多源异构数据融合的城市私家车流量预测研究

Study of forecasting urban private car volumes based on multi-source heterogeneous data fusion

通过有效地捕获城市私家车出行的时空特征,提出一种多源异构数据融合的私家车流量预测模型。首先,融合私家车轨迹和城市区域数据表征城市私家车的出行分布;其次,通过多视角时空图建模私家车出行和城市区域之间的动态关联,设计了多图卷积-注意力网络以提取车流量演变的时空特征;最后,进一步融合时空特征与天气等外部特征,联合预测私家车流量。在长沙市和深圳市采集的真实数据上进行验证,实验结果表明,与现有的模型相比,所提模型的均方根误差约降低了11.3%~20.3%,平均绝对百分误差约降低了10.8%~36.1%。

By effectively capturing the spatio-temporal characteristics of urban private car travel,a multi-source heterogeneous data fusion model for private car volume prediction was proposed.Firstly,private car trajectory and area-of-interest data were integrated.Secondly,the spatio-temporal correlations between private car travel and urban areas were modeled through multi-view spatio-temporal graphs,the multi-graph convolution-attention network(MGC-AN)was proposed to extract the spatio-temporal characteristics of private car travel.Finally,the spatio-temporal characteristics and external characteristics such as weather were integrated for joint prediction.Experiments were conducted on real datasets,which were collected in Changsha and Shenzhen.The experimental results show that,compared with the existing prediction model,the root mean square error of the MGC-AN is reduced 11.3%~20.3%,and the average absolute percentage error is reduced 10.8%~36.1%.