Self-adapting traffic flow status forecasts using clustering
- 1 January 2009
- journal article
- Published by Institution of Engineering and Technology (IET) in IET Intelligent Transport Systems
- Vol. 3 (1), 67-76
- https://doi.org/10.1049/iet-its:20070048
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.Keywords
This publication has 11 references indexed in Scilit:
- Online Learning Solutions for Freeway Travel Time PredictionIEEE Transactions on Intelligent Transportation Systems, 2008
- Where are public transit needed – Examining potential demand for public transit for commuting tripsComputers, Environment and Urban Systems, 2007
- Incremental and online learning through extended kalman filtering with constraint weights for freeway travel time predictionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- A Study of Hybrid Neural Network Approaches and the Effects of Missing Data on Traffic ForecastingNeural Computing & Applications, 2001
- Self-Organizing MapsSpringer Series in Information Sciences, 2001
- Development and evaluation of a hybrid travel time forecasting modelTransportation Research Part C: Emerging Technologies, 2000
- Spectral Basis Neural Networks for Real-Time Travel Time ForecastingJournal of Transportation Engineering, 1999
- Data clusteringACM Computing Surveys, 1999
- Combining kohonen maps with arima time series models to forecast traffic flowTransportation Research Part C: Emerging Technologies, 1996
- Automated detection of lane-blocking freeway incidents using artificial neural networksTransportation Research Part C: Emerging Technologies, 1995