面向流场可视化的沉浸式虚拟现实协作框架郑志文 1,2) , 杨超 2,3) , 韩永国 1) , 杨洋 4)* , 吴亚东 5) , 王昉 2,3) , 张晓蓉 1) 1) (西南科技大学计算机科学与技术学院 绵阳 621010) 2) (中国空气动力研究与发展中心计算空气动力研究所 绵阳 621000) 3) (空气动力学国家重点实验室 绵阳 621000) 4) (四川省计算机研究院 成都 610041) 5) (四川轻化工大学计算机科学与工程学院 自贡 643002)

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Transportation system has time-varying, coupling and nonlinear dynamic characteristics. Traffic flow forecast is one of the key technologies of traffic guidance. It is very difficult to accurately forecast them effectively. This paper has analyzed the complexity and the evaluation index of urban transportation network and has proposed the forecasting model of the hybrid algorithm based on chaos immune knowledge. First of all, the chaos knowledge is introduced into the topology structure of immune network, so as to obtain the matching predictive values and knowledge base. Secondly, this algorithm can dynamically control and adjusted the regional search speed and can fuse the information obtained by the chaos and immune algorithm, in order to realize the short-term traffic flow forecast. Finally, the simulation experiment shows that the traffic flow forecasting error obtained by the method is small, feasible and effective and can better meet the needs of the traffic guidance system.

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