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Traffic Flow Estimation System using a Hybrid Approach

  • Aung, Swe Sw (Information Engineering Department, University of the Ryukyus) ;
  • Nagayama, Itaru (Information Engineering Department, University of the Ryukyus) ;
  • Tamaki, Shiro (Information Engineering Department, University of the Ryukyus)
  • Received : 2017.02.07
  • Accepted : 2017.05.31
  • Published : 2017.08.30

Abstract

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.

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