首页|Spatio-Temporal Learning for Route-Based Travel Time Estimation
Spatio-Temporal Learning for Route-Based Travel Time Estimation
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
万方数据
维普
Travel time estimation(TTE)is a fundamental task to build intelligent transportation systems.However,most existing TTE solutions design models upon simple homogeneous graphs and ignore the heterogeneity of traffic net-works,where,e.g.,main roads typically contribute differently from side roads.In terms of spatial dimension,few studies consider the dynamic spatial correlations across road segments,e.g.,the traffic speed/volume on road segment A may cor-relate with the traffic speed/volume on road segment B,where A and B could be adjacent or non-adjacent,and such corre-lations may vary across time.In terms of temporal dimension,even fewer studies consider the dynamic temporal depen-dences,where,e.g.,the historical states of road A may directly correlate with the recent state of A,and may also indirect-ly correlate with the recent state of road B.To track all aforementioned issues of existing TTE approaches,we provide HDTTE,a solution that employs heterogeneous and dynamic spatio-temporal predictive learning.Specifically,we first de-sign a general multi-relational graph constructor that extracts hidden heterogeneous information of road segments,where we model road segments as nodes and model correlations as edges in the multi-relational graph.Next,we propose a dy-namic graph attention convolution module that aggregates dynamic spatial dependence of neighbor roads to focal roads.We also present a novel correlation-augmented temporal convolution module to capture the influence of states at past time steps on current traffic states.Finally,in view of the periodic dependence of traffic,we develop a multi-scale adaptive fu-sion layer to enable HDTTE to exploit periodic patterns from recent,daily,and weekly traffic states.An experimental study using real-life highway and urban datasets demonstrates the validity of the approach and its advantage over others.
heterogeneous traffic modelingspatio-temporal learningtravel time estimationtraffic speed prediction
房子荃、孙琦晨、陈璐、胡丹蕾、高云君
展开 >
School of Software Technology,Zhejiang University,Ningbo 315048,China
College of Computer Science,Zhejiang University,Hangzhou 310027,China
万方数据
维普
