HetETA: Heterogeneous Information Network Embedding for Estimating Time of Arrival
Huiting Hong: AI Labs Didi Chuxing Beijing China ; Yucheng Lin: AI Labs Didi Chuxing Beijing China ; Xiaoqing Yang: AI Labs Didi Chuxing Beijing China ; Zang Li: AI Labs Didi Chuxing Beijing China ; Jieping Ye: AI Labs Didi Chuxing Beijing China ; Kun Fu: AI Labs Didi Chuxing Beijing China ; Zheng Wang: AI Labs Didi Chuxing Beijing China ; Xiaohu Qie: Technology Ecosystem Development Didi Chuxing Beijing China
The estimated time of arrival (ETA) is a critical task in the intelligent transportation system, which involves the spatiotemporal data. Despite a significant amount of prior efforts have been made to design efficient and accurate systems for ETA task, few of them take structural graph data into account, much less the heterogeneous information network. In this paper, we propose HetETA to leverage heterogeneous information graph in ETA task. Specifically, we translate the road map into a multi-relational network and introduce a vehicle-trajectories based network to jointly consider the traffic behavior pattern. Moreover, we employ three components to model temporal information from recent periods, daily periods and weekly periods respectively. Each component comprises temporal convolutions and graph convolutions to learn representations of the spatiotemporal heterogeneous information for ETA task. Experiments on large-scale datasets illustrate the effectiveness of the proposed HetETA beyond the state-of-the-art methods, and show the importance of representation learning of heterogeneous information networks for ETA task.
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