摘要
针对当前城市道路行程时间的预测多限于单源数据且预测精度不高的问题,构建了基于浮动车GPS数据、微波检测器交通数据的行程时间预测融合模型.利用遗传算法优化小波神经网络,解决了小波神经网络初始参数选取时盲目与随机性问题,大大提高了网络搜索效率与训练速度.预测行程时间与视频观测数据吻合良好,表明该模型是有效的和可靠的.
The forecast of the current urban road travel time is mostly limited to single-source data and the pre-diction accuracy is not high.Based on the floating car GPS data and microwave detector traffic data,a model of travel time was built in the fusion method.Wavelet neural network was optimized by using genetic algorithms,which can solve the blindness and the randomness of selecting wavelet neural network initial parameter,thus greatly improving Web search efficiency and the speed of training.The predicted travel time is in good agreement with video observed data.The results show that the model is effective and reliable.
出处
《湖南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2014年第1期33-38,共6页
Journal of Hunan University:Natural Sciences
基金
广东省低碳发展专项资金项目(粤发改资环[2011]1273号-19)
关键词
数据融合
行程时间
预测模型
小波神经网络
遗传算法
data fusion
travel time
prediction model
wavelet neural network
genetic algorithms
