지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
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In this research, neural-based model was developed to forecast link travel times. And it is also compared with other time series forecasting models such as Box-Jenkins model, Kalman filter model. These models are validated to evaluate the accuracy of models with real time series data gathered by the license plate method. Neural network’s convergency and generalization were investigated by modifying learning rate, momentum term and the number of hidden layer units. Through this experiment, the optimum configuration of the neural network architecture was determined. Optimum learning rate, momentum term and the number of hidden layer units show 0.3, 0.5, 13 respectively. It may be applied to DRGS(dynamic route guidance system) with a minor modification. The methods are suggested at the conclusion of this paper. And There is no doubt that this neural - based model can be applied to many other time series forecasting problems such as population forecasting, vehicle volume forecasting, etc.
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UCI(KEPA) : I410-ECN-0101-2018-053-003230695