APLIKASI GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) PADA PEMODELAN VOLUME KENDARAAN MASUK TOL SEMARANG
Time series data from neighboring separated location often associated both spatially and through time. Generalized space time autoregrresive (GSTAR) model is one of the most common used space-time model to modeling and predicting spatial and time series data. This study applied GSTAR to modeling veh...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Universitas Diponegoro
2013-12-01
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| Series: | Media Statistika |
| Online Access: | https://ejournal.undip.ac.id/index.php/media_statistika/article/view/7639 |
| Summary: | Time series data from neighboring separated location often associated both spatially and through time. Generalized space time autoregrresive (GSTAR) model is one of the most common used space-time model to modeling and predicting spatial and time series data. This study applied GSTAR to modeling vehicle volume entering four tollgate (GT) in Semarang City: GT Muktiharjo, GT Gayamsari, GT Tembalang, and GT Manyaran. The data was collected by month from 2003 to 2009. The best model provided by this study is GSTAR (21)-I(1,12) uniformly weighted with the smallest REMSE mean 76834.
Key words: GSTAR, Vehicle Volume, Space-Time Model |
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| ISSN: | 1979-3693 2477-0647 |