Real-time Travel Time Estimation with Sparse Reliable Surveillance Information
Real-time Travel Time Estimation with Sparse Reliable Surveillance Information
复制标题
利用稀疏可靠的监视信息进行实时行程时间估计
DOI：
10.1145/3380988
复制
发表时间：
2020
影响因子：
--
通讯作者：
Hengchang Liu
中科院分区：
文献类型：
--
作者：
Wen Zhang;Yang Wang;Xike Xie;Chuancai Ge;Hengchang Liu
Origin-destination (OD) travel time estimation is of paramount importance for applications such as intelligent transportation. In this work, we propose a new solution for OD travel time estimation, with road surveillance camera data. The surveillance information supports accurate and reliable observations at camera-equipped intersections, but is associated with missing and incomplete surveillance records at the camera-free intersections. To overcome this, we propose a modified version of multi-layer graph convolutional networks. The camera surveillance data is used to extract the traffic flow of each intersection, the extracted information serves as the input of the multi-layer GCN based model, based on which the real-time traffic status can be predicted. To enhance the estimation accuracy, we address the effects of various features for the travel time estimation with encoder-decoder networks and embedding techniques. We further improve the generalization of our model by using multi-task learning. Extensive experiments on real datasets are done to verify the effectiveness of our proposals.
