Abstract
A key problem for the efficient use of stationary traffic prediction
models is that for adaptation to new data they require human-made
re-calibration with a new database. So far, there has been a lack of
knowledge of how to develop a practical prediction model that would
learn while working online. Anyone providing real-time traffic
information and making forecasts of the traffic situation could benefit
from such models. The aim of the study is to develop a method to make a
self-adapting short-term prediction model for the status of traffic
flow. The principles for such a model are described. The method is based
on self-organising map and the model is implemented on a highway in the
Helsinki Metropolitan Area. Specifically, the structure of the model
makes it possible for the model to learn by itself without the need to
save all the data into databases. Consequently, long-term online use of
the model makes fewer demands on computers. The results indicated that
the self-adapting principle improved the performance of the model. The
principles of the model can also be applied in other locations.
Original language | English |
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Pages (from-to) | 67-76 |
Number of pages | 10 |
Journal | IET Intelligent Transport Systems |
Volume | 3 |
Issue number | 1 |
DOIs | |
Publication status | Published - 2009 |
MoE publication type | A1 Journal article-refereed |