Traffic Flow Estimation System using a Hybrid Approach

Swe Sw Aung; Itaru Nagayama; Shiro Tamaki

doi:10.5573/IEIESPC.2017.6.4.281

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대한전자공학회
IEIE Transactions on Smart Processing & Computing 학술저널
IEIE Transactions on Smart Processing & Computing Vol.6 No.4
2017.8 281 - 291 (11page)
DOI : 10.5573/IEIESPC.2017.6.4.281

저자정보

Swe Sw Aung (University of the Ryukyus, Okinawa)
Itaru Nagayama (University of the Ryukyus, Okinawa)
Shiro Tamaki (University of the Ryukyus, Okinawa)

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Nowadays, as traffic jams are a daily elementary problem in both developed and developing countries, systems to monitor, predict, and detect traffic conditions are playing an important role in research fields. Comparing them, researchers have been trying to solve problems by applying many kinds of technologies, especially roadside sensors, which still have some issues, and for that reason, any one particular method by itself could not generate sufficient traffic prediction results. However, these sensors have some issues that are not useful for research. Therefore, it may not be best to use them as stand-alone methods for a traffic prediction system. On that note, this paper mainly focuses on predicting traffic conditions based on a hybrid prediction approach, which stands on accuracy comparison of three prediction models: multinomial logistic regression, decision trees, and support vector machine (SVM) classifiers. This is aimed at selecting the most suitable approach by means of integrating proficiencies from these approaches. It was also experimentally confirmed, with test cases and simulations that showed the performance of this hybrid method is more effective than individual methods.

#Decision tree #Logistic regression #Support vector machine