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欧阳俊,陆锋,刘兴权,段滢滢(中科院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京 100101;中南大学地学与环境工程学院,长沙 410083)

摘 要
Short-term urban traffic forecasting based on multi-kernel SVM model

ouyangjun,LU Feng,LIU Xingquan,DUAN Yingying(State Key Laboratory of Resources and Environmental Information System,Institute of Geographical Science and Natural Resources Research,CAS,Beijing 100101;School of Geoscience and Environmental Engineering,Central South University,Changsha 410083)

City road traffic system is characterized as a system of nonlinearity, uncertainty and spatial-temporal correlation, which makes traffic system parameters description and knowledge extraction difficult, and results in current short-term traffic forecast methods can not obtain satisfactory accuracy. This paper presents a hybrid multiple-kernel support vector machine model (MSVM) for conducting short-term traffic forecast. With statistical analysis of large amounts of traffic condition data samples, the proposed model not only has a capacity of recognizing and dealing with different types of input data separately, but also takes advantages of global optimization, generalization and adaptability of support vector machine. Moreover, the parameters of the hybrid model is optimized with an improved particle swarm algorithm (PSO). Aiming at the linear correlation between real time and historical traffic condition, the nonlinear correlation between real time and previous time period, and also up and downstream traffic condition, the proposed model uses a linear kernel to extract the linear pattern of traffic flow and a nonlinear kernel to map the nonlinear pattern of residuals from the linear kernel. Both the historical regularity and time-variation characteristics of city road traffic are considered in the MSVM model so as to obtain the knowledge from the influential factors of real time traffic condition in order to improve forecast accuracy. The experimental results show that the proposed model behaves satisfactory performance and robustness, and has a good potential for applications.