCD) are becoming widely available, remarkable ongoing efforts attempt to improve the existing state-of-the-practice with a more accurate understanding of the route choice behaviour. This paper documents a study, conducted with real trajectory data collected on a motorway network in Germany. The aim was to analyze the usage of FCD to estimate route choices on different spatial scales. The logit model was used as a starting point. Then different levels of use of trajectory data in the process of traffic assignment were tested and compared. The validation is made against reference data collected through Automatic Number Plate Recognition (ANPR) cameras on selected routes during consecutive weekdays. The drawback of using the logit model is discussed as well as the advantages of the FCD involvement in the estimation process. We conducted that an aggregated set of FCD can estimate highly accurate and realistic route choice proportions.

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