面向行程时间预测准确度评价的数据融合方法

提出BP神经网络融合模型.该模型由三部分组成:初始数据产生模块、BP神经网络数据融合模块、融合结果分析模块.选择四个参数作为该模型的输入变量,其中路段交通流密度和交通量由线圈数据提供,而行程时间估计值与浮动车样本量由浮动车数据提供,并且给出选择这四个参数的依据与原因.最后选择杭州市的一条主干道作为目标路段,采集该路段上的406组数据对该模型进行验证,试验结果表明模型对准确度评价的相对误差仅为4.86％.

Data Fusion Method for Accuracy Evaluation of Travel Time Forecast

A BP neural network model was brought forward, which was composed by the initial data generated module, the BP network based data fusion module and the result analysis module. Four variables such as link average density, traffic volume, link average travel time based on floating car data(FCD) and floating car sampling size were taken as input variables. Link average density and traffic volume could be obtained by the data of loop detectors, while link average travel time and floating car sampling size could be acquired with FCD. Then, the reasons to choose those four variables were given with the support of a statistical analysis. At last, an arterial road in Hangzhou was chosen as an object link, 406 groups of data were utilized to verify the model. The results show that the mean absolute error (MAE) of the proposed model is only 4.86%.