Probabilistic Forecasting of Traffic Flow Using Multikernel Based Extreme Learning Machine

Real-time and accurate prediction of traffic flow is the key to intelligent transportation systems (ITS). However, due to the nonstationarity of traffic flow data, traditional point forecasting can hardly be accurate, so probabilistic forecasting methods are essential for quantification of the potential risks and uncertainties for traffic management. A probabilistic forecasting model of traffic flow based on a multikernel extreme learning machine (MKELM) is proposed. Moreover, the optimal output weights of MKELM are obtained by utilizing Quantum-behaved particle swarm optimization (QPSO) algorithm. To verify its effectiveness, traffic flow probabilistic prediction using QPSO-MKELM was compared with other learning methods. Experimental results show that QPSO-MKELM is more effective for practical applications. And it will help traffic managers to make right decisions.