Haitao Yuan
ABSTRACT
We studied the ETA problem on a road network. We proposed a comprehensive and novel neural network based approach that is able to fully exploit spatio-temporal features extracted from four significant aspects: heterogeneity, proximity, periodicity and dynamicity. We built a link-connection graph to capture each route’s static contexts (i.e., spatial proximity and temporal periodicity), and we collect historical traffic conditions as its dynamic contexts.
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- Haitao Yuan and et al.2021. An Effective Joint Prediction Model for Travel Demands and Traffic Flows. In ICDE.Google Scholar
- Haitao Yuan, Guoliang Li, Zhifeng Bao, and Ling Feng. 2020. Effective Travel Time Estimation: When Historical Trajectories over Road Networks Matter. In SIGMOD. 2135–2149.Google Scholar
- Hanyuan Zhang, Hao Wu, Weiwei Sun, and Baihua Zheng. 2018. DeepTravel: a Neural Network Based Travel Time Estimation Model with Auxiliary Supervision. In IJCAI, Jérôme Lang (Ed.). ijcai.org, 3655–3661.Google Scholar
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