Fuzzy Modeling Approach for Combined Forecasting of Urban Traffic Flow
作者： Antony Stathopoulos , Loukas Dimitriou , Theodore Tsekeris
DOI: 10.1111/J.1467-8667.2008.00558.X
关键词: Metaheuristic 、 Kalman filter 、 Fuzzy logic 、 Set (abstract data type) 、 Fuzzy rule 、 Traffic flow 、 Artificial neural network 、 Artificial intelligence 、 Mixed model 、 Computer science 、 Data mining
摘要: This paper looks at the problem of accuracy short-term traffic flow forecasting in complex case urban signalized arterial networks. A new, artificial intelligence-based approach is offered for improving predictions through suitably combining forecasts derived from a set individual predictors. employs fuzzy rule-based system (FRBS), which augmented with an appropriate metaheuristic (direct search) technique to automate tuning parameters within online adaptive rolling horizon framework. The proposed hybrid FRBS used nonlinearly combine resulting Kalman filter and neural network model. Empirical results obtained model's implementation into actual show ability considerably overperform given
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