Simulated annealing: theory and applications
Simulated annealing: theory and applications
The nature of statistical learning theory
The nature of statistical learning theory
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Software reliability forecasting by support vector machines with simulated annealing algorithms
Journal of Systems and Software
BP neural network with rough set for short term load forecasting
Expert Systems with Applications: An International Journal
Space-time modeling of traffic flow
Computers & Geosciences
Taiwanese 3G mobile phone demand forecasting by SVR with hybrid evolutionary algorithms
Expert Systems with Applications: An International Journal
Traffic Flow Forecasting Algorithm Using Simulated Annealing Genetic BP Network
ICMTMA '10 Proceedings of the 2010 International Conference on Measuring Technology and Mechatronics Automation - Volume 03
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 1
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On chaotic simulated annealing
IEEE Transactions on Neural Networks
An evolutionary algorithm that constructs recurrent neural networks
IEEE Transactions on Neural Networks
SVR with chaotic genetic algorithm in taiwanese 3g phone demand forecasting
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Collaborative analytics for predicting expressway-traffic congestion
Proceedings of the 14th Annual International Conference on Electronic Commerce
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Accurate forecasting of inter-urban traffic flow has been one of the most important issues globally in the research on road traffic congestion. However, the information of inter-urban traffic presents a challenging situation; the traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during daily peak periods, traffic flow data reveals cyclic (seasonal) trend. In the recent years, the support vector regression model (SVR) has been widely used to solve nonlinear regression and time series problems. However, the applications of SVR models to deal with cyclic (seasonal) trend time series have not been widely explored. This investigation presents a traffic flow forecasting model that combines the seasonal support vector regression model with chaotic simulated annealing algorithm (SSVRCSA), to forecast inter-urban traffic flow. Additionally, a numerical example of traffic flow values from northern Taiwan is employed to elucidate the forecasting performance of the proposed SSVRCSA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal autoregressive integrated moving average (SARIMA), back-propagation neural network (BPNN) and seasonal Holt-Winters (SHW) models. Therefore, the SSVRCSA model is a promising alternative for forecasting traffic flow.