ConSTGAT: Contextual Spatial-Temporal Graph Attention Network for Travel Time Estimation at Baidu Maps - CrossMinds.ai
ConSTGAT: Contextual Spatial-Temporal Graph Attention Network for Travel Time Estimation at Baidu Maps
Aug 13, 2020100 views
Jizhou Huang
The task of travel time estimation (TTE), which estimates the travel,time for a given route and departure time, plays an important role,in intelligent transportation systems such as navigation, route planning, and ride-hailing services. This task is challenging because,of many essential aspects, such as traffic prediction and contextual information. First, the accuracy of traffic prediction is strongly,correlated with the traffic speed of the road segments in a route. Existing work mainly adopts spatial-temporal graph neural networks,to improve the accuracy of traffic prediction, where spatial and,temporal information is used separately. However, one drawback is,that the spatial and temporal correlations are not fully exploited to,obtain better accuracy. Second, contextual information of a route,,i.e., the connections of adjacent road segments in the route, is an essential factor that impacts the driving speed. Previous work mainly,uses sequential encoding models to address this issue. However, it,is difficult to scale up sequential models to large-scale real-world,services. In this paper, we propose an end-to-end neural framework,named ConSTGAT, which integrates traffic prediction and contextual information to address these two problems. Specifically, we,first propose a spatial-temporal graph neural network that adopts a,novel graph attention mechanism, which is designed to fully exploit,the joint relations of spatial and temporal information. Then, in order to efficiently take advantage of the contextual information, we,design a computationally efficient model that applies convolutions,over local windows to capture a route’s contextual information and,further employs multi-task learning to improve the performance. In,this way, the travel time of each road segment can be computed in,parallel and in advance. Extensive experiments conducted on largescale real-world datasets demonstrate the superiority of ConSTGAT.,In addition, ConSTGAT has already been deployed in production,at Baidu Maps, and it successfully keeps serving tens of billions,of requests every day. This confirms that ConSTGAT is a practical,and robust solution for large-scale real-world TTE services.
SIGKDD_2020
Applied Research
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