Abstract
An online short-term prediction model of highway travel time was made using MLP-neural networks on a Finnish two-lane two-way highway. The results of the online application of the model showed that the use of even a simple prediction model could improve the quality of travel time information. However, the problem remained that if the congestion phenomenon changes (even slightly), the neural network needs new training. In addition to this, there is often too little time to collect training data, which leads to a small number of samples that represent random incidents and consequently to a need to learn while working online. The purpose of this ongoing study is to make a self-adapting short-term prediction model of the flow status. At present, the information of the flow status is given on the Internet based on latest measurements of travel time. The objective is to improve the quality of that information.
Original language | English |
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Title of host publication | German-Dutch-Finnish Seminar on traffic engineering |
Subtitle of host publication | CD-ROM |
Publication status | Published - 2004 |
MoE publication type | B3 Non-refereed article in conference proceedings |
Event | German - Dutch - Finnish seminar on traffic engineering - Otaniemi, Espoo, Finland Duration: 3 Oct 2007 → 5 Oct 2007 |
Seminar
Seminar | German - Dutch - Finnish seminar on traffic engineering |
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Country/Territory | Finland |
City | Espoo |
Period | 3/10/07 → 5/10/07 |