Open AccessCalibration of the empirical fundamental relationship using very large databases
Author(s) -
Juliana Mitsuyama Cardoso,
Lucas Assirati,
José Reynaldo Setti
Publication year - 2021
Publication title -
transportes
Language(s) - English
Resource type - Journals
eISSN - 2237-1346
pISSN - 1415-7713
DOI - 10.14295/transportes.v29i1.2317
Subject(s) - outlier , range (aeronautics) , calibration , computer science , data mining , filter (signal processing) , genetic algorithm , algorithm , curve fitting , statistics , mathematics , artificial intelligence , machine learning , engineering , computer vision , aerospace engineering
This paper describes a procedure for fitting traffic stream models using very large traffic databases. The proposed approach consists of four steps: (1) an initial treatment to eliminate noisy, inaccurate data and to homogenize the information over the density range; (2) a first fitting of the model, based on the sum of squared orthogonal errors; (3) a second filter, to eliminate outliers that survived the initial data treatment; and (4) a second fitting of the model. The proposed approach was tested by fitting the Van Aerde traffic stream model to 104 thousand observations collected by a permanent traffic monitoring station on a freeway in the metropolitan region of São Paulo, Brazil. The model fitting used a genetic algorithm to search for the best values of the model parameters. The results demonstrate the effectiveness of the proposed approach.