Calibration of the empirical fundamental relationship using very large databases
| Autor: | José Reynaldo Setti, Juliana Mitsuyama Cardoso, Lucas Assirati |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
050210 logistics & transportation
TA1001-1280 Database Computer science Calibration (statistics) 05 social sciences 020101 civil engineering Model parameters 02 engineering and technology Filter (signal processing) very large databases computer.software_genre Data treatment 0201 civil engineering Transportation engineering Traffic flow models genetic algorithm 0502 economics and business Outlier Genetic algorithm Range (statistics) Initial treatment Model fitting computer |
| Zdroj: | Transportes, Vol 29, Iss 1 (2021) |
| ISSN: | 2237-1346 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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