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Expressway traffic flow prediction using chaos cloud particle swarm algorithm and PPPR model. (English) Zbl 1299.90093

Summary: Aiming at the real-time, fluctuation and nonlinear characteristics of the expressway short-term traffic flow forecasting, the parameter projection pursuit regression (PPPR) model is applied to forecast the expressway traffic flow, where the orthogonal Hermite polynomial is used to fit the ridge functions and the least square method is employed to determine the polynomial weight coefficient \(\boldsymbol{c}\). In order to efficiently optimize the projection direction \(\boldsymbol{a}\) and the number \(M\) of ridge functions of the PPPR model, the chaos cloud particle swarm optimization (CCPSO) algorithm is applied to optimize the parameters. The CCPSO-PPPR hybrid optimization model for expressway short-term traffic flow forecasting is established, in which the CCPSO algorithm is used to optimize the optimal projection direction \(a\) in the inner layer while the number \(M\) of ridge functions is optimized in the outer layer. Traffic volume, weather factors and travel date of the previous several time intervals of the road section are taken as the input influencing factors. Example forecasting and model comparison results indicate that the proposed model can obtain a better forecasting effect and its absolute error is controlled within \([-6, 6]\), which can meet the application requirements of expressway traffic flow forecasting.

MSC:

90B20 Traffic problems in operations research
68W20 Randomized algorithms
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