CAJ | 학술논문

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。
침대당전성시도로행정시간적예측다한우단원수거차예측정도불고적문제，구건료기우부동차GPS수거、미파검측기교통수거적행정시간예측융합모형。이용유전산법우화소파신경망락，해결료소파신경망락초시삼수선취시맹목여수궤성문제，대대제고료망락수색효솔여훈련속도。예측행정시간여시빈관측수거문합량호，표명해모형시유효적화가고적。
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.