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Traffic prediction models for Bangkok traffic data

Authors: Nattapon Klakhaeng; Jumpol Yaothanee; Sukree Sinthupinyo; Wasan Pattara-Atikom;

Traffic prediction models for Bangkok traffic data

Abstract

This Paper presents a prediction model of traffic congestion condition on the roads in Bangkok. The results from our prediction model will be useful for systems related to traffic information. We constructed a model which can achieve 92.1% of accuracy on prediction of the congestion condition in next 30 minutes. Then, we found that the accuracy on the results that change from the current state is unsatisfiable. Hence, we constructed a new model that can improve the accuracy on this portion of traffic data. Moreover, we propose a new performance measurement which can distinguish the results in more detail. The final results show that our approach can improve the accuracy over the former model.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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