Abstract
The principal aim of this study was to develop a method
for making a short-term prediction model of traffic flow
status (i.e. travel time and a five-step
travel-speed-based classification) and test its
performance in the real world environment. Specifically,
the objective was to find a method that can predict the
traffic flow status on a satisfactory level, can be
implemented without long delays and is practical for
real-time use also in the long term. A sequence of
studies shows the development process from offline models
with perfect data to online models with field data.
Models were based on MLP neural networks and
self-organising maps. The purpose of the online model was
to produce real-time information of the traffic flow
status that can be given to drivers. The models were
tested in practice. In conclusion, the results of online
use of the prediction models in practice were promising
and even a simple prediction model was shown to improve
the accuracy of travel time information especially in
congested conditions. The results also indicated that the
self-adapting principle improved the performance of the
model and made it possible to implement the model quite
quickly. The model was practical for real-time use also
in the long term in terms of the number of carry bits
that it requires to restore the history of samples of
traffic situations. As self-adapting this model performed
better than as a static version i.e. without the
self-adapting feature, as the proportion of correctly
predicted traffic flow status increased considerably for
the self-adapting model during the online trial.
Original language | English |
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Qualification | Doctor Degree |
Awarding Institution |
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Supervisors/Advisors |
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Award date | 12 Jun 2009 |
Place of Publication | Espoo |
Publisher | |
Print ISBNs | 978-951-38-7340-0 |
Electronic ISBNs | 978-951-38-7341-7 |
Publication status | Published - 2009 |
MoE publication type | G5 Doctoral dissertation (article) |
Keywords
- prediction
- traffic flow status
- travel time