实时数据下城市高架路短期交通流预测模型  

Prediction Model of Short-term Traffic Stream of Urban Elevated Road under Real-time Data

作　　者：钟建新 蓝志清 Zhong Jianxin;Lan Zhiqing(Department of Mathematics,GanZhou Teachers College,GanZhou 341000,China)

机构地区：[1]赣州师范高等专科学校数学系,江西赣州341000

出　　处：《安徽电子信息职业技术学院学报》2022年第5期14-19,共6页Journal of Anhui Vocational College of Electronics & Information Technology

基　　金：2020年江西省教育厅科学技术研究重点项目“实时视域下赣州市高架路交通流预测模型研究”(203502)。

摘　　要：灰狼算法具有容易困于局部最优且收敛精度有缺陷,笔者改进非线性收敛因子,同时利用差分进化策略,得到新的灰狼优化算法。借助基准测试函数进行验证,改进后算法的全局搜索能力和收敛速度强于标准灰狼算法。进一步把改进后的算法应用在城市高架路短期交通流预测,具有较好的可行性,经过比较可知,预测的精度有明显的提升,优化了实时数据下高架路短期交通流预测的计算路径。For the gray wolf algorithm,it is easy to be trapped in the local optimum and the convergence speed is not perfect.The author obtained an improved gray wolf optimization algorithm based on nonlinear convergence factor and differential evolution strategy.After verification by the benchmark function,the global search of the improved algorithm,the ability and convergence speed are stronger than the standard gray wolf algorithm,and the improved algorithm is further applied to the short-term traffic flow prediction of urban elevated roads,which has good feasibility.After comparison,it can be seen that the prediction accuracy has been significantly improved,and the real-time data of elevated roads has been optimized.

关 键 词：实时数据 灰狼算法 交通流预测 数学模型 

分 类 号：U491[交通运输工程—交通运输规划与管理]