Traffic-flow forecasting using a 3-stage model

During the past few years, various traffic-flow forecasting models, i.e. an ARIMA, an ANN, and so on, have been developed to predict more accurate traffic flow. However, these strategies rest on the assumption that the pattern that has been identified will continue into the future. So ARIMA or ANN m...

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Bibliographic Details
Main Authors: Chang, S.C., Kim, R.S., Kim, S.J., Ahn, M.H.
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Data analysis
Feedforward systems
Mechatronics
Neural networks
Predictive models
Statistics
Telecommunication traffic
Time series analysis
Traffic control
Online Access:Request full text
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Summary:During the past few years, various traffic-flow forecasting models, i.e. an ARIMA, an ANN, and so on, have been developed to predict more accurate traffic flow. However, these strategies rest on the assumption that the pattern that has been identified will continue into the future. So ARIMA or ANN models with its traditional architecture cannot be expected to give good predictions unless this assumption is valid. In this paper, we compared with an ANN model and ARIMA model and tried to combine an ARIMA model and ANN model for obtaining a better forecasting performance. In addition to combining two models, we also introduced judgmental adjustment technique that has an effect on correcting irregular and infrequent future events. Our approach can improve the forecasting power in traffic flow. To prove it, we have compared the performance of the models.
DOI:10.1109/IVS.2000.898384